Monitoring performance of Medicare HMOs continues. Hospitalization Rates for Ambulatory Care Sensitive Conditions in California Medicare HMOs
|
|
- Pauline Scott
- 5 years ago
- Views:
Transcription
1 ORIGINAL RESEARCH Hospitalization Rates for Ambulatory Care Sensitive Conditions in California Medicare HMOs Feng Zeng, PhD, June F. O Leary, PhD, Elizabeth M. Sloss, PhD, Nasreen Dhanani, PhD, and Glenn Melnick, PhD Abstract Objective: To examine annual hospitalization rates for ambulatory care sensitive conditions (ACSCs) among Medicare HMO beneficiaries. Design: Cross-sectional descriptive study. Setting and participants: Medicare beneficiaries aged 65 years and older continuously enrolled in 1 of 15 California Medicare HMOs from 1 January through 31 December Measurements: Hospitalization rates overall and for each of 15 ACSCs (bacterial pneumonia, cellulitis, dehydration, gastric and duodenal ulcer, hypoglycemia, hypokalemia, severe ear/nose/throat infections, urinary tract infections, asthma/chronic obstructive pulmonary disease, congestive heart failure, diabetes, hypertension, seizure disorder, influenza, and malnutrition). The rate for the 15 ACSCs combined as well as acute, chronic, and preventable indices were also estimated. Results: Of the 1.2 million Medicare beneficiaries enrolled in California HMOs during 2001, 24% were 80 years or older (range among plans, 15% 34%), 5% were African American (range, 1% 25%), and 6% were Medicaid-eligible (range, 3% 16%). Enrollees experienced a total of 315,503 hospitalizations in 2001 (267 per 1000), 22% of which were for an ACSC. ACSC hospitalization rates varied widely by plan and were higher among older enrollees, males, and those eligible for Medicaid. Conclusion: ACSC hospitalization rates are easy to calculate based on administrative data. These rates can be used by individual plans as a method to screen for possible access and quality of care problems. Monitoring performance of Medicare HMOs continues to be a topic of considerable interest to policymakers and health plan administrators. A measure that has been suggested for tracking the performance of these plans is hospitalization rates for ambulatory care sensitive conditions (ACSCs) [1]. This approach is based on the assumption that providing patients with timely and appropriate care in the outpatient setting will lead to reduced hospitalizations for ACSCs [2 4]. High ACSC admission rates may be indicative of problems with availability, access, or appropriateness of primary care, whereas low ACSC admission rates might reflect provision of effective primary care and preventive services [5]. Use of annual ACSC hospitalization rates to monitor access to care in Medicare HMOs builds on existing models developed for monitoring care for Medicare fee-for-service (FFS) beneficiaries. The rates are based on inpatient hospital encounter data that are routinely collected by Medicare HMOs. Tracking ACSC hospitalization rates over time is a practical tool that Medicare HMOs could use to identify clinical areas and subgroups of enrollees in further need of quality assessment and improvement. McCall et al [1] investigated the feasibility of using ACSC hospitalization rates to monitor the performance of Medicare HMOs. They hypothesized that ACSC hospitalization rates could be used for monitoring Medicare HMOs if 3 conditions are met: complete hospital data are available, ACSC hospitalization rates are considered to be valid measures of providing ambulatory care, and ACSC hospitalization rates are statistically reliable [1]. Using hospital inpatient encounter data for July 1997 to June 1998, McCall et al identified 305 Medicare HMOs with up to 255,520 enrollees and up to 54,009 discharges per HMO. They first evaluated the completeness of the inpatient encounter data and found it to be sufficiently complete based on a comparison of hospitalization rates for all conditions versus only ACSCs between Medicare HMO and FFS beneficiaries. Hospitalization rates were then calculated by age and sex and for ACSