Medicaid and Medicare clinical and economic research | 3M US
Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 3M Clinical and Economic Research Richard F. Averill, MS Richard L. Fuller, MS Ronald E. Mills, PhD March 2021 Table of Contents Executive Summary....................................................................................................................2 Introduction ................................................................................................................................ 3 Potentially Preventable Emergency Department Visits (PPVs) ................................................3 Risk Adjusting PPVs....................................................................................................................4 Potentially Preventable Return Emergency Department Visits (PPRED) Following Hospital Discharge .....................................................................................................................5 Overlap Between PPVs and PPREDs ..........................................................................................6 Determining PPV Relative Cost .................................................................................................6 National and Best Practice Norms .............................................................................................6 PPV Financial Impact..................................................................................................................7 Data ............................................................................................................................................. 8 PPV Results by Risk Categories .................................................................................................8 PPV Results by Geographic Region ........................................................................................ 11 PPV Frequency ........................................................................................................................ 14 Summary and Conclusions...................................................................................................... 14 References............................................................................................................................... 16 Appendix A: Bibliography of Publicly Available Articles and Reports on PPVs, CRGs, APR DRGs, EAPGs, PPREDs .......................................................................................... 17 Appendix B: Potentially Preventable Emergency Department Visits (PPVs) ........................ 31 Appendix C: Description of CRG Logic .................................................................................. 40 Appendix D: PPV %(A-E)/E and $(A-E) by CBSA..................................................................... 42 Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 1 Executive Summary A well-functioning delivery system within a managed care plan or geographic region should be able to minimize the need for emergency department visits. In this 3M Clinical and Economic Research report, the Potentially Preventable Emergency Department Visits (PPVs) methodology was used to identify emergency department visits that may be potentially preventable. If there are an excess number of PPVs compared to a national norm within a managed care plan or geographic region, it is likely the excess PPVs represent emergency department visits that could be avoided if the delivery system functioned effectively. The study used a random data sample of five percent of Medicare fee-for-service (FFS) beneficiaries contained in the Medicare Standard Analytic Files for calendar year 2017 and 2018. The data from 2017 was used to determine the burden of chronic disease for each beneficiary and to risk adjust PPV rates in the 2018 data. After excluding FFS beneficiaries who were not enrolled in part A and B for the full two-year period, 1,388,114 beneficiaries remained in the analysis database. These beneficiaries experienced 583,708 emergency department visits, of which 320,720 were a PPV (54.9 percent of the emergency department visits). Extrapolated to the entire Medicare population, the 320,720 PPVs represent $2.0 billion in annual FFS Medicare expenditures. Based on a risk-adjusted national norm, the analysis found considerable PPV performance variation across census regions, states and Core Based Statistical Areas (CBSAs) from the Office of Management and Budget. Across states, PPV performance compared to the risk-adjusted national norm varied from 35.12 percent below expected for North Dakota to 70.87 percent above expected for the District of Columbia. A best practice PPV norm was determined using 40 percent of the CBSAs with the best PPV performance that had at least 1,500 beneficiaries in the analysis data. To achieve PPV best practice performance nationally, overall PPV performance would need to improve by 14.35 percent, which would result in an annual reduction in Medicare expenditures of $256.4 million (12.8 percent of the $2.0 billion in PPV expenditures). PPV performance can be an effective measure of delivery system performance within a managed care plan or geographic region. The extent of PPV performance variation indicates that there are PPV performance improvement opportunities in many geographic areas. The $256.4 million annual Medicare expenditure reduction gained through PPV best practice provides an achievable PPV quality improvement target. Based on a risk-adjusted national norm, the analysis found considerable PPV performance variation across census regions, states and Core Based Statistical Areas (CBSAs) from the Office of Management and Budget. Across states, PPV performance compared to the risk-adjusted national norm varied from 35.12 percent below expected for North Dakota to 70.87 percent above expected for the District of Columbia. Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 2 Introduction In 2017, 18.5 percent of adults 65 years or older had at least one emergency departments (ED) visit with 33 percent of these patients having two or more ED visits.1 Non-emergency conditions make up a significant proportion of ED visits. Many non-emergency visits were by individuals who either lack access to primary care altogether or whose primary care providers provide inadequate access to care, especially after hours or on weekends. Emergency departments have increasingly served as primary care providers of last resort, and non-emergent care provided in emergency departments has come to be seen as an indicator of the inadequacy of primary care services in the U.S. ED overcrowding by those with minor medical conditions such as sore throats and earaches may also hinder an ED's ability to provide quality care. Many EDs are overcrowded and struggle to handle an increase in patient visits leading to delays in the treatment of serious conditions, long waiting times and ambulance diversions.2 The majority of patients seen in the ED do not require any significant diagnostic evaluation or procedures and are discharged to home.3 Much of this inappropriate ED utilization could be eliminated if the primary care system functioned as it should. ED visits originate from a home setting or nursing home/rehabilitation setting. In particular, nursing home residents, who are treated in the ED for conditions such as urinary tract infections, could be more appropriately treated in the nursing home. Other conditions prompting ED visits for nursing home residents, such as those related to falls or pneumonia, could be avoided by preventing the adverse health event itself. Decreasing potentially preventable visits to EDs can reduce health care costs, lessen complications resulting from medical treatment for nursing home residents, and improve quality of care. The objective of this report is to determine for Medicare beneficiaries the extent of geographic variation in the rate of ED visits that are potentially preventable and to quantify the financial impact of excess potentially preventable ED visits. Potentially Preventable Emergency Department Visits (PPVs) A well-functioning delivery system within a managed care plan or geographic region should be able to minimize the need for ED visits. Potentially Preventable ED Visits. (PPVs) are ED visits that can often be avoided. The occurrence of an excess number of PPVs is indicative of an ineffective delivery system. Of course, not every PPV can be prevented. But if there are an excess number of PPVs compared to national benchmarks within a managed care plan or geographic region, it is likely that the excess PPVs represent ED visits that could be avoided if the delivery system functioned more effectively. There are five broad categories of PPVs: · ED visits for chronic disease management that could potentially have been managed in the outpatient setting (e.g., asthma) · ED visits for minor acute conditions that could potentially have been managed in the outpatient setting (e.g., constipation) · ED visits for signs and symptoms that do not require urgent care (e.g., lumbago) · ED visits for minor trauma (contusions) · ED visits that could potentially have been avoided for residents of a residential care facility such as a skilled nursing facility (e.g., trauma due to a fall) The most prevalent PPVs are for minor trauma and pain. These hospital emergency department visits may result from lack of access to adequate primary care or inadequate coordination of ambulatory care services. PPVs also include chronic conditions (e.g., hypertension) for which Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 3 adequate monitoring and follow-up, such as proper medication management, could have prevented the ED visit. As such, the occurrence of high rates of PPVs within a managed care plan or geographic region may represent a failure of the ambulatory care delivery system. A comprehensive evaluation of potentially preventable ED visits can provide a more complete assessment of the continuity of care and functioning of the health care delivery system within a managed care plan or geographic region. Appendix A contains PPV research articles and studies using PPVs, and Appendix B contains a more detailed description of the PPV methodology. 3M Clinical Risk Groups (CRGs) are used to risk adjust PPV rates. CRGs are a categorical clinical model that uses historical claims data to assign beneficiaries to a single mutually exclusive category that defines a beneficiary's chronic disease burden. Risk Adjusting PPVs To compare PPV rates across geographic regions and managed care plans, the PPV rates must be risk adjusted. 3M Clinical Risk Groups (CRGs) are used to risk adjust PPV rates. CRGs are a categorical clinical model that uses historical claims data to assign beneficiaries to a single mutually exclusive category that defines a beneficiary's chronic disease burden.4 The CRGs (Version 2.1) are composed of 332 base CRGs that describe the beneficiary's most significant chronic conditions and explicit severity levels that distinguish differences in disease burden due to severity of illness resulting in 1,414 individual CRGs. The individual CRGs are aggregated into nine health statuses ranging from catastrophic to healthy. Status 1 Healthy Status 2 History of Acute Disease e.g., Chest Pain Status 3 Single Minor Chronic Disease e.g., Migraine Status 4 Minor Chronic Disease in Multiple Organ Systems e.g., Migraine and BPH Status 5 Single Dominant or Moderate Chronic Disease e.g., CHF Status 6 - Dominant or Moderate Chronic Disease in Multiple Organ Systems, e.g., CHF, COPD Status 7 - Dominant Chronic Disease in Three or More Organ Systems, e.g., CHF, COPD, DM Status 8 - Malignancy, Under Active Treatment, e.g., Lung Cancer Status 9 - Catastrophic Conditions, e.g., Major Organ Transplant Based on the severity levels of the chronic conditions that comprise each status, beneficiaries in the nine statuses are assigned a severity level between one and six resulting in 53 aggregated CRG risk categories. Six of the aggregated CRGs in statuses 1 and 2 relate to pregnancy and delivery. Because this report analyzed Medicare data, the pregnancy and delivery CRGs were very low volume and were excluded from the analysis, resulting in the 47 CRG risk categories that were utilized to risk adjust the PPVs. The CRGs are a transparent system with a definition manual available for inspection. Appendix A contains CRG research articles and studies using CRGs, and Appendix C contains a more detailed description of the CRG methodology. Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 4 Potentially Preventable Return Emergency Department Visits (PPRED) Following Hospital Discharge PPVs represent an evaluation of ED usage within a population and reflect the impact of adequate access to ambulatory care and/or the adequate coordination of ambulatory care services. Return ED visits following a hospital discharge primarily reflect the performance of hospitals and have a direct impact on PPV performance. While, in general, managed care plans primarily focus on and are measured on population management performance, they are highly dependent on hospital performance to achieve better overall population PPV performance. Because of this interdependence, managed care plans will often provide incentive plans to hospitals to improve ED usage performance. Managed care plans must understand and quantify the impact of hospital performance on population performance to develop an effective incentive plan for hospitals. The interrelationship between PPVs and return ED visits following a hospital discharge was examined as follows. Potentially Preventable Return ED Visits (PPRED) Potentially Preventable Return ED Visits (PPREDs) are return ED visits within 30 days following a prior hospitalization. PPREDs may result from deficiencies in the process of care (e.g., ED visit for a surgical wound infection) or inadequate post-discharge follow-up (e.g., prescription not filled) rather than unrelated events that occur post discharge (e.g., broken arm due to trauma). PPREDs may result from actions taken or omitted during the initial hospital stay, such as incomplete treatment or poor care of the underlying problem, or from poor coordination of services at the time of discharge and afterwards, such as incomplete discharge planning or inadequate access to care. The admissions considered at risk for a PPRED and the clinical circumstances under which a subsequent ED visit is considered potentially preventable are specified in the PPRED methodology logic. The PPRED PPVs represent an evaluation of ED usage within a population and reflect designation is assigned to any admission that was followed by one or more potentially preventable ED visits during the the impact of adequate access to ambulatory care and/or the adequate coordination of ambulatory care services. Return ED visits 30 days following a hospital discharge. Appendix A contains PPRED research articles and studies using PPREDs. Risk Adjusting PPREDs following a hospital discharge primarily reflect the performance of hospitals and have a direct impact on PPV performance. 3M All Patient Refined Diagnosis Related Groups (APR DRGs) are a categorical clinical model composed of base categories (base APR DRGs) that are subdivided into four severity of illness subclasses.5 These subclasses are unique to each base APR DRG and are based on the extent of physiologic decompensation or organ system loss of function. The four severity of illness subclasses are numbered sequentially from 1 to 4 indicating respectively, minor, moderate, major, and extreme severity of illness. The combination of the base APR DRGs and the four severity of illness subclasses constitute a system of patient risk classes. The APR DRG based risk classes are exhaustive and mutually exclusive resulting in a patient being assigned to one and only one risk class. The APR DRGs and severity of illness subclasses are used for performance Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 5 reporting in five U.S. states and as the basis of payment adjustments in 30 states. The APR DRG methodology is a transparent system with a full definition manual. Overlap Between PPVs and PPREDs An ED visit can simultaneously be a PPV and PPRED. If an ED visit is both a PPV and a PPRED (i.e., a potentially preventable return ED visit within 30 days of a hospital discharge), the subsequent PPRED visits following an admission are not eligible to be a PPV because those ED visits are more likely to be associated with the care and follow-up provided by the hospital and therefore reflect a hospital performance issue as opposed to a delivery system performance issue, which is more likely associated with a lack of adequate access to ambulatory care and/or the adequate coordination of ambulatory care in the community. Determining PPV Relative Cost Enhanced Ambulatory Patient Groups (EAPGs) are a categorical clinical model that categorizes patients according to the amount and type of resources used in an ambulatory visit such as an ED visit.6 These resources include significant procedures, physical therapy, rehabilitation, dental procedures, medical visits, counseling, radiology, laboratory, drugs and biologicals, devices, supplies, ancillary tests, equipment, type of room, and treatment time. Patients in each EAPG have similar clinical characteristics and resource use. EAPGs were developed to encompass the full range of ambulatory settings including same day surgery units, hospital emergency rooms and outpatient clinics. EAPG relative resource weights are available that measure the relative costliness of each EAPG. The relative costliness of the mix of PPVs within a CRG risk class is determined by assigning each PPV to an EAPG and using the standard EAPG relative resource weights as a measure of the case mix of the PPVs in each CRG risk class. National and Best Practice Norms Each Medicare beneficiary is assigned to a CRG risk class based on their disease burden, which is determined from claims history data for the year preceding the year in which PPVs are assigned, as illustrated in Figure 1. Figure 1: CRG and PPV assignment periods Assign CRGs Base Year Assign PPVs Evaluation Year Within each CRG risk class a PPV relative weight is computed that reflects the PPV rate (frequency) and the case mix (relative costliness) of the PPVs. Thus, a higher weight for a CRG risk class can be the result of a high rate of PPV occurrence or that the mix of PPVs is more costly. National Norm A PPV national norm is calculated by summing the EAPG relative resource weights for all PPVs identified in the evaluation year within a CRG risk category--and across all beneficiaries assigned to the CRG risk category for the base year--and computing the mean value per beneficiary (referred to as the PPV national norm value). The end result is that each CRG risk class is assigned Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 6 a PPV relative weight that can be used to compute expected PPV performance. The expected PPV value (E) for any subset of beneficiaries is the number of beneficiaries in each CRG risk category times the PPV norm value for the CRG risk category and summed overall CRG risk categories (indirect rate standardization). For any subset of beneficiaries, such as beneficiaries in a specific geographic region, the PPV actual value in a CRG risk category is computed by summing all the EAPG relative resource weights of the PPVs for beneficiaries assigned to the CRG risk category. By summing all the PPV relative resource weights across all beneficiaries across all CRG risk categories, the actual value (A) is determined. The actual value (A) represents good performance if (A-E) is negative (A<E) and poor performance if (A-E) is positive (A>E). The %(A-E)/E is the percent that actual performance is below expected (%(A-E)/E is negative) or above expected (%(A-E)/E is positive). Best Practice Norm In addition to the national PPV norm, this report also determined a "best practice" norm. Using the metropolitan areas identified in the Core Based Statistical Areas (CBSAs) from the Office of Management and Budget, PPV performance across metropolitan areas was examined. Using the national norm, the (A/E) for each CBSA with at least 1,500 beneficiaries is used to determine the subset of CBSAs with the best PPV performance and that constitutes 40 percent of the beneficiaries in the Medicare population sample included in the analysis. This subset of CBSAs is referred to as the PPV best practice CBSAs. For the PPV best practice CBSAs, the overall A/E is computed. The A/E ratio for the PPV best practice CBSAs is less than one and is a measure of the level of relative performance achieved by PPV best practice CBSAs. For example, an A/E ratio of 0.8 for the PPV best practice CBSAs means that in these CBSAs, the PPV performance is 20 percent (1 - 0.8) lower than would be expected compared to all CBSAs. The value of the PPV relative weight in each CRG risk category in the PPV national norm is multiplied by the A/E ratio for the PPV best practice CBSAs to create a PPV best practice norm. Rather than selecting an arbitrary performance percentile as a best practice norm, using a PPV best practice norm created in this way is a performance level that is actually being achieved in a substantial number of geographic areas and represents an achievable performance improvement level. PPV Financial Impact A PPV financial conversion factor is computed based on allowed Medicare payments (the amount actually paid by Medicare). The financial conversion factor is used to express PPV actual performance (A) and PPV expected performance (E) in financial terms so that the financial impact of a PPV performance difference (A-E) can be determined. By comparing the financial impact of PPVs at the level of each clinically meaningful CRG risk category, the clinical and financial aspects of care are linked, which can facilitate behavior change and performance improvement initiatives. The Medicare savings estimated in this report is conservative because it is based solely on the (AE) difference. Thus, the underlying rate of PPVs as measured by E is accepted as a baseline level of underlying performance and only the PPV (A-E) difference is viewed as the basis for potential savings. The magnitude of the PPV (A-E) differences is directly related to the level of variation in PPVs across geographic regions. The greater the variation in PPVs across geographic regions, the greater the opportunity for savings. If there is little variation in PPVs across geographic regions, this analysis will conclude there is little opportunity for improvement and savings, essentially accepting the status quo as an acceptable level of performance. Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 7 Data The study used data in the Medicare Standard Analytic Files (Limited Data Set or LDS) for calendar year 2017 and 2018. The LDS files contain 100 percent of Medicare fee-for-service (FFS) claims data for inpatient, outpatient, skilled nursing facilities and home health agencies. The LDS carrier file contains Medicare FFS claims data for professional providers, including physicians, physician assistants, clinical social workers, and nurse practitioners for a random sample of five percent of Medicare beneficiaries. The LDS Master Beneficiary Summary File (MBSF) contains enrollment data on all Medicare beneficiaries enrolled in or entitled to Medicare within a given calendar year. To identify the burden of chronic disease and to assign CRGs, it was necessary to build a complete longitudinal record of all FFS claims for each Medicare beneficiary. Because the LDS carrier file was limited to a five percent sample of Medicare beneficiaries, the data in this study was limited to the beneficiaries in the LDS carrier file. The carrier file is a sample across all types of beneficiaries including beneficiaries in Medicare Advantage plans. To create a sample of FFS beneficiaries, the following edits were applied: · Exclude beneficiaries who were not enrolled in both Part A and B for the full year (i.e., newly enrolled, disenrolled or reported died) · Exclude beneficiaries who were enrolled in a managed care plan for one or more months · Exclude beneficiaries who were enrolled in hospice Calendar year 2017 was used to assign the CRG to each beneficiary and calendar year 2018 was used to assign the PPVs to each beneficiary. After these exclusions were applied, there were 1,388,114 beneficiaries in the analysis data. Of the 1,388,114 beneficiaries, 329,957 beneficiaries had one or more ED visits (23.8 percent), resulting in a total of 583,708 ED visits. 29,494 of those ED visits were a PPRED that followed a hospital discharge and were not eligible to be a PPV. 18,008 of the PPREDs would have been a PPV resulting in 320,720 PPVs (54.9 percent of the ED visits). By comparing the financial impact of PPVs at the level of each clinically meaningful CRG risk category, the clinical and financial aspects of care are linked, which can facilitate behavior change and performance improvement initiatives. PPV Results by Risk Categories Based on each beneficiary's claim history from 2017, beneficiaries were assigned to one of 47 CRG risk categories. Beneficiaries in each CRG risk category who had a PPV were identified using the 2018 data. Beneficiaries assigned to CRG status 3-9 all had at least one chronic disease. Table 1 contains summary data by CRG risk category for the beneficiaries with at least one chronic disease. Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 8 Table 1: PPV data by CRG risk category for beneficiaries with at least one chronic disease CRG Status 3 Single Minor Chronic Disease 4 Minor Chronic Disease in Multiple Organ Systems 5 Single Dominant or Moderate Chronic Disease 6 Dominant or Moderate Chronic Disease in Multiple Organ Systems 7 Dominant Chronic Disease in Three or More Organ Systems 8 Malignancy under Active Treatment Beneficiaries ED Visits PPVs PPVs/1,000 PPV Weight PPV $ Weight Beneficiaries ED Visits PPVs PPVs/1,000 PPV Weight PPV $ Weight Beneficiaries ED Visits PPVs PPVs/1,000 PPV Weight PPV $ Weight Beneficiaries ED Visits PPVs PPVs/1,000 PPV Weight PPV $ Weight Beneficiaries ED Visits PPVs PPVs/1,000 PPV Weight PPV $ Weight Beneficiaries ED Visits PPVs 9 Catastrophic Conditions PPVs/1,000 PPV Weight PPV $ Weight Beneficiaries ED Visits PPVs PPVs/1,000 PPV Weight PPV $ Weight 1 65,271 10,797 5,757 88.2 0.0307 21.27 29,906 5,274 2,848 95.2 0.0317 21.97 188,238 43,864 24,052 127.8 0.0442 30.63 131,904 37,433 20,929 158.7 0.0552 38.25 27,445 13,412 7,524 274.1 0.0982 68.05 3,205 1,026 538 167.9 0.0594 41.16 977 428 239 244.6 0.0935 64.79 2 15,539 4,302 2,425 156.1 0.0545 37.77 15,467 2,862 1,498 96.9 0.0342 23.70 92,835 31,571 17,547 189.0 0.0650 45.04 116,473 46,432 26,217 225.1 0.0801 55.51 31,652 23,311 12,903 407.7 0.1508 104.50 3,975 1,908 1,081 271.9 0.0941 65.21 2,379 1,245 723 303.9 0.1047 72.56 Severity Level 3 4 5 21,184 5,606 3,139 148.2 0.0511 35.41 51,829 20,132 11,466 221.2 0.0760 52.67 98,172 48,665 27,358 278.7 0.0998 69.16 17,434 16,696 9,156 525.2 0.2001 138.67 4,156 2,291 1,301 313.0 0.1195 82.81 2,664 2,206 1,269 476.4 0.1637 113.44 6,120 2,166 1,257 205.4 0.0740 51.28 19,500 9,479 5,505 282.3 0.1002 69.44 78,452 50,776 28,624 364.9 0.1331 92.24 14,431 16,657 9,026 625.5 0.2439 169.02 2,170 1,570 872 401.8 0.1466 101.59 3,702 3,942 2,102 567.8 0.1753 121.48 5,672 2,835 1,546 272.6 0.1032 71.52 54,610 42,252 24,028 440.0 0.1624 112.54 13,916 18,032 9,671 695.0 0.2787 193.14 542 460 260 479.7 0.1610 111.57 7,341 7,817 3,619 493.0 0.1753 121.48 6 304 106 60 197.4 0.1032 71.52 36,839 35,658 19,839 538.5 0.2018 139.85 17,017 26,744 13,280 780.4 0.3318 229.93 7,037 13,459 5,510 783.0 0.2924 202.63 There is nearly a nine-fold difference in the number of PPVs per 1000 beneficiaries across CRG risk category ranging from 88.2 to 783.0. The PPV relative weight for each CRG risk category reflects the combined impact of the PPV frequency and the relative costliness of the PPVs. The relative expected costliness of PPVs in each CRG risk category is determined by multiplying the PPV relative weight by the financial conversion factor of $692.99. The product of the number of PPVs in each CRG risk category and the PPV relative expected costliness for the CRG risk category summed over all CRG risk categories determines the expected PPV cost for any subset of beneficiaries. Table 2 contains summary data by CRG risk category for beneficiaries who do not have a chronic disease. Status 1 is for beneficiaries who are healthy and have no significant acute diseases in their history. Healthy nonusers with no significant contact with the health care system and healthy beneficiaries who had a mention of a chronic disease in their history but no subsequent treatment Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 9 (potentially a rule out diagnosis) are assigned to separate CRGs. Across these three healthy Status 1 CRG categories, the PPVs per 1,000 varied from 64.8 to 103.5. There are four CRG risk categories in Status 2 for beneficiaries with a history of acute disease. The four significant acute CRG risk categories are for beneficiaries with significant acute disease, multiple or reoccurring significant disease, major trauma or major acute disease and significant acute disease with a mention of a chronic disease in their history but no subsequent treatment. Across these four significant acute Status 2 CRG categories, the PPVs per 1,000 varied from 129.2 to 195.9. While the variation in PPV/1,000 for status 1 and 2 was modest, status 1 and 2 had 199,756 of the beneficiaries (14.4 percent) and 18,207 of the PPVs (5.7 percent). Table 2: PPV data by CRG risk category for beneficiaries with no chronic diseases CRG Status 1 Healthy 1 Healthy Non User 1 Healthy with Unconfirmed Chronic Disease 2 Multiple or Reoccurring Significant Acute Disease 2 Significant Acute Disease 2 Major Trauma Or Major Acute Disease 2 Significant Acute Disease With Unconfirmed Chronic Disease Beneficiaries ED Visits PPVs PPVs/1,000 PPV Weight PPV $ Weight Beneficiaries ED Visits PPVs PPVs/1,000 PPV Weight PPV $ Weight Beneficiaries ED Visits PPVs PPVs/1,000 PPV Weight PPV $ Weight Beneficiaries ED Visits PPVs PPVs/1,000 PPV Weight PPV $ Weight Beneficiaries ED Visits PPVs PPVs/1,000 PPV Weight PPV $ Weight Beneficiaries ED Visits PPVs PPVs/1,000 PPV Weight PPV $ Weight Beneficiaries ED Visits PPVs PPVs/1,000 PPV Weight PPV $ Weight 67,482 10,319 5,678 84.14 0.0286 19.82 79,613 9,746 5,164 64.86 0.0216 14.97 20,921 4,056 2,165 103.48 0.0361 25.02 6,187 1,518 862 139.32 0.0461 31.95 13,124 3,032 1,695 129.15 0.0412 28.55 2,363 834 463 195.94 0.0659 45.67 10,066 2,789 1,524 151.40 0.0533 36.94 Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 10 PPV Results by Geographic Region PPV %(A-E)/E and $(A-E) by Census Region Table 3 contains the PPV %(A-E)/E and $(A-E) by census region for the national norm and best practice norm. Across census regions the PPVs/1,000 beneficiaries ranged from 197.2 for the west north central census region to 260.0 for the New England census region. The %(A-E)/E with the national norm ranged from 9.8 percent below expected for the west north central census region to 16.8 percent above expected for the New England census region. The %(A-E)/E with the best practice norm ranged from 3.1 percent above expected for the west north central census region to 33.6 percent above expected for the New England census region. To achieve best practice across all regions, overall PPV performance would need to improve by 14.35 percent, which would generate $9.2 million in reduced Medicare expenditures. The 1,388,114 beneficiaries in the analysis data represent 3.59 percent of the 38,665,082 Medicare FFS beneficiaries in 2018.7 Extrapolating the reduction in Medicare expenditures from these beneficiaries to the full Medicare FFS population results in an estimated annual reduction of Medicare expenditures of $256.4 million, assuming PPV performance is improved by the 14.35 percent needed to achieve best practice nationally. It is important to keep in mind the $256.4 million represents a reduction in expenditures from achieving best practice $(A-E). The 320,720 PPVs represent $73.3 million in Medicare expenditures ($A) which extrapolated to the full Medicare FFS population is $2.0 billion. While the $2.0 billion reflect Medicare expenditures associated with PPVs, only the $256.4 million reduction is viewed as achievable in the short term. Approximately one-third of Medicare beneficiaries are enrolled in a Medicare Advantage (MA) Plan. The PPV performance in MA plans may differ from Medicare FFS so MA plan beneficiaries are not included in the estimated PPV reduction in Medicare expenditures. Table 3: PPV %(A-E)/E and $(A-E) by Census Region Region New England ME, VT, NH, CT, MA, RI Middle Atlantic South Atlantic NY, NJ, PA FL, GA, SC, NC, VA, WV, DC, MD, DE E North Central IL, WI. MI, IN. OH E South Central KY, TN, AL, MS W South Central W North Central Mountain TX, OK, AR, LA MN, IA, MO, KS, NE, SD, ND AZ, NM, UT, CO, NV, WY, ID, MT Pacific CA, OR, WA, HI, AK Total Count Benef Count PPVs PPVs per 1000 Benef %(A-E)/E PPV Nat Norm %(A-E)/E PPV BP Norm PPV $(A-E) Nat Norm PPV $(A-E) BP Norm 78,205 20,336 174,276 37,413 305,134 74,873 212,275 51,026 97,793 24,456 148,401 35,488 100,994 19,915 96,064 19,193 174,972 38,020 1,388,114 320,720 260.03 214.68 245.38 240.38 250.08 239.14 197.19 199.79 217.29 231.05 16.80 -8.14 3.88 4.15 -0.13 -1.98 -9.81 -3.68 -2.17 0.00 33.56 5.05 18.78 19.10 14.21 12.09 3.13 10.15 11.87 14.35 692,572 1,210,004 -763,090 413,949 633,560 2,684,977 477,617 1,920,962 -6,767 661,580 -158,679 847,057 -513,758 143,593 -172,144 415,406 -189,311 906,418 0 9,203,945 Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 11 PPV %(A-E)/E and $(A-E) by State Table 4 contains the PPV %(A-E)/E and $(A-E) by state for the national norm and best practice norm. The PPVs/1,000 beneficiaries ranged from 130.18 for North Dakota to 406.46 for the District of Table 4: PPV %(A-E)/E and $(A-E) by State State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware DC Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming Count Benef 23,675 3,451 28,123 18,214 117,877 19,223 14,634 6,878 2,475 92,161 38,527 4,573 8,253 59,705 33,376 19,369 16,784 24,386 20,150 8,616 32,329 36,477 45,595 14,387 18,922 30,072 6,784 11,224 11,432 9,480 44,306 9,431 73,425 48,553 4,079 48,376 21,991 16,311 56,545 3,902 29,366 5,079 30,810 88,046 8,651 5,096 43,229 32,760 11,616 25,223 4,167 Count PPVs 6,191 736 5,924 3,874 26,883 4,037 4,336 1,598 1,006 20,640 9,149 781 1,564 13,147 8,079 3,169 3,182 6,235 5,837 2,070 8,813 10,168 11,555 3,596 5,116 7,144 1,008 1,519 2,238 1,908 9,111 2,012 15,383 12,856 531 12,631 5,422 3,119 12,919 868 7,060 774 6,914 20,355 1,757 986 10,672 6,501 3,079 5,614 653 PPVs per 1000 Benef 261.50 213.27 210.65 212.69 228.06 210.01 296.30 232.33 406.46 223.96 237.47 170.79 189.51 220.20 242.06 163.61 189.59 255.68 289.68 240.25 272.60 278.75 253.43 249.95 270.37 237.56 148.58 135.33 195.77 201.27 205.64 213.34 209.51 264.78 130.18 261.10 246.56 191.22 228.47 222.45 240.41 152.39 224.41 231.19 203.10 193.49 246.87 198.44 265.07 222.57 156.71 %(A-E)/E PPV Nat Norm 0.98 2.82 2.29 -5.26 0.58 2.96 25.48 1.70 70.87 -7.03 -1.79 -17.05 -16.81 -1.33 5.26 -21.67 -14.02 2.86 10.35 12.53 19.82 23.89 1.32 11.15 7.61 1.59 -24.13 -28.54 -5.97 -2.88 -9.67 -0.33 -12.71 10.28 -35.12 13.64 -2.58 -9.43 -1.15 -1.27 6.35 -24.25 -8.16 -4.14 -5.54 -8.17 9.57 -7.52 3.72 2.10 -14.42 %(A-E)/E PPV BP Norm 15.47 17.58 16.98 8.34 15.01 17.74 43.48 16.29 95.39 6.31 12.31 -5.15 -4.88 12.84 20.36 -10.43 -1.68 17.62 26.18 28.68 37.01 41.67 15.86 27.10 23.05 16.17 -13.24 -18.29 7.53 11.06 3.29 13.98 -0.18 26.11 -25.81 29.95 11.40 3.57 13.04 12.90 21.62 -13.38 5.02 9.61 8.02 5.01 25.30 5.75 18.61 16.76 -2.14 $(A-E) PPV Nat Norm 12,699 4,398 31,788 -48,945 34,904 27,552 204,937 6,199 92,952 -361,790 -36,866 -37,366 -68,023 -42,040 95,346 -214,958 -120,218 38,813 117,921 54,645 334,999 468,945 33,679 83,539 77,095 26,078 -76,489 -158,614 -34,553 -13,427 -230,898 -1,486 -496,878 267,049 -71,094 363,411 -30,932 -73,655 -35,314 -2,669 94,167 -58,490 -135,373 -196,722 -23,243 -19,859 212,745 -117,593 24,104 27,221 -27,692 $(A-E) PPV BP Norm 175,122 23,938 205,632 67,812 789,407 144,285 305,890 52,090 109,413 284,076 222,077 -9,866 -17,254 356,020 322,972 -90,471 -12,595 209,343 260,956 109,382 547,138 715,272 354,648 177,569 204,259 231,869 -36,709 -88,871 38,121 45,074 68,730 55,511 -6,305 593,077 -45,687 697,735 119,377 24,397 351,524 23,730 280,174 -28,221 72,857 398,912 29,417 10,657 491,615 78,542 105,317 189,587 -3,598 Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 12 Columbia. The %(A-E)/E with the national norm ranged from 35.12 percent below expected for North Dakota to 70.87percent above expected for DC. The %(A-E)/E with the best practice norm ranged from 25.81 percent below expected for North Dakota to 95.39 percent above expected for D.C. Figure 1 is a U.S. map with the %(A-E)/E for the national norm by state color coded as follows: Green: Yellow: Orange: Red: %(A-E)/E >10% below expected 12 states %(A-E)/E 0 -10% below expected 16 states %(A-E)/E 0 -10% above expected 17 states %(A-E)/E >10% above expected 6 states Wide PPV performance variation is not only across states but also within states. The state of residency of the beneficiary was used to assign beneficiaries to a state in Table 4. Using the metropolitan areas identified in the Core Based Statistical Areas (CBSAs) from the Office of Management and Budget, Appendix D contains PPV %(A-E)/E and $(A-E) for the national norm and best practice for each CBSA with at least 1,000 beneficiaries in the analysis database. Some CBSAs encompass multiple states. For example, the Philadelphia metropolitan area encompasses parts of New Jersey, Delaware and Maryland. When a CBSA encompassed more than one state, the CBSA in Appendix D was assigned to the primary state associated with the CBSA (the Philadelphia metropolitan area was assigned to Pennsylvania). Figure 2: PPV %(A-E)/E performance by state Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 13 Table 5 contains the nine CBSAs in Pennsylvania with at least 1,000 beneficiaries in the analysis database. The PPV performance of the CBSAs in the southeast portion of the state is consistently below expected for the national norm. However, the performance in the rest of the state is consistently above expected for the national norm. PPREDs relate to return ED visits following a hospital discharge and were excluded as a PPV because the return ED visit is primarily a hospital performance issue as opposed to the performance of the delivery system within a geographic region. To examine the relationship between hospital and delivery system performance, a national PPRED norm was created and the PPRED %(A-E)/E was computed for each state. Across states the correlation between the PPRED %(A-E)/E and the PPV %(A-E)/E was 0.32. The modest positive correlation indicates that hospital performance in preventing potentially preventable return ED visit and delivery system performance in preventing potentially preventable ED visits tends to be similar within geographic regions, even though PPREDs were excluded from the PPVs. Table 5: PPV %(A-E)/E and $(A-E) for the seven largest CBSAs in Pennsylvania CBSA Pennsylvania South East Region Philadelphia-Camden-Wilmington Lancaster Harrisburg-Carlisle York-Hanover Reading Rest of State Pittsburgh Scranton--Wilkes-Barre--Hazleton Erie Chambersburg-Waynesboro Count Benef 56,545 Count PPVs 12,919 PPVs per 1000 Benef 228.47 %(A-E)/E PPV Nat Norm -1.15 PPV $(A-E) Nat Norm -35,314 28,551 2,469 2,300 2,092 1,971 2,213 149 171 132 108 77.49 60.54 74.35 62.97 55.03 -0.71 -22.48 -5.71 -19.32 -32.40 -10,949 -30,034 -7,172 -21,855 -36,025 6,393 3,479 1,120 1,033 580 90.75 315 90.51 97 86.18 97 94.18 17.71 13.03 12.79 17.53 60,487 25,150 7,585 10,056 %(A-E)/E PPV BP Norm 13.04 PPV $(A-E) BP Norm 351,524 13.54 -11.35 7.83 -7.74 -22.70 182,851 -13,265 8,601 -7,656 -22,070 34.60 29.25 28.98 34.40 103,352 49,380 15,027 17,254 PPV Frequency Table 6 contains the EAPG assigned to the 28 PPVs comprising at least one percent of the PPVs. The highest volume PPVs are for minor musculoskeletal and skin problems and nonspecific symptoms such as abdominal pain. Summary and Conclusions The 1,388,114 beneficiaries in the analysis database had 583,708 ED visits of which 320,720 were a PPV (54.9 percent of the ED visits). The 320,720 PPVs represent $2.0 billion in annual Medicare expenditures. If PPV best practice was achieved nationally, overall PPV performance would need to improve by 14.35 percent, which would result in an annual reduction in Medicare expenditures of $256.4 million (12.8 percent of the $2.0 billion in PPV expenditures). There was significant PPV performance variation across census regions, states and CBSAs. Across states, PPV performance based on a national norm varied from 35.12 percent below expected for North Dakota to 70.87 percent above expected for DC. PPV performance is an important measure of delivery system performance in a managed care plan or geographic region. The extent of PPV performance variation across states indicates there are PPV performance improvement opportunities in many geographic areas. Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 14 Table 6: EAPG of the 28 PPVs comprising at least one percent of the PPVs EAPG of PPV 661 LEVEL II MUSCULOSKELETAL & CONNECTIVE TISSUE DIAGNOSES 674 CONTUSION, OPEN WOUND, TRAUMA TO SKIN & SUBCUTANEOUS TISSUE 628 ABDOMINAL PAIN 871 SIGNS, SYMPTOMS & OTHER FACTORS INFLUENCING HEALTH STATUS 656 BACK & NECK DIAGNOSES EXCEPT LUMBAR DISC DIAGNOSES 727 ACUTE LOWER URINARY TRACT INFECTIONS 627 NON-BACTERIAL GASTROENTERITIS, NAUSEA & VOMITING 576 LEVEL I OTHER RESPIRATORY DIAGNOSES 675 OTHER SKIN, SUBCUTANEOUS TISSUE & BREAST DIAGNOSES 561 VERTIGINOUS DIAGNOSES EXCEPT FOR BENIGN VERTIGO 562 INFECTIONS OF UPPER RESPIRATORY TRACT & OTITIS MEDIA 271 PHYSICAL THERAPY 599 HYPERTENSION 270 OCCUPATIONAL THERAPY 530 HEADACHES OTHER THAN MIGRAINE 624 LEVEL I GASTROINTESTINAL DIAGNOSES 694 ELECTROLYTE DISORDERS 630 CONSTIPATION 601 LEVEL I CARDIAC ARRHYTHMIA & CONDUCTION DIAGNOSES 711 DIABETES WITH OTHER MANIFESTATIONS & COMPLICATIONS 602 ATRIAL FIBRILLATION 564 LEVEL I OTHER EAR, NOSE, MOUTH, THROAT & CRANIAL/FACIAL DIAGNOSES 826 ACUTE ANXIETY & DELIRIUM STATES 658 LUMBAR DISC DIAGNOSES WITH SCIATICA 663 PAIN 563 DENTAL & ORAL DIAGNOSES & INJURIES 573 COMMUNITY ACQUIRED PNUEMONIA 553 LEVEL I OTHER OPHTHALMIC DIAGNOSES Count Percent 26,178 8.2 23,864 7.4 18,442 5.8 16,840 5.3 14,937 4.7 13,355 4.2 13,325 4.2 12,480 3.9 10,219 3.2 9,914 3.1 9,457 2.9 8,450 2.6 7,994 2.5 7,541 2.4 7,324 2.3 6,028 1.9 6,004 1.9 5,389 1.7 5,306 1.7 4,639 1.4 3,742 1.2 3,641 1.1 3,556 1.1 3,550 1.1 3,513 1.1 3,474 1.1 3,454 1.1 3,242 1.0 Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 15 References 1. Centers for Disease Control. HUS Trend Tables 2018. https://www.cdc.gov/nchs/data/hus/2018/036.pdf 2. Machlin, SR, Medical Expenditure Panel Survey (MEPS), Statistical Brief 111: Expenses for a Hospital Emergency Room Visit, 2003, Adjusted to 2007 Data. Rockville, MD: Agency for Healthcare Research and Quality (AHRQ), 2006. Available at https://meps.ahrq.gov/data_files/publications/st111/stat111.pdf. Last accessed October 2016. Leah S. Honigman, MD, MPH; Jennifer L. Wiler, MD, MBA; Sean Rooks, BA; Adit A. Ginde, MD, MPH National Study of Non-urgent Emergency Department Visits and Associated Resource Utilization. The Western Journal of American Medicine. 2013; 14 (6): 609-616 3. National Hospital Ambulatory Medical Care Survey: 2011 Emergency Department Summary (U.S DHHS). 4. Hughes, Averill, Eisenhandler, Goldfield, Muldoon, Neff, Gay. (2003). Clinical Risk Groups (CRGs): A Classification System for Risk-Adjusted Capitation-Based Payment and Managed Care. Medical Care, 42(1). 5. Averill, Goldfield, Muldoon, Steinbeck, Grant. (2002). A Closer Look at All Patient Refined DRGs. Journal of the American Health Information Management Association, 73(1). 6. Goldfield N, Averill R, Eisenhandler J, Grant T. Ambulatory Patient Groups, Version 3.0--a classification system for payment of ambulatory visits. Journal of Ambulatory Care Management. 2008;31(1):2-16. doi:10.1097/01.JAC.0000304091.21087.08. 7. Centers for Medicare and Medicaid Services (CMS). Medicare Enrollment 2018. https://www.cms.gov/research-statistics-data-systems/cms-program-statistics/2018medicare-enrollment-section Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 16 Appendix A: Bibliography of Publicly Available Articles and Reports on PPVs, CRGs, APR DRGs, EAPGs, PPREDs All articles and reports are publicly available and are listed in chronological order. The opinions and conclusions in these articles and reports are solely those of the authors. Potentially Preventable Emergency Department Visits (PPVs) Articles, Reports, and Book Chapters Goldfield N, Kelly W, Patel K. Potentially Preventable Events: an actionable set of measures for linking quality improvement and cost savings. Qual Manage Health Care. 2012;21(4):213-219. Millwee B, Goldfield N, Averill R, Hughes J. Payment system reform: one state's journey. J Ambul Care Manage. 2013;36(3):199-208. Medicare Payment Advisory Commission. Feasibility of measuring population-based outcomes: potentially preventable admissions and emergency department visits. Online Appendix 3A in Report to the Congress: Medicare and the Health Care Delivery System. Washington, DC: MedPAC, June 2014. 3M Health Information Systems. The 3M Value Index Score: Measurement and Evidence. Murray, UT: 3M HIS, 2015. Bernstein AB. Potentially Preventable Events: Comparing Medicaid and Privately Insured Populations. Presentation to the Medicaid and CHIP Payment and Access Commission. Washington, DC: MACPAC, Dec. 15, 2015. Minnesota Department of Health. An Introductory Analysis of Potentially Preventable Health Care Events in Minnesota. St. Paul. MN: MNDOH, 2015. Minnesota Department of Health. An Introductory Analysis of Potentially Preventable Health Care Events in Minnesota: Supplemental Technical Information. St. Paul. MN: MNDOH, 2015. Burns & Associates. External Quality Review of Indiana's Hoosier Lose Year Healthwise Program and Healthy Indiana Plan for The Review Year Calendar Year 2014. Report to the Indiana Office of Medicaid Policy and Planning. Phoenix, AZ: Burns & Associates, 2016. Quinn K, Weimar D, Gray J, Davies B. Thinking about clinical outcomes in Medicaid. J Ambul Care Manage. 2016;39(2). Florida Agency for Health Care Administration. Analysis of Potentially Preventable Healthcare Events of Florida Medicaid Enrollees: July 2015 to June 2016. Tallahassee, FL: AHCA, Winter 2017. Florida Agency for Healthcare Administration. Analyzing Potentially Preventable Healthcare Events of Florida Medicaid Enrollees. Tallahassee, FL: AHCA, Spring 2017. University of Florida, Institute for Child Health Policy. Texas Medicaid Managed Care and CHIP Summary of Activities and Trends in Healthcare Quality. Tallahassee, FL: ICHP, 2017. Myers & Stauffer. Cost Effectiveness Study Report for Mississippi Coordinated Access Network (MississippiCAN). Report to the Mississippi Division of Medicaid. Windsor, CT: Myers & Stauffer, 2017. Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 17 Florida Agency for Health Care Administration. Analysis of Potentially Preventable Healthcare Events of Florida Medicaid Enrollees 2015-2016 and 2016-2017. Tallahassee, FL: AHCA, Winter 2018. Fuller RL, Goldfield NI, Hughes JS, McCullough EC. Nursing home compare star rankings and the variation in potentially preventable emergency department visits and hospital admissions. Popul Health Manage. Epub ahead of print. July 30, 2018. Millwee B, Quinn K, Goldfield N. Moving toward paying for outcomes in Medicaid. J Ambul Care Manage. 2018;41(2):88-94. Millwee B, Goldfield N, Turnipseed J. Achieving improved outcomes through value-based purchasing in one state. Am J Med Qual. 2018;33(2):162-171. North Carolina Department of Health and Human Services. Plan for Implementation of Hospital Quality Outcomes Program and PHP Quality Outcomes Program. Report to the Legislature. Raleigh, ND: NCDHHS, Sept. 28, 2018. Burns & Associates. External Quality Review of Indiana's Care Programs: Hoosier Healthwise, Hoosier Care Connect and HIP Review Year Calendar 2017. Report to the Indiana Office of Medicaid Policy and Planning. Phoenix, AZ: Burns & Associates, 2019. Florida Agency for Health Care Administration. Analysis of Potentially Preventable Healthcare Events of Florida Medicaid Enrollees 2015-2016 and 2016-2017. Tallahassee, FL: AHCA, Winter 2018. New York Department of Health. DSRIP PAOP Meeting June 24, 2019. Presentation, available at www.health.ny.gov/health_care/medicaid/redesign/dsrip/paop/meetings/2019/docs/201906-24_pm-ff.pdf. Texas External Quality Review Organization. Quality, Timeliness, and Access to Health Care for Texas Medicaid and CHIP Recipients: Summary of Activities Calendar Year 2017. Austin, TX: Texas EQRO, n.d. Websites 3M Health Information Systems. 3M Patient Classification Methodologies. Webpage: www.3m.com/his/methodologies. Accessed 2020. New York Department of Health--consumer information. https://health.data.ny.gov/. Accessed 2020 Superior Health Plan. www.superiorhealthplan.com/providers/resources/providerprograms/3m-his.html. Accessed 2020 Texas Health and Human Services Commission. www.thlcportal.com. Accessed 2020 Excellus BlueCross BlueShield. Potentially Preventable Emergency Room Visits in New York State. Available at www.excellusbcbs.com/wps/wcm/connect/341d4367-74bd-48ef-b980bffd2006ba44/ER+infographic-EX+FINAL+4-616.pdf?MOD=AJPERES&%20%20CACHEID=341d4367-74bd-48ef-b980-bffd2006ba44. Accessed June 30, 2019. New York Department of Health. DSRIP Stories of Meaningful Change in Patient Health. Albany, n.d. Available at: www.health.ny.gov/health_care/medicaid/redesign/dsrip/2019/docs/stories.pdf. Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 18 Superior Health Plan. 3M HIS Prospective Dashboard User Guide. Available at https://www.superiorhealthplan.com/content/dam/centene/Superior/Provider/PDFs/SHP_20 173928-3M-HIS-Dashboard-Training-P-05312018.pdf. Superior Health Plan. 3M Health Information Systems Guide: Understanding the Domains and Metrics. Available at https://www.superiorhealthplan.com/content/dam/centene/Superior/Provider/PDFs/SHP_20 195046-3M-HIS-Resource-Guide-P-508-03202019.pdf Clinical Risk Groups (CRGs) Articles, Reports, and Book Chapters Goldfield N, Averill R, Eisenhandler J, Hughes JS, Muldoon J, Steinbeck B, Bagadia F. The prospective risk adjustment system. J Ambul Care Manage. 1999;22(2):41-52. National Association of Children's Hospitals and Related Institutions. Summary Description of Clinical Risk Groups (CRGs). Washington, DC: NACHRI; 2000. Medicare Payment Advisory Commission. Report to the Congress: Improving Risk Adjustment in Medicare. Washington, DC: MedPAC, November 2000. Goldfield N, Averill R, Eisenhandler J. Payment and provider profiling of episodes of illness of clinical illnesses involving rehabilitation. J Outcome Meas. 2000;4(3):706-720. Majeed A, Bindman AB, Weiner JP. Use of risk adjustment in setting budgets and measuring performance in primary care I--how it works. BMJ 2001;323:604607. Bethell C, Read D. Approaches to Identifying Children and Adults with Special Health Care Needs: A Resource Manual for State Medicaid Agencies and Managed Care Organizations. Report to CMS. Available at www.childhealthdata.org. 2002. Neff JM, Sharp VL, Muldoon J, Graham J, Popalisky J, Gay JC. Identifying and classifying children with chronic conditions using administrative data with the Clinical Risk Group classification system. Ambul Pediatr. 2002;2(1):71-79. Averill RF, Goldfield NI, Eisenhandler J, Muldoon JH, Hughes JS, Neff JM, Gay JG, Gregg LW, Gannon DE, Shafir BV, Bagadia FA, Steinbeck BA. Development and evaluation of Clinical Risk Groups. In: Goldfield N, Delivering High-Quality Cost-Effective Health Care to All: The Scientific and Political Ingredients for Success. Northampton, MA: Artichoke Publications, 2004. Goldfield N, Eisenhandler J, Gay G, McCullough E, Bao M, Neff J, Muldoon J, Hughes J, Mills R. Development of an episode of illness classification for population management using pharmacy data. Dis Manag. 2004;5(3). Hughes JS, Averill RF, Eisenhandler J, Goldfield NI, Muldoon J, Neff JM, Gay JC. Clinical Risk Groups (CRGs): a classification system for risk-adjusted capitation-based payment and health care management. Med Care. 2004;42(1):81-90. Neff JM, Sharp VL, Muldoon J, Graham J, Myers K. Profile of medical charges for children by health status group and severity level in a Washington State health plan. HSR. 2004;39(1):73-90. Berlinguet M, Preyra C, Dean S. Comparing the Value of Three Main Diagnostic Based Risk Adjustment Systems. Ottawa: ON: Canadian Health Services Research Foundation, 2005. Neff JM, Sharp VL, Popalisky J, Fitzgibbon T. Using medical billing data to evaluate chronically ill children over time. J Am Care Manage. 2006; 29(4):283-290. Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 19 Maine Health Information Center. Children in Out-of-Home Placement in New Hampshire Health Status, Utilization, Payments, and Preventive Visits, State Fiscal Year 2007. (Concord, NH: DHHS, 2009) Bernstein RH. New arrows in the quiver for targeting case management: high-risk versus highopportunity case identification. J Ambul Care Manage. 2007;30(1):39-51. Alberta Health Quality Council. 2009 Measuring and Monitoring for Success. Calgary, AB: AHQC, 2009. Kelly WP, Wendt SW, Vogel BB. Guiding principles for payment system reform. J Ambul Care Manage. 2010;33(1):29-34. Neff JM, Clifton H, Park KJ, Goldenberg C, Popalisky J, Stout JW, Danielson BS. Identifying children with lifelong chronic conditions by using hospital discharge data. Acad Pediatr. 2010;10(6):417-423. Eisenhandler J, Averill R, Vertrees J, Quain A, Switalski J. A Comparison of the Explanatory Power of Two Approaches to the Prediction of Post Acute Care Resources Use. Report to CMS. Wallingford, CT: 3M Health Information Systems, 2011. New Hampshire Department of Health and Human Services. New Hampshire Medicaid Annual Report State Fiscal Year 2010. Concord, NH: DHHS, 2011. Children's Hospital Association. Defining Children with Medical Complexities. Alexandra, VA: CHA, 2013. 3M Health Information Systems. The Impact of Disability Measures on Expected Medicare Payments and Expected Provider Charges for Event-Based Episodes that Include Post-Acute Care. Salt Lake City, UT: 3M HIS, 2013. Medicare Payment Advisory Commission. Approaches to bundling payment for post-acute care. Chapter 3 in Report to the Congress: Medicare and the Health Care System. Washington, DC: MedPAC, June 2013. Onpoint Health Data. Children's Health Insurance Programs in New Hampshire: Access, Prevention, Care Management, Utilization, & Payments (State Fiscal Year 2011). Report to DHHS. Concord, NH: DHHS, 2013 Schone E, Brown RS. Risk Adjustment: What Is the Current State of the Art, and How Can It Be Improved? Princeton, NJ: Robert Wood Johnson Foundation, 2013 Vertrees J, Averill R, Eisenhandler J, Quain A, Switalski J, Gannon D. The Ability of Event-Based Episodes to Explain Variation in Charges and Medicare Payments for Various Post Acute Service Bundles. Report to MedPAC. Wallingford, CT: 3M Health Information Systems, 2013. Vigen G, Coughlin S, Duncan I. Measurement and Performance Healthcare Quality and Efficiency: Resources for Healthcare Professionals. Third update. Society of Actuaries, 2013. Berry J, Hall M, Hall DE, Kuo DZ, Cohen E, Agrawal R, Mandl KD, Clifton H, Neff J. Inpatient growth and resource use in 28 children's hospitals. JAMA Pediatrics. 2013;167(2):170-177. Fuller R, Goldfield N, Averill R, Eisenhandler J, Vertrees J. Adjusting Medicaid managed care payments for changes in health status. Med Care Res Rev. 2013;70(1):68-83. Lion KC, Rafton SA, Shafii J, Brownstein D, Michel E, Tolman M, Ebel BE. Association between language, serious adverse events, and length of stay among hospitalized children. Hosp Pediatr. 2013;3(3): 219-225. https://doi.org/10.1542/hpeds.2012-0091 Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 20 Vertrees J, Averill R, Eisenhandler, J, Quain, A, Switalski J. Bundling Post-Acute Care Services into MS-DRG Payments. Medicare Medicaid Res Rev. 2013;3(3):E1-E19 3M Health Information Systems. The 3M Value Index Score: Measurement and Evidence. Murray, UT: 3M HIS, 2015. Johnson TL, Brewer D, Estracio R, Vlasimsky T, Durfee MJ, Thompson KR, Everhart RM, Rinehart DJ, Batal H. Augmenting predictive modeling tools with clinical insights for care coordination. eGEMs (Generating Evidence & Methods to Improve Patient Outcomes). 2015;3(1). North Carolina Community Care Networks, Inc. Clinical Program Analysis. Report to the North Carolina Department of Health and Human Services. 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Factors Affecting the Development of Medicaid Hospital Payment Policies--Findings from Structured Interviews in Five States. Report to MACPAC. Lansing, MI: HMA, 2018. Medicaid and CHIP Payment and Access Commission. State Medicaid Payment Policies for Inpatient Hospital Services. Available at https://www.macpac.gov/publication/macpacinpatient-hospital-payment-landscapes/ Fuller RL, Hughes JS, Goldfield NI, Atkinson G. Are we confident of across-hospital mortality comparisons? Am J Med Qual. 2018;33(6):662-664. McCormick PJ, Lin HM, Deiner SG, Levin MA. Validation of the All Patient Refined Diagnosis Related Group (APR-DRG) risk of mortality and severity of illness modifiers as a measure of perioperative risk. J Med Syst. 2018;42(5):81. Deschepper M. Using standard available hospital-wide data in the interpretation and prediction of outcome indicators. Doctoral dissertation, Ghent University. Faculty of Medicine and Health Sciences; 2019. Averill RF, Fuller RL, Mills RE. 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Accessed 2020 Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 30 Appendix B: Potentially Preventable Emergency Department Visits (PPVs) This Appendix gives an overview of the Potentially Preventable Emergency Department Visits (PPVs), a methodology that can be used to determine the amount of variability in emergency department visits and to estimate the potential magnitude of avoidable emergency department visits. PPV Assignment Criteria Potentially unnecessary hospital emergency room visits are not unusual. In 2011, there were over 136 million visits to emergency departments (EDs) throughout the United States,1 many of which were for non-emergency conditions. Many of these non-emergency visits were by people who either lack access to primary care altogether or whose primary care providers provide inadequate access to care, especially after hours or on weekends. Emergency departments have increasingly served as primary care providers of last resort, and non-emergent care provided in emergency departments has come to be seen as an indicator of the inadequacy of primary care services in the U.S. Researchers have found that Emergency Department (ED) overcrowding by those with minor medical conditions such as sore throats and earaches may also hinder an ED's ability to provide quality care. Many EDs, after all, are already overcrowded and struggling to handle an increase in patient visits. These visits originate from a home setting or nursing home/rehabilitation hospital setting. Background on Emergency Department use and overuse Increasing use of the Emergency Department (ED) as a source of first-contact care for nonemergent conditions has contributed to overcrowding, which in turn causes a number of complications, as pointed out by the American College of Emergency Physicians, the Institute of Medicine, and the Government Accounting Office.2 These complications include: · Delays in the treatment of serious problems, including heart attacks · Increased waiting times for people with minor illnesses · Reduced promptness and quality of pain management · Hallway boarding of admitted patients · Ambulance diversions · Decreased physician productivity Evidence for ED utilization for non-emergent care comes from the National Hospital Ambulatory Medical Care Survey: 2011 Emergency Department Summary (U.S. Department of Health and Human Services)3 and includes the following: · 28 percent of ED patients had no diagnostic or screening services performed. · 49 percent of ED patients had no procedures performed (the most frequent procedure performed was infusion of intravenous fluids). · 43 percent of patients were designated as either semi-urgent (able to wait an hour to be seen) or non-urgent at the time of arrival by the triage nurse. · The great majority of patients (83 percent) were discharged to home: 11.9 percent were admitted to hospital, another 2.1 percent to an observation unit, and 2.1 percent were transferred to another hospital. Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 31 Analysts have pointed out that much of this inappropriate utilization could be eliminated if our primary care system functioned as it should. Many analysts have attempted to estimate the magnitude of this burden, with varying success. · Relevant information from the Emergency Department survey cited above includes: · About 3.6 percent of ED visits were made by patients who had been seen in the same ED within the last 72 hours. · About 2.1 percent of ED visits were made by patients who had been discharged from the hospital within the last 7 days. · Though overall ED visits increased, the number of visits considered emergent or urgent (15.9 million) did not change significantly from 2005, nor did the number of patients arriving by ambulance (18.4 million). With respect to nursing homes, older adults, particularly nursing home residents, comprise a large and growing percentage of those visiting the ED. Prior research has identified conditions that may lead to potentially preventable visits to an ED among nursing home residents. Researchers argue that some of these conditions, such as urinary tract infections, could be more appropriately treated in the nursing home. Other conditions prompting ED visits, such as those related to falls or pneumonia, may have been avoided by preventing the adverse health event itself. Decreasing potentially preventable visits to EDs may reduce health care costs, lessen trauma or complications resulting from medical treatment for nursing home residents, and improve quality of care. According to a recently published survey on ED visits and Medicaid, with respect to children, a handful of conditions account for more than half of all ED visits by both privately insured and Medicaid-covered children aged 0 to 12 years: acute respiratory and other common infections and injuries. Together, these conditions accounted for 53 percent of ED visits by children with Medicaid and almost 60 percent of all visits by privately insured children. Very few other condition groups account for a large enough share of visits that, if redirected to other care settings, could have a real impact on patient volume in emergency departments. This is strong evidence supporting the idea that settings other than emergency departments could manage a large share of visits by children, but these settings would require capacity to treat 1) urgent and common childhood infections; and 2) minor or uncomplicated injuries."4 Classification methodologies addressing preventable emergency visits There have been several methods developed to identify potentially preventable emergency visits with the goal of reducing their frequency. Of greatest relevance: · New York University Emergency Department Visit (NYU ED) severity algorithm · The Emergency Severity Index (ESI) · The 3M Potentially Preventable Emergency Department Visits (PPV) methodology, based on the 3MTM Enhanced Ambulatory Patient Grouping (EAPG) System The NYU ED classification description divides patients into four categories of need based on a three-step process: first on the severity of findings at the time of admission to the ED, then based on the types of services provided in the ED, and then finally the diagnosis assigned to the patient at the end of the visit. First a determination of "emergent" versus "non-emergent" need is made based on demographics, vital signs, primary symptoms and comorbid conditions. Then the emergent cases are separated into "emergent, primary care treatable" and "emergent, ED care needed" based on whether the patient received any services that would have only been available in an ED setting and unavailable in a primary care setting. A "preventability percentage" is assigned based on the initial research sample. Thus (and this is from their web site) "for abdominal pain, the algorithm assigns a specific percentage of the visit into the categories of `non-emergent,' Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 32 `emergent/primary care treatable,' and `emergent/ED care needed-not preventable/avoidable' based on what we observed in our sample for cases with an ultimate discharge diagnosis of abdominal pain." Finally, the group of "emergent, ED care needed" patients are further separated into groups considered to be "preventable/avoidable" with adequate primary care services, or "not preventable/avoidable." This last distinction is based on the whether an ambulatory care sensitive condition diagnosis code was assigned to the patient at the time of discharge from the ED, and the probability of that diagnosis being preventable or avoidable derived from previous analyses.5 A number of studies have evaluated the NYU ED classification system, some favorable and some not.6 A comprehensive study of the details of the system by the Washington State Hospital Association (WHSA) found several defects:7 · The model has not been updated since 2001, so that the additions and changes in diagnostic coding and clinical practice have not been incorporated. · The classification system includes the category of "unclassifiable" and in their study 42 percent of cases fell into this category. · The model does not evaluate each visit claim as necessary or unnecessary, appropriate or not appropriate. A recent article highlighted some of the many factors pertaining to avoidable ED visits, "Previous studies have found a lower rate of resource utilization for non-urgent patients; however, our analysis shows a high rate of interventions for even the lowest acuity visits. This suggests that health care services are needed even for the lowest acuity visit and calls into question the designation of a non-urgent ED visits as being unnecessary." Categorizing an ED visit as unnecessary depends not only on patient acuity but also the appropriateness of the site of service and availability of alternate sources of acute, unscheduled care. The ED may in fact be an appropriate site of service for a non-urgent presentation or complaint if there are no other available sites to provide timely care to the patient.8 This article highlights the need to look at avoidable ED visits as part of a coordinated care or integrated delivery system approach. That is, the challenge for the integrated delivery systems that are being implemented is to exactly address the challenge in the last sentence of this excerpt. A second methodology examining appropriateness and severity of ED visits, The Emergency Severity Index (ESI), provides an example of a purely clinically based approach to severity classification, and relies on signs of acuity such as hypotension, fever, tachycardia, and selected high-risk symptoms, and was designed to classify severity at the bedside for individual patients. It can be also used to stratify severity for performance evaluations for groups of ED patients, but requires either prospective data gathering or retrospective chart review. For research purposes, therefore, the ESI has much higher costs than a system based on routinely available computerized clinical data.9 Assign EAPG A patient's individual outpatient services are assigned to EAPGs. EAPGs are a comprehensive method of determining a patient's reason for an ambulatory visit and are used in the PPV logic to identify patients that had candidate PPV events. The standard EAPG logic partitions outpatient services into separate days and assigns the individual outpatient services to an EAPG. Each EAPG is assigned to one of five categories comprised of per-diem visits, significant procedure, ancillary service, incidental services and medical visit indicator. The medical visit EAPG is used to identify candidate potentially preventable Emergency Department (ED) visits. PPV evaluation for the majority of EAPGs is based on the medical reason for why the patient was seen in the ED, not the specific services performed during the encounter. For instance, if a patient is seen in the ED for a headache and a CT scan is performed, the PPV logic will evaluate if the visit for the headache may Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 33 have been prevented. Refer to the EAPG section of this manual for a detailed description of EAPG methodology. There is a small subset of significant procedure EAPGs that are potentially preventable. For example, bunion procedures, circumcisions, fitting of contact lenses, etc. Outpatient encounters for per-diem visits and significant procedures determine the categorization for the reason for the visit and are not assigned a medical EAPG. Only those outpatient encounters with a medical visit indicator that do not also have a per-diem or significant procedure performed are classified with a medical visit EAPG. However, there are a select set of ancillary procedures that dominate the cost of the visit and are categorized as significant procedure EAPGs. For example, performing an MRI for mild low back pain may not be useful to establish a diagnosis. Outpatient encounters that are found on the list of significant procedure ancillary EAPGs are reassigned to a medical visit EAPG based on the reason for the ambulatory visit. Determine if the outpatient visit occurred in a hospital emergency room Treatment for outpatient services can occur in many health care settings. PPVs are only assigned to visits that occurred in a hospital's emergency department. Outpatient visits with charges for the following revenue codes or Evaluation and Management HCPCS/CPT codes (CPT codes, descriptions and materials only © 2019 American Medical Association. All Rights Reserved): Revenue center codes 0450 Emergency department general 0451 EMTALA emergency medical screening 0452 ER beyond EMTALA screening 0456 Urgent Care 0459 Other emergency room 0981 Emergency room E&M HCPCS/CPT codes 99281 Emergency Department visit (straight forward decision making) 99282 Emergency Department visit (low complexity) 99283 Emergency Department visit (expanded problem focus exam/moderate complexity) 99284 Emergency Department visit (detail exam/mod complexity) 99285 Emergency Department visit (high complexity) are identified as ED visits for a patient G0380 Lev 1 hosp type B ED visit G0381 Lev 2 hosp type B ED visit G0382 Lev 3 hosp type B ED visit G0383 Lev 4 hosp type B ED visit G0384 Lev 5 hosp type B ED visit G0390 Trauma Respons w/hosp Criti Determine if reason for the visit is an ambulatory care sensitive condition PPVs are emergency room visits that may result from a lack of adequate access to care or ambulatory care coordination. Similar to PPAs, PPVs are ambulatory sensitive conditions (e.g., asthma) which adequate patient monitoring and follow-up (e.g., medication management) should be able to reduce or eliminate. PPVs are inefficient and expensive either because the care could have been provided in a less expensive setting that was not available, or because inadequate care of a chronic or sub-acute problem in the outpatient setting resulted in an acute deterioration, or a combination of both. In addition, when a PPV occurs shortly following a hospitalization, the PPV may be the result of actions taken or omitted during the hospital stay, such as incomplete treatment or poor care of the underlying problem and/or poor coordination with the primary care or specialist physicians. Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 34 The PPV methodology utilizes the 3MTM Enhanced Ambulatory Patient Grouping (EAPG) System as its foundation, in order to identify those emergency department services that are potentially preventable. The 3M EAPGs are a classification methodology that categorizes all ambulatory patient services, regardless of setting, in the same way that diagnosis related groups (DRGs) comprehensively categorize inpatient hospital services. EAPGs have the following characteristics that are necessary for any ambulatory patient classification system: · Comprehensiveness all ambulatory services are included · Administrative simplicity uses claims data, and chart review is not needed · Homogeneous resource use within each patient class · Clinical meaningfulness · Minimal Upcoding and Code Fragmentation minimal opportunities for providers to assign patients to higher paying classes through upcoding (e.g. codes for "simple" and "complex" procedures are placed in separate classes) · Flexibility - The patient classification methodology is flexible enough to accommodate a full range of options for incorporating ancillary services into the visit payment. The EAPG based potentially preventable ED visits/services classification methodology consists of Diagnostic and Procedural axes of classification. The first step in developing a patient classification methodology is to choose the initial classification variable. In DRGs, the principal diagnosis is used to classify patients into a set of mutually exclusive major diagnostic categories (MDCs). For EAPGs, the initial classification variables are procedures rather than diagnoses. The procedures that could be performed on an ambulatory basis were assigned to one of two classes: Significant Procedures. These are ordinarily scheduled in advance, constitute the reason for the visit, and dominate the time and resources expended during the visit. Significant procedures range in scope from debridement of nails and excision of a skin lesions to pacemaker replacements and stress tests. Significant procedures need to be scheduled and consume the vast majority of the resources for that visit (all the above examples fall into that category) and these are their defining characteristics. Ancillary Services. These include tests and procedures that can assist in diagnosis or treatment at the time of a medical encounter. Examples of ancillary procedures range from simple injections and immunizations to a cardiogram. ED patients who do not undergo a significant or ancillary procedure are assigned to a PPV diagnostic group based on the diagnostic code that is the reason for the visit. In addition to this Diagnostic and Procedural classification, all EAPGs are divided into those that are and are not potentially preventable when they occur in the ED. Finally, all PPVs are divided into the following categories: · Potential areas of overuse · Acute infections that could be treated in a primary care setting · Chronic illnesses related to malignancy · Mental health and substance abuse encounters · Other chronic illnesses except mental health, substance abuse and malignancy Understanding that the rate of preventable ED visits will never be zero, the PPV methodology examines all ED visits for opportunities for improvement. Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 35 Determine if patient was admitted from a residential nursing care facility Research suggests increased Emergency Department (ED) visits from nursing home residents could be prevented with better quality of care was taken at the nursing care facility. For example, if a patient had a UTI the facility should have been able to treat it, therefore the event would have been avoided. These visits start in the nursing home/rehabilitation hospital setting. Like PPA, PPV also uses Nursing Care Residential Sensitive Condition Criteria. In addition to the ambulatory sensitive conditions described above, additional diagnoses are considered PPVs specifically for patients admitted from a residential nursing care facility. Patients treated in the ED for acute major eye infections as well as patients treated for osteomyelitis, septic arthritis and other musculoskeletal infections are considered candidate PPVs. The full list of EAPGs that represents both the ambulatory sensitive conditions and the residential nursing care facility sensitive conditions are detailed in the PPV section of this manual. Thus, patients are identified as PPV candidates if they are treated in the emergency room directly coming from a residential nursing care facility and assigned a residential nursing care facility sensitive condition. The same logic used with the PPA assignment for residential nursing care facility identification is used with PPV assignment. Residential nursing care facilities are designated as one of the following places of service: SNF, nursing home, inpatient psychiatric facility, Intermediate Care Facility/Individuals with Intellectual Disabilities, residential substance abuse treatment facility, psychiatric residential treatment center, comprehensive inpatient rehabilitation facility. Refer to the place of service section of this manual for detailed logic for residential nursing care facility identification. Determine if reason for the visit is a trauma-related condition Additional trauma criteria are applied to determine if a PPV is potentially preventable for those patients treated in the ED coming from a residential nursing care facility. If a visit has a significant procedure EAPG assigned, and the reason for the visit is trauma-related, and the patient came from a residential nursing care facility, the ED visit would be considered preventable. This is based upon the premise that a nursing home facility should have measures to avoid trauma. For example, if a patient fell and sustained a hip fracture the fracture may have been avoided all together by preventing the adverse event, in this case a fall. A list of trauma diagnoses, when coded as the principal diagnosis, determines if the reason for the ED visit was trauma related. Potentially preventable visits (PPV) output Potentially preventable visits (PPV) contain a number of outputs including risk status, exclusion status, and reason. There are two risk (R) statuses for PPV: At Risk Potentially Preventable (RP) and At Risk Not Potentially Preventable (RN). There are two exclusion (E) statuses for PPV: Excluded Potentially Preventable (EP) and Excluded Not Potentially Preventable (EN). Within PPV, there are a few scenarios where exclusion logic is applied: 1. Exclusion logic is applied if the outpatient visit date falls on or within the admit and discharge dates of an inpatient admission. Any PPV claim that fits that criteria will be returned with a status of EP or EN and assigned a reason of 92 - Inpatient admission overlap. Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 36 2. If enabled, exclusion logic is applied if a line item is performed in an ER environment (place of service value of 23). Any PPV claim that fits that criteria will be returned with a status of EP or EN and assigned a reason of 97 - Line item performed in an ER setting. 3. If enabled, exclusion logic is applied to exclude claims that are not coded with a bill type of '13' indicating a claim not performed in an outpatient setting. Any PPV claim that fits that criteria will be returned with a status of EP or EN and assigned a reason of 98 - Nonoutpatient facility claim. For PPV, there are specific medical EAPGs that require additional code level detail to determine the potential preventability of a visit. For these EAPGs, the principal diagnosis is required to make a final determination. If the principal diagnosis for the claim is not considered potentially preventable, the claim will be returned with a status of RN. If the principal diagnosis is considered potentially preventable, the claim will be returned with a status of RP and the relevant reason assigned. Additionally, for EAPGs that require code level detail, a PPV may not be assigned in some cases due to diagnosis specific age criteria. If the principal diagnosis is potentially preventable but is associated with specific age criteria, the admission is not considered potentially preventable if the patient's age falls within that range. In this case, the claim will be returned with a status of RN. Potentially Preventable (RP) · 21 - Potentially Preventable PPV Reasons · 0 - Not Potentially Preventable · 1 - Acute illness not related to infection · 2 - Acute infections that could be treated in a primary care setting · 3 - Chronic illnesses related to malignancy · 4 - Other chronic illnesses except mental health, substance abuse and malignancy · 5 - Mental health and substance abuse encounters · 6 - Trauma · 7 - Not appropriate for ED · 92 - Inpatient admission overlap · 97 - Line item performed in an ER setting (exclusion logic) · 98 - Non outpatient facility claim (exclusion logic) Grouper assignment to one of the following EAPGs is not compatible with PPV and will output an error return (RX): · EAPG 993 Inpatient only procedures · EAPG 994 User customizable inpatient procedures · EAPG 999 Other unassigned Interventions to help reduce preventable emergency visits PPVs can identify patterns of potentially avoidable emergency department visits and may suggest areas where primary care services should be improved. If inappropriate ED utilization is to be minimized, however, structural changes in the organization and delivery of first contact care will be essential. The following are recommendations from the medical literature on community initiatives that can help reduce unnecessary ER visits: Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 37 · Establish medical homes where primary care physicians coordinate patients' care. · Start a telephone line where nurses direct callers to the best places for care. · Enroll children in telemedicine programs. · Improve the availability of after-hours care. · Increase enrollment in safety net programs. · Simplify health information so patients can learn to care for themselves and avoid the ER. · Educate the community on appropriate ER visits. · Create case management programs to help people manage chronic diseases. · Start workplace wellness programs to bolster workers' health. · Establish urgent care centers to take on patients who are not necessarily seen in an ED but who were not able to obtain a timely primary care physician appointment or in fact who do not have a primary care physician. Prevalence and potential cost savings related to preventable Emergency Department visits The Minnesota Department of Health published a study on the volume and payments for potentially preventable events within the state. They identified 1.2 million potentially preventable Emergency Department (ED) visits with an associated cost of $1.3 billion in 2012 alone.10 The distribution of the PPVs were observed to fall predominately upon Medicaid where Medicaid enrollees accounted for 14 percent of the population but 41 percent of PPVs. The New York State Department of Health has been publicly reporting PPVs for the Medicaid program since 2011.11 In 2011 there were 2,568,757 PPVs, a rate of 45.44 per 100 people. In 2013 the rate had barely changed at 45.31 per 100 people with 2,741,677 PPVs. Education, information and incentives are required to lower these rates with the potential to unlock billions of dollars for state budgets and the knock-on effects of reducing ED crowding and the need to maintain excess high-cost ED capacity. References 1. Centers for Disease Control. Fast Facts. http://www.cdc.gov/nchs/fastats/emergencydepartment.htm. Last accessed December 2016. 2. Machlin, SR, Medical Expenditure Panel Survey (MEPS), Statistical Brief 111: Expenses for a Hospital Emergency Room Visit, 2003, Adjusted to 2007 Data. Rockville, MD: Agency for Healthcare Research and Quality (AHRQ), 2006. Available at https://meps.ahrq.gov/data_files/publications/st111/stat111.pdf. Last accessed October 2016. Leah S. Honigman, MD, MPH; Jennifer L. Wiler, MD, MBA; Sean Rooks, BA; Adit A. Ginde, MD, MPH National Study of Non-urgent Emergency Department Visits and Associated Resource Utilization. The Western Journal of American Medicine. 2013; 14 (6): 609-616 3. National Hospital Ambulatory Medical Care Survey: 2011 Emergency Department Summary (U.S DHHS). 4. Somers A, Boukus E, Carrier E. Dispelling Myths About Emergency Department Use: Majority of Medicaid Visits Are for Urgent or More Serious Symptoms HSC Research Brief No. 23 July 2012. Center for Health Systems Change found at http://www.hschange.com/CONTENT/1302/. Last accessed October 2016. 5. Billings, J. found at http://wagner.nyu.edu/faculty/billings/nyued-background. Last accessed October 2016. 6. Ballard, Dustin W. MD, MBE; Price, Mary MA; Fung, Vicki PhD; Brand, Richard PhD; Reed, Mary E. DrPH; Fireman, Bruce MA; Newhouse, Joseph P. PhD; Selby, Joseph V. MD, MPH; Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 38 Hsu, John MD, MBA, MSCE Validation of an Algorithm for Categorizing the Severity of Hospital Emergency Department Visits Medical Care. January 2010. 48 (1): 58-63. 7. Washington State Hospital Association. The NYU Classification System for ED Visits: WSHA Technical Concerns found at http://www.wsha.org/files/169/NYU_Classification_System_for_EDVisits.pdf 8. Ballard, Dustin W. MD, MBE; Price, Mary MA; Fung, Vicki PhD; Brand, Richard PhD; Reed, Mary E. DrPH; Fireman, Bruce MA; Newhouse, Joseph P. PhD; Selby, Joseph V. MD, MPH; Hsu, John MD, MBA, MSCE Validation of an Algorithm for Categorizing the Severity of Hospital Emergency Department Visits Medical Care. January 2010. 48 (1): pp 58-63. Leah S. Honigman, MD, MPH; Jennifer L. Wiler, MD, MBA; Sean Rooks, BA; Adit A. Ginde, MD, MPH National Study of Non-urgent Emergency Department Visits and Associated Resource Utilization. The Western Journal of American Medicine. 2013. 14 (6): 609-616. 9. Gilboy N et al. Emergency Severity Index (ESI) A Triage Tool for Emergency Department Care Version Agency for Health Care Research and Quality found at http://www.ahrq.gov/professionals/systems/hospital/esi/esihandbk.pdf. Last accessed October 2016. 10. Minnesota Department of Health found at http://www.health.state.mn.us/healthreform/allpayer/potentially_preventable_events_072 115.pdf. Last accessed October 2016. 11. New York State Department of Health found at https://health.data.ny.gov/Health/Medicaid-Potentially-Preventable-Emergency-VisitP/cr7a-34ka. Last accessed October 2016. Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 39 Appendix C: Description of CRG Logic Clinical Risk Groups (CRGs) are a categorical clinical model that uses historical claims data to assign individuals to a single mutually exclusive category that defines an individual's chronic disease burden (Hughes, 2004). Each CRG is composed of a base CRG that describes the patient's most significant chronic conditions and two to six explicit severity levels that distinguish differences in disease burden due to severity of illness (e.g., a patient with diabetes and congestive heart failure at severity level 3). The CRG logic follows the logical progression of a disease. The CRG assignment process is as follows: Phase 1: Categorize diagnoses and procedures · All diagnoses are assigned to an MDC (Major Diagnostic Category) · Within each MDC, diagnoses are assigned to one of 557 EDCs (Episode Diagnostic Categories) · All procedures are assigned to one of 640 EPCs (Episode Procedure Category) · Each EDC is categorized as dominant chronic, moderate chronic, minor chronic, chronic manifestation, significant acute or minor acute · Only one diagnosis from an inpatient admission is needed to establish an EDC · Two diagnoses from different days are needed to establish an EDC for outpatient visits except for diagnoses for selected conditions and diagnosis codes which are in fact procedures (e.g., history of a heart transplant) · For inpatient services diagnoses from physician and other professional claims are not used (i.e., only the hospital claim is used). · Diagnoses from "other" providers (e.g., ambulances, freestanding laboratory, etc.) are not used. · Some diagnosis codes create multiple EDCs. (e.g., the diabetic neuropathy code creates both the chronic disease EDC for diabetes and the chronic manifestation EDC for diabetic neuropathy EDC). · Conditionality rules are also applied and affect diagnosis or severity assignment: Persistence and recurrence rules (e.g., hypertension must persist over a period of time to be considered an established diagnosis) Demographic (e.g., congestive heart failure among children vs. adults) · The temporal relationship between EDCs and EPCs is used to establish final EDCs EDCs can cause other EDCs to be "ignored" Acquired hemiplegia removes stroke from contributing to the severity of illness rating EPCs can cause EDC and EPCs to be "ignored" Angioplasty removes Angina from the severity logic Kidney transplant causes renal dialysis to be removed from the severity logic Phase 2: Identify chronic illnesses and specify their severity of illness · Each MDC with a chronic EDC will be assigned a PCD (Primary Chronic Disease) Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 40 · Only one PCD can be assigned per MDC. If there is more than one EDC within an MDC, the PCDs will be selected in hierarchical order within the MDC (e.g., dominant chronic EDCs selected before moderate chronic EDCs) · Some chronic EDCs cannot become PCDs if a certain other EDC is present (e.g., skin ulcers cannot be a PCD if diabetes is present) · After a PCD is selected it is assigned a severity of illness level · The severity level assignment for each PCD is establish by the presence of related conditions (e.g., skin ulcers in a diabetic) Phase 3: Assign the CRG · Assignment to one of 272 base CRGs based on the combination of PCDs that are present · The highest volume diseases or combinations of diseases are assigned a unique base CRG, for example: Diabetes Diabetes with CHF Diabetes with CHF and COPD · All CRGs are assigned to one of nine hierarchical health statuses ranging from catastrophic to healthy Status 1 Healthy Status 2 History of Acute Disease e.g., Chest Pain Status 3 Single Minor Chronic Disease e.g., Migraine Status 4 Minor Chronic Disease in Multiple Organ Systems e.g., Migraine and BPH Status 5 Single Dominant or Moderate Chronic Disease e.g., Diabetes Status 6 - Dominant or Moderate Chronic Disease in Multiple Organ Systems, e.g., Diabetes, and COPD Status 7 - Dominant Chronic Disease in Three or More Organ Systems, e.g., CHF, Diabetes, and COPD Status 8 - Malignancy, Under Active Treatment, e.g., Lung Cancer Status 9 - Catastrophic Conditions, e.g., Major Organ Transplant · Assignment is done from most serious (catastrophic) to least serious (healthy) · Each base CRG is subdivided into discrete severity subclasses based on the severity levels of the PCDs The CRGs (Version 2.1) are composed of 332 base CRGs that describes the individual's most significant chronic conditions and explicit severity levels that distinguish differences in disease burden due to severity of illness resulting in 1,414 individual CRGs. A more detailed description of CRGs is available at: https://apps.3mhis.com/docs/Groupers/ Clinical_Risk_Grouping_CRG/methodology_overview/grp401_crg_v2.1_meth_overview.pdf References Hughes JS, Averill RF, Eisenhandler J, Goldfield NI, Muldoon J, Neff JM, Gay JC. Clinical Risk Groups (CRGs): a classification system for risk-adjusted capitation-based payment and health care management. Med Care. 2004;42(1):81-90. Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 41 Appendix D: PPV %(A-E)/E and $(A-E) by CBSA State Alabama Alabama Alabama Alabama Alabama Alabama Alabama Alabama Alabama Alaska Alaska Alaska Arizona Arizona Arizona Arizona Arizona Arizona Arkansas Arkansas Arkansas Arkansas Arkansas California California California California California California California California California California California California CBSA Birmingham-Hoover, AL Huntsville, AL Montgomery, AL Mobile, AL Daphne-FairhopeFoley, AL Tuscaloosa, AL Florence-Muscle Shoals, AL Rural Alabama Aggregate small CBSAs Anchorage, AK Rural Alaska Aggregate small CBSAs Phoenix-MesaScottsdale, AZ Tucson, AZ Prescott, AZ Lake Havasu CityKingman, AZ Rural Arizona Aggregate small CBSAs Little Rock-North Little Rock-Conway, AR FayettevilleSpringdale-Rogers, AR-MO Fort Smith, AR-OK Rural Arkansas Aggregate small CBSAs Los Angeles-Long Beach-Anaheim, CA San FranciscoOakland-Hayward, CA San Diego-Carlsbad, CA Riverside-San Bernardino-Ontario, CA Sacramento-Roseville--ArdenArcade, CA San Jose-SunnyvaleSanta Clara, CA Oxnard-Thousand Oaks-Ventura, CA Fresno, CA Bakersfield, CA Santa Maria-Santa Barbara, CA Stockton-Lodi, CA Salinas, CA Count Benef Count PPVs 3,829 284 2,163 161 1,410 112 1,331 89 1,084 82 1,072 88 1,002 103 4,471 379 7,009 562 1,820 148 967 33 668 55 15,542 3,968 2,166 1,251 238 157 1,712 143 789 38 3,832 207 4,175 334 PPVs per 1000 Benef %(A-E)/E PPV Nat Norm $(A-E) PPV Nat Norm %(A-E)/E PPV BP Norm 74.17 74.47 79.55 67.24 -6.23 -6.57 5.05 -18.95 -13,077 -7,848 3,739 -14,499 7.23 6.84 20.13 -7.32 75.60 81.85 -3.20 -0.23 -1,876 -141 10.69 14.09 102.57 84.88 36.22 8.85 18,939 21,386 55.77 24.47 80.23 81.57 34.06 1.14 22.58 -45.72 4,409 18,951 -19,222 15.66 40.17 -37.93 82.63 27.07 8,148 45.30 80.49 60.00 72.67 10.74 -12.17 5.46 84,058 -22,862 5,652 26.63 0.43 20.60 83.52 48.58 14.11 -29.83 12,249 -11,291 30.48 -19.76 54.14 -21.50 -39,377 -10.23 80.09 10.56 22,125 26.42 2,043 153 1,545 103 4,490 215 6,947 555 31,567 2,097 14,178 1,208 8,500 580 74.78 66.50 47.97 79.82 66.43 85.17 68.28 -0.62 -15.08 -34.08 7.18 -16.61 21.60 -2.96 -664 -12,639 -77,178 25,727 -289,523 148,646 -12,274 13.64 -2.89 -24.62 22.56 -4.65 39.05 10.96 8,090 696 86.02 13.21 56,287 29.46 7,090 602 5,096 347 3,486 234 3,223 226 2,427 161 2,425 185 2,251 204 2,249 161 84.94 68.06 66.98 70.07 66.23 76.15 90.67 71.62 16.75 0.25 -9.53 -3.95 -14.87 12.69 21.84 6.16 59,880 596 -17,053 -6,433 -19,455 14,410 25,353 6,474 33.51 14.64 3.45 9.84 -2.65 28.86 39.33 21.39 Geographic Variation in Hospital Emergency Department Visits in the Medicare Population $(A-E) PPV BP Norm 13,264 7,146 13,025 -4,897 5,487 7,508 25,501 51,706 52,762 29,484 -13,945 11,926 182,303 713 18,633 23,147 -6,540 -16,391 48,430 12,706 -2,118 -48,760 70,726 -70,824 235,008 39,745 109,746 104,742 30,685 5,395 14,016 -3,034 28,661 39,921 19,669 42 State California California California California California California California California California California California California California Colorado Colorado Colorado Colorado Colorado Colorado Connecticut Connecticut Connecticut Connecticut Connecticut Delaware Delaware Delaware District of Columbia Florida Florida Florida Florida Florida Florida Florida Florida Florida CBSA San Luis Obispo-Paso Robles-Arroyo Grande, CA Santa Rosa, CA Visalia-Porterville, CA Chico, CA Redding, CA Santa CruzWatsonville, CA Modesto, CA Vallejo-Fairfield, CA Merced, CA Yuba City, CA Eureka-ArcataFortuna, CA Rural California Aggregate small CBSAs Denver-AuroraLakewood, CO Colorado Springs, CO Fort Collins, CO Boulder, CO Rural Colorado Aggregate small CBSAs Hartford-West Hartford-East Hartford, CT Bridgeport-StamfordNorwalk, CT New Haven-Milford, CT Norwich-New London, CT Aggregate small CBSAs Salisbury, MD-DE Dover, DE Rural Delaware Washington-ArlingtonAlexandria, DC-VAMD-WV Miami-Fort Lauderdale-West Palm Beach, FL Tampa-St. PetersburgClearwater, FL Orlando-KissimmeeSanford, FL Jacksonville, FL North Port-SarasotaBradenton, FL Cape Coral-Fort Myers, FL Deltona-Daytona Beach-Ormond Beach, FL Palm Bay-MelbourneTitusville, FL Port St. Lucie, FL Count Benef Count PPVs 2,116 160 2,038 157 1,977 132 1,949 161 1,840 156 1,740 101 1,583 154 1,521 157 1,224 109 1,118 69 1,093 62 2,571 99 6,725 519 6,741 529 2,801 219 1,527 127 1,039 75 2,390 77 4,707 349 4,391 437 4,009 327 3,514 393 1,260 141 970 103 3,928 355 1,300 93 7 1 24,047 2,035 16,543 1,355 11,547 883 7,859 600 6,999 570 6,535 425 4,825 272 3,563 266 3,413 219 3,002 231 PPVs per 1000 Benef %(A-E)/E PPV Nat Norm $(A-E) PPV Nat Norm %(A-E)/E PPV BP Norm 75.61 77.17 66.99 82.61 84.52 58.05 97.27 103.39 89.34 62.08 56.78 38.37 77.24 78.48 78.33 83.31 71.88 32.34 74.22 14.86 8.24 -12.76 9.58 26.57 -10.70 32.11 49.97 22.04 -16.88 -15.32 -43.30 8.72 8.38 10.27 25.92 8.00 -52.14 8.78 14,347 8,297 -13,422 9,758 22,624 -8,384 25,934 36,311 13,684 -9,765 -7,778 -52,206 28,880 28,348 14,159 18,149 3,836 -58,353 19,548 31.35 23.77 -0.24 25.31 44.73 2.12 51.07 71.49 39.55 -4.95 -3.16 -35.16 24.33 23.93 26.09 43.99 23.50 -45.27 24.40 99.43 81.57 111.86 112.28 106.57 90.50 71.82 118.11 24.03 8.47 36.25 34.04 38.92 17.97 -11.37 37.03 58,620 17,692 72,478 24,896 20,068 37,521 -8,301 155 41.83 24.04 55.81 53.27 58.85 34.90 1.35 56.69 84.62 18.65 221,701 35.68 81.93 76.48 76.31 81.43 65.05 56.40 -1.98 -9.20 -8.03 1.78 -13.74 -26.55 -18,945 -61,976 -36,269 6,901 -46,935 -68,176 12.09 3.84 5.17 16.38 -1.36 -16.01 74.70 64.26 76.98 -4.24 -21.40 -4.02 -8,160 -41,372 -6,703 9.51 -10.12 9.76 Geographic Variation in Hospital Emergency Department Visits in the Medicare Population $(A-E) PPV BP Norm 26,461 20,934 -219 22,536 33,311 1,453 36,071 45,431 21,477 -2,504 -1,404 -37,074 70,429 70,800 31,463 26,935 9,850 -44,308 47,478 89,233 43,911 97,568 34,076 26,540 63,729 861 207 370,854 101,315 22,602 20,443 55,601 -4,073 -35,954 16,013 -17,106 14,236 43 State Florida Florida Florida Florida Florida Florida Florida Florida Florida Florida Florida Florida Florida Florida Georgia Georgia Georgia Georgia Georgia Georgia Georgia Hawaii Hawaii Idaho Idaho Idaho Illinois Illinois Illinois Illinois Illinois Illinois Illinois Indiana Indiana Indiana Indiana Indiana Indiana Indiana Iowa Iowa CBSA Naples-ImmokaleeMarco Island, FL Pensacola-Ferry PassBrent, FL Lakeland-Winter Haven, FL Ocala, FL The Villages, FL Crestview-Fort Walton Beach-Destin, FL Punta Gorda, FL Homosassa Springs, FL Sebastian-Vero Beach, FL Gainesville, FL Panama City, FL Tallahassee, FL Rural Florida Aggregate small CBSAs Atlanta-Sandy Springs-Roswell, GA Augusta-Richmond County, GA-SC Savannah, GA Columbus, GA-AL Macon, GA Rural Georgia Aggregate small CBSAs Urban Honolulu, HI Aggregate small CBSAs Boise City, ID Rural Idaho Aggregate small CBSAs Chicago-NapervilleElgin, IL-IN-WI Peoria, IL Rockford, IL Ottawa-Peru, IL Springfield, IL Rural Illinois Aggregate small CBSAs Indianapolis-CarmelAnderson, IN Evansville, IN-KY South BendMishawaka, IN-MI Fort Wayne, IN Terre Haute, IN Rural Indiana Aggregate small CBSAs Omaha-Council Bluffs, NE-IA Des Moines-West Des Moines, IA Count Benef Count PPVs 2,951 196 2,621 247 2,583 151 2,346 160 1,777 129 1,733 158 1,726 115 1,447 107 1,430 113 1,288 95 1,215 100 1,064 108 2,319 130 3,010 267 16,932 1,279 2,798 158 1,425 94 1,334 95 1,053 117 4,697 308 11,002 905 2,851 158 1,734 102 2,224 189 1,347 18 5,454 332 39,412 2,205 1,705 1,120 1,014 4,682 2,999 147 153 80 103 252 11,303 987 8,320 809 1,795 165 1,475 114 1,455 135 1,143 97 3,097 127 10,953 880 4,183 324 2,829 162 PPVs per 1000 Benef %(A-E)/E PPV Nat Norm $(A-E) PPV Nat Norm %(A-E)/E PPV BP Norm 66.32 -7.59 -11,137 5.67 94.28 15.96 23,576 32.61 58.40 68.28 72.63 -29.15 -15.08 -7.68 -43,006 -19,716 -7,440 -18.98 -2.90 5.57 91.41 66.36 73.86 14.36 -19.15 -6.37 13,784 -18,805 -5,042 30.77 -7.55 7.06 79.03 73.89 81.98 101.96 55.85 2.15 -5.62 4.69 33.37 -31.38 1,648 -3,930 3,093 18,811 -41,048 16.81 7.92 19.71 52.51 -21.53 88.82 10.18 17,117 25.99 75.53 1.13 9,912 15.64 56.49 66.12 70.92 110.70 65.48 -22.08 -10.65 -11.52 37.44 -18.10 -31,037 -7,780 -8,534 22,005 -47,098 -10.90 2.18 1.18 57.17 -6.35 82.25 55.29 2.65 -22.65 16,170 -31,998 17.38 -11.55 59.08 84.96 13.07 -9.56 18.47 -80.31 -7,501 20,417 -49,764 3.42 35.48 -77.48 60.88 -14.46 -38,900 -2.19 76.09 66.67 89.66 71.77 101.98 53.91 -0.25 -14.09 16.02 -0.74 31.59 -30.48 -5,158 -16,709 14,624 -413 17,204 -76,693 14.07 -1.76 32.67 13.51 50.48 -20.50 87.31 13.07 79,064 29.30 97.21 91.72 25.66 16.16 114,439 15,874 43.69 32.83 77.28 93.08 85.23 40.97 1.65 14.23 1.40 -45.98 1,279 11,694 931 -74,842 16.23 30.63 15.95 -38.22 80.35 2.53 15,043 17.24 77.53 6.64 13,994 21.94 57.28 -20.25 -28,510 -8.80 $(A-E) PPV BP Norm 7,282 42,109 -24,489 -3,311 4,718 25,832 -6,484 4,886 11,270 4,840 11,367 25,885 -24,632 38,219 119,886 -13,395 1,391 766 29,381 -14,439 92,843 -14,272 2,350 34,287 -41,987 -5,142 256,295 -1,827 26,083 6,630 24,039 -45,114 154,968 170,418 28,201 11,032 22,005 9,286 -54,413 89,691 40,444 -10,840 Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 44 State Iowa Iowa Iowa Iowa Kansas Kansas Kansas Kansas Kentucky Kentucky Kentucky Kentucky Louisiana Louisiana Louisiana Louisiana Louisiana Louisiana Louisiana Louisiana Maine Maine Maine Maine Maryland Maryland Maryland Maryland Massachusetts Massachusetts Massachusetts Massachusetts Massachusetts Massachusetts Massachusetts Michigan Michigan Michigan Michigan Michigan Michigan Michigan CBSA Davenport-MolineRock Island, IA-IL Cedar Rapids, IA Rural Iowa Aggregate small CBSAs Wichita, KS Topeka, KS Rural Kansas Aggregate small CBSAs Louisville/Jefferson County, KY-IN Lexington-Fayette, KY Rural Kentucky Aggregate small CBSAs New Orleans-Metairie, LA Lafayette, LA Shreveport-Bossier City, LA Baton Rouge, LA Lake Charles, LA Houma-Thibodaux, LA Rural Louisiana Aggregate small CBSAs Portland-South Portland, ME Bangor, ME Rural Maine Aggregate small CBSAs Baltimore-ColumbiaTowson, MD HagerstownMartinsburg, MD-WV Rural Maryland Aggregate small CBSAs Boston-CambridgeNewton, MA-NH Worcester, MA-CT Springfield, MA Barnstable Town, MA Pittsfield, MA Rural Massachusetts Aggregate small CBSAs Detroit-WarrenDearborn, MI Grand RapidsWyoming, MI Lansing-East Lansing, MI Flint, MI Ann Arbor, MI Kalamazoo-Portage, MI Rural Michigan Count Benef Count PPVs 2,086 170 1,380 149 6,446 148 6,613 437 3,353 263 1,702 129 3,442 71 5,532 394 6,103 454 1,929 185 7,098 545 7,564 650 3,355 325 2,558 209 2,382 242 2,366 180 1,169 117 1,146 95 2,323 198 5,048 474 2,930 310 1,048 64 3,354 196 1,311 146 15,554 1,427 1,565 135 743 77 2,265 218 24,121 4,022 3,433 2,484 1,281 65 2,244 397 326 208 128 6 697 55 17,843 1,631 2,932 330 2,164 129 1,783 136 1,420 89 1,375 100 5,223 267 PPVs per 1000 Benef %(A-E)/E PPV Nat Norm $(A-E) PPV Nat Norm %(A-E)/E PPV BP Norm 81.55 108.03 22.92 10.07 34.74 -68.90 10,788 26,637 -226,787 25.87 54.08 -64.43 66.14 78.53 75.77 20.66 -10.00 7.32 -0.18 -72.15 -33,688 12,448 -159 -127,681 2.91 22.72 14.15 -68.15 71.15 -5.57 -16,085 7.98 74.34 95.82 76.75 -5.72 26.29 -6.10 -19,083 26,665 -24,535 7.81 44.41 7.37 85.98 7.57 31,698 23.00 96.98 81.76 20.31 0.10 38,068 139 37.58 14.46 101.57 76.17 100.03 83.11 85.29 24.14 -6.86 26.07 1.97 3.52 32,606 -9,195 16,758 1,276 4,673 41.96 6.51 44.16 16.60 18.38 93.85 14.13 40,639 30.51 105.94 61.34 58.46 49.70 -20.73 -22.27 71,414 -11,650 -38,940 71.18 -9.35 -11.12 111.41 60.57 38,181 83.61 91.75 19.86 163,830 37.06 86.17 104.02 7.90 33.11 6,841 13,323 23.38 52.22 96.30 22.26 27,519 39.80 93.05 98.63 94.98 83.65 99.81 90.17 20.14 30.29 26.32 9.77 32.48 24.31 260,689 63,915 47,087 12,817 21,725 794 37.38 48.99 44.45 25.52 51.50 42.15 78.40 8.38 2,928 23.93 91.38 7.86 82,309 23.33 112.63 42.52 68,278 62.98 59.63 76.16 62.58 -25.28 -12.67 -17.58 -30,258 -13,648 -13,134 -14.56 -0.13 -5.75 72.87 51.14 -2.17 -32.70 -1,543 -89,948 11.86 -23.04 Geographic Variation in Hospital Emergency Department Visits in the Medicare Population $(A-E) PPV BP Norm 24,229 36,259 -185,476 8,577 33,785 11,077 -105,471 20,167 22,769 39,394 25,920 84,280 61,588 18,311 49,556 7,633 24,825 9,400 21,318 76,740 89,448 -4,597 -17,000 46,092 267,382 17,711 18,373 43,036 423,169 90,394 69,535 29,281 30,118 1,204 7,313 213,791 88,429 -15,237 -125 -3,757 7,364 -55,429 45 State Michigan Minnesota Minnesota Minnesota Minnesota Mississippi Mississippi Mississippi Mississippi Mississippi Missouri Missouri Missouri Missouri Missouri Montana Montana Nebraska Nebraska Nebraska Nevada Nevada Nevada Nevada New Hampshire New Hampshire New Hampshire New Hampshire New Hampshire New Jersey New Jersey New Jersey New Jersey New Mexico New Mexico New Mexico New York New York New York New York CBSA Aggregate small CBSAs Minneapolis-St. PaulBloomington, MN-WI Duluth, MN-WI Rural Minnesota Aggregate small CBSAs Jackson, MS Gulfport-BiloxiPascagoula, MS Tupelo, MS Rural Mississippi Aggregate small CBSAs St. Louis, MO-IL Kansas City, MO-KS Springfield, MO Rural Missouri Aggregate small CBSAs Rural Montana Aggregate small CBSAs Lincoln, NE Rural Nebraska Aggregate small CBSAs Las Vegas-HendersonParadise, NV Reno, NV Rural Nevada Aggregate small CBSAs Manchester-Nashua, NH Claremont-Lebanon, NH-VT Concord, NH Rural New Hampshire Aggregate small CBSAs Allentown-BethlehemEaston, PA-NJ Atlantic CityHammonton, NJ Trenton, NJ Aggregate small CBSAs Albuquerque, NM Rural New Mexico Aggregate small CBSAs New York-NewarkJersey City, NY-NJPA Albany-SchenectadyTroy, NY Buffalo-CheektowagaNiagara Falls, NY Rochester, NY Count Benef Count PPVs 12,954 1,078 7,327 679 1,102 81 2,287 85 5,946 537 2,825 216 2,053 193 1,093 89 5,165 344 6,225 11,743 8,499 1,829 6,300 610 1,076 737 163 315 7,147 573 2,731 54 4,049 293 1,691 89 3,005 35 3,035 184 6,782 508 2,187 143 233 4 2,207 132 2,257 166 1,867 52 1,107 90 550 14 1,682 99 4,637 377 1,747 117 1,610 151 1,809 168 2,776 175 852 29 5,780 447 PPVs per 1000 Benef %(A-E)/E PPV Nat Norm $(A-E) PPV Nat Norm %(A-E)/E PPV BP Norm 83.19 5.97 42,071 21.18 92.71 73.91 37.18 20.28 0.63 -48.41 79,372 356 -55,294 37.54 15.08 -41.01 90.29 76.42 20.69 2.46 63,789 3,594 38.01 17.17 94.13 81.48 66.67 19.08 7.11 -14.44 21,459 4,095 -40,268 36.17 22.48 -2.16 98.07 91.65 86.72 89.03 50.01 26.03 11.11 14.93 14.94 -33.50 87,366 74,565 66,351 14,666 -109,963 44.11 27.05 31.42 31.43 -23.95 80.11 19.88 3.39 -70.53 13,014 -90,042 18.23 -66.30 72.44 52.38 11.53 7.10 -24.32 -83.94 13,477 -19,726 -125,485 22.47 -13.46 -81.64 60.77 -16.44 -25,154 -4.45 74.87 65.51 18.38 0.02 -6.03 -74.50 80 -6,367 -8,667 14.38 7.46 -70.84 59.90 -16.04 -17,504 -3.99 73.65 1.25 1,417 15.77 27.62 81.58 25.66 -59.65 16.60 -63.38 -52,833 8,909 -16,926 -53.86 33.33 -58.12 59.13 -14.65 -11,833 -2.41 81.32 -0.17 -436 14.16 66.99 93.52 -17.60 16.51 -17,320 14,790 -5.77 33.24 92.64 62.95 33.77 17.48 -8.37 -47.86 17,283 -11,063 -18,303 34.34 4.78 -40.38 77.30 9.53 26,946 25.25 80,297 4,933 3,426 252 2,952 281 2,579 236 61.44 73.46 95.33 91.60 -19.96 -4.23 16.31 18.07 -852,734 -7,706 27,345 25,059 -8.48 9.51 33.00 35.02 Geographic Variation in Hospital Emergency Department Visits in the Medicare Population $(A-E) PPV BP Norm 130,515 128,488 7,395 -40,960 102,474 21,918 35,573 11,327 -5,268 129,497 158,810 122,127 26,987 -68,763 61,174 -74,020 37,296 -9,546 -106,724 -5,957 44,230 6,893 -7,207 -3,810 15,696 -41,717 15,645 -13,574 -1,699 32,412 -4,968 26,029 29,689 5,524 -13,503 62,421 -316,673 15,150 48,388 42,458 46 State New York New York New York New York New York New York North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Dakota North Dakota Ohio Ohio Ohio Ohio Ohio Ohio Ohio Ohio Ohio Ohio Oklahoma Oklahoma Oklahoma Oklahoma Oregon Oregon Oregon Oregon Oregon Oregon Oregon CBSA Syracuse, NY Utica-Rome, NY Binghamton, NY Kingston, NY Rural New York Aggregate small CBSAs Charlotte-ConcordGastonia, NC-SC Virginia BeachNorfolk-Newport News, VA-NC Raleigh, NC Myrtle BeachConway-North Myrtle Beach, SC-NC Asheville, NC Greensboro-High Point, NC Winston-Salem, NC Durham-Chapel Hill, NC Hickory-LenoirMorganton, NC Wilmington, NC Fayetteville, NC New Bern, NC Rocky Mount, NC Rural North Carolina Aggregate small CBSAs Rural North Dakota Aggregate small CBSAs Cleveland-Elyria, OH Cincinnati, OH-KY-IN Columbus, OH Dayton, OH Youngstown-WarrenBoardman, OH-PA Toledo, OH Akron, OH Canton-Massillon, OH Rural Ohio Aggregate small CBSAs Oklahoma City, OK Tulsa, OK Rural Oklahoma Aggregate small CBSAs Portland-VancouverHillsboro, OR-WA Eugene, OR Medford, OR Salem, OR Bend-Redmond, OR Rural Oregon Aggregate small CBSAs Count Benef Count PPVs 2,484 164 1,484 130 1,260 90 1,140 72 2,331 147 7,449 659 9,587 859 PPVs per 1000 Benef 66.06 87.49 71.41 63.21 63.01 %(A-E)/E PPV Nat Norm -10.45 13.08 -0.78 -11.95 -16.29 $(A-E) PPV Nat Norm -13,271 10,410 -490 -6,779 -19,806 %(A-E)/E PPV BP Norm 2.40 29.31 13.46 0.68 -4.28 88.41 14.79 58,796 31.26 89.61 20.33 100,594 37.60 $(A-E) PPV BP Norm 2,666 20,395 7,397 339 -4,546 108,692 162,684 8,630 701 4,508 369 81.18 81.79 3.63 7.06 17,019 16,850 18.51 22.42 75,815 46,802 4,359 327 2,997 221 2,374 217 2,196 192 2,166 153 1,887 153 1,797 140 1,624 153 1,092 88 1,005 114 4,746 300 12,950 1,240 1,507 28 1,498 70 8,433 747 7,763 660 6,168 572 3,067 315 2,440 222 2,401 210 2,243 212 1,518 172 2,599 129 14,764 6,195 4,527 4,345 1,401 545 308 229 6,311 572 5,438 442 1,463 78 1,384 113 1,143 93 1,113 47 1,033 12 5,823 294 75.09 73.79 91.34 87.21 70.57 80.89 77.92 94.48 80.46 113.44 63.25 95.75 18.88 46.63 88.53 84.98 92.68 102.65 91.04 87.55 94.65 113.07 49.74 94.87 88.00 68.10 52.63 90.61 81.23 53.65 81.57 81.79 42.51 11.89 50.53 4.23 2.23 12.08 8.11 -4.85 2.41 1.81 15.31 4.43 40.61 -19.16 22.18 -72.71 -34.20 11.25 8.43 17.13 27.75 15.41 10.31 16.76 44.30 -38.20 18.29 11.46 -13.13 -32.57 15.34 15.33 -21.42 20.39 16.48 -36.07 -81.47 -28.46 9,212 3,341 16,196 9,958 -5,402 2,487 1,729 14,115 2,581 22,817 -49,300 156,025 -52,540 -25,160 52,326 35,550 57,930 47,389 20,549 13,612 21,120 36,518 -55,378 150,085 38,855 -32,292 -76,562 52,707 40,690 -14,823 13,249 9,166 -18,494 -37,428 -81,121 19.19 16.90 28.17 23.63 8.80 17.10 16.43 31.85 19.41 60.79 -7.56 39.72 -68.79 -24.76 27.22 23.99 33.94 46.08 31.97 26.14 33.52 65.01 -29.33 35.27 27.46 -0.66 -22.90 31.89 31.88 -10.14 37.67 33.20 -26.89 -78.81 -18.19 36,522 22,154 33,021 25,363 8,570 15,450 13,690 25,688 9,899 29,869 -17,004 244,289 -43,472 -15,928 110,691 88,461 100,378 68,823 37,289 30,185 36,933 46,862 -37,186 253,060 81,391 -1,426 -47,065 95,823 74,001 -6,137 21,404 16,147 -12,059 -31,662 -45,348 Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 47 State Pennsylvania Pennsylvania Pennsylvania Pennsylvania Pennsylvania Pennsylvania Pennsylvania Pennsylvania Pennsylvania Pennsylvania Pennsylvania Rhode Island South Carolina South Carolina South Carolina South Carolina South Carolina South Carolina South Carolina South Carolina South Dakota South Dakota South Dakota Tennessee Tennessee Tennessee Tennessee Tennessee Tennessee Tennessee Tennessee Texas Texas Texas Texas Texas Texas Texas Texas CBSA Philadelphia-CamdenWilmington, PA-NJDE-MD Pittsburgh, PA Scranton--WilkesBarre--Hazleton, PA Lancaster, PA Harrisburg-Carlisle, PA York-Hanover, PA Reading, PA Erie, PA ChambersburgWaynesboro, PA Rural Pennsylvania Aggregate small CBSAs Providence-Warwick, RI-MA Greenville-AndersonMauldin, SC Columbia, SC Charleston-North Charleston, SC Hilton Head IslandBluffton-Beaufort, SC Spartanburg, SC Florence, SC Rural South Carolina Aggregate small CBSAs Sioux Falls, SD Rural South Dakota Aggregate small CBSAs Nashville-Davidson-Murfreesboro-Franklin, TN Memphis, TN-MS-AR Knoxville, TN Chattanooga, TN-GA Kingsport-BristolBristol, TN-VA Clarksville, TN-KY Rural Tennessee Aggregate small CBSAs Dallas-Fort WorthArlington, TX Houston-The Woodlands-Sugar Land, TX San Antonio-New Braunfels, TX Austin-Round Rock, TX El Paso, TX McAllen-EdinburgMission, TX Killeen-Temple, TX Beaumont-Port Arthur, TX Count Benef Count PPVs PPVs per 1000 Benef %(A-E)/E PPV Nat Norm $(A-E) PPV Nat Norm %(A-E)/E PPV BP Norm 28,551 2,213 6,393 580 3,479 315 2,469 149 2,300 171 2,092 132 1,971 108 1,120 97 1,033 97 2,649 164 10,476 906 7,654 728 4,410 296 4,272 288 3,956 370 1,817 100 1,603 145 1,310 132 1,930 147 3,701 302 1,190 66 1,422 33 2,353 161 77.49 90.75 90.51 60.54 74.35 62.97 55.03 86.18 94.18 61.80 86.50 95.18 67.01 67.43 93.61 55.16 90.49 100.75 76.35 81.65 55.54 23.50 68.29 -0.71 17.71 13.03 -22.48 -5.71 -19.32 -32.40 12.79 17.53 -19.32 8.18 19.11 -8.73 -5.23 26.52 -12.64 18.29 27.66 2.72 10.77 -18.96 -65.95 0.38 -10,949 60,487 25,150 -30,034 -7,172 -21,855 -36,025 7,585 10,056 -27,167 47,483 80,988 -19,582 -11,028 53,794 -10,048 15,543 19,816 2,704 20,355 -10,716 -44,855 417 13.54 34.60 29.25 -11.35 7.83 -7.74 -22.70 28.98 34.40 -7.74 23.70 36.20 4.37 8.37 44.68 -0.10 35.27 45.98 17.46 26.66 -7.33 -61.07 14.78 6,119 428 5,914 471 4,020 280 2,787 203 1,475 119 1,111 92 4,372 297 8,239 561 20,062 1,590 69.88 79.70 69.74 72.74 80.98 82.44 67.85 68.04 79.26 -10.13 5.60 -10.75 -7.88 1.18 6.20 -15.05 -12.21 -0.76 -33,387 17,326 -23,403 -12,017 966 3,704 -36,422 -54,007 -8,406 2.77 20.76 2.06 5.34 15.70 21.44 -2.86 0.39 13.49 15,102 1,118 7,788 468 6,053 428 1,692 117 1,591 139 1,582 114 1,547 123 74.02 60.06 70.79 69.24 87.18 72.27 79.79 -3.20 -18.81 -1.56 -5.59 -1.31 -5.76 -5.15 -25,572 -75,100 -4,700 -4,808 -1,278 -4,843 -4,641 10.70 -7.16 12.57 7.96 12.85 7.76 8.47 $(A-E) PPV BP Norm 182,851 103,352 49,380 -13,265 8,601 -7,656 -22,070 15,027 17,254 -9,520 120,338 134,182 8,576 15,411 79,251 -70 26,208 28,808 15,179 44,082 -3,623 -36,320 14,340 7,991 56,144 3,915 7,122 11,233 11,204 -6,052 1,525 130,936 74,855 -24,997 33,153 5,985 10,945 5,707 6,678 Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 48 State Texas Texas Texas Texas Texas Texas Texas Texas Texas Utah Utah Utah Utah Utah Utah Vermont Vermont Vermont Virginia Virginia Virginia Virginia Virginia Virginia Virginia Washington Washington Washington Washington Washington Washington Washington Washington Washington Washington West Virginia West Virginia West Virginia West Virginia Wisconsin Wisconsin Wisconsin CBSA Corpus Christi, TX Tyler, TX Amarillo, TX Brownsville-Harlingen, TX Lubbock, TX Longview, TX Waco, TX Rural Texas Aggregate small CBSAs Salt Lake City, UT Ogden-Clearfield, UT Provo-Orem, UT St. George, UT Rural Utah Aggregate small CBSAs Burlington-South Burlington, VT Rural Vermont Aggregate small CBSAs Richmond, VA Roanoke, VA Lynchburg, VA Charlottesville, VA BlacksburgChristiansburgRadford, VA Rural Virginia Aggregate small CBSAs Seattle-TacomaBellevue, WA Spokane-Spokane Valley, WA Kennewick-Richland, WA Bremerton-Silverdale, WA Olympia-Tumwater, WA Yakima, WA Port Angeles, WA Bellingham, WA Rural Washington Aggregate small CBSAs Huntington-Ashland, WV-KY-OH Charleston, WV Rural West Virginia Aggregate small CBSAs MilwaukeeWaukesha-West Allis, WI Madison, WI Green Bay, WI Count Benef Count PPVs 1,351 91 1,184 93 1,165 84 1,157 102 1,105 86 1,082 98 1,006 77 9,249 487 14,744 2,686 1,933 1,099 1,006 913 1,275 203 127 64 71 26 732 59 1,409 120 1,370 30 1,450 148 6,227 557 2,110 166 1,934 119 1,495 109 PPVs per 1000 Benef 67.52 78.74 72.11 %(A-E)/E PPV Nat Norm -12.85 -1.65 -5.34 $(A-E) PPV Nat Norm -9,321 -1,086 -3,281 %(A-E)/E PPV BP Norm -0.34 12.46 8.25 88.45 78.17 90.84 76.65 52.67 -2.19 -3.52 16.06 1.59 -30.97 -1,590 -2,181 9,428 834 -151,456 11.84 10.33 32.72 16.16 -21.06 86.47 75.58 65.44 58.63 70.61 28.69 8.94 4.70 -4.84 -19.75 1.45 -55.06 72,488 6,317 -4,458 -10,988 705 -22,248 24.57 19.73 8.82 -8.23 16.01 -48.62 81.27 14.88 5,340 31.37 85.12 21.68 24.19 -67.83 16,189 -43,402 42.01 -63.21 101.88 89.45 78.69 61.45 72.72 45.94 19.52 11.62 -18.19 2.65 32,227 63,029 11,977 -18,313 1,948 66.89 36.67 27.64 -6.45 17.39 1,053 6,458 5,270 12,412 2,968 1,683 1,573 1,348 1,289 1,004 1,001 1,845 6,155 2,340 1,374 2,982 3,411 99 94.06 480 74.25 500 94.97 841 67.75 223 75.06 120 71.31 115 73.08 68 50.41 126 97.83 53 52.87 64 63.79 25 13.75 351 57.01 255 108.82 120 87.58 166 55.61 318 93.20 22.37 -1.57 22.55 -1.54 7.75 -3.63 8.92 -22.85 38.46 -21.91 -3.48 -78.16 -17.12 30.84 9.56 -27.49 12.23 12,549 -5,297 63,811 -9,113 11,102 -3,136 6,522 -13,943 24,273 -10,322 -1,597 -62,926 -50,230 41,591 7,277 -43,562 24,002 39.94 12.56 40.13 12.59 23.21 10.20 24.55 -11.77 58.33 -10.71 10.37 -75.02 -5.23 49.61 25.28 -17.08 28.33 5,564 2,978 1,033 564 101.36 177 59.54 79 76.37 29.97 -10.97 1.36 90,113 -15,143 731 48.62 1.80 15.90 $(A-E) PPV BP Norm -218 7,158 4,436 7,509 5,605 16,793 7,435 -90,083 174,276 23,178 7,103 -4,005 6,795 -17,177 9,844 24,588 -35,372 41,030 103,562 24,915 -5,678 11,159 19,589 37,072 99,328 65,167 29,084 7,696 15,701 -6,283 32,195 -4,410 4,157 -52,822 -13,411 58,518 16,829 -23,673 48,638 127,854 2,178 7,500 Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 49 State Wisconsin Wisconsin Wyoming Wyoming CBSA Rural Wisconsin Aggregate small CBSAs Rural Wyoming Aggregate small CBSAs Count Benef Count PPVs 4,706 180 8,671 724 1,483 26 2,555 209 PPVs per 1000 Benef 38.31 %(A-E)/E PPV Nat Norm -47.24 $(A-E) PPV Nat Norm -111,857 %(A-E)/E PPV BP Norm -39.67 83.46 17.35 12.47 -74.24 55,592 -51,396 28.61 -70.54 81.76 22.30 26,396 39.85 $(A-E) PPV BP Norm -82,141 111,551 -42,707 41,251 Geographic Variation in Hospital Emergency Department Visits in the Medicare Population 50 Health Information Systems 575 West Murray Boulevard Salt Lake City, UT 84123 U.S.A. 800 367 2447 www.3m.com/his 3M is a trademark of 3M Company. 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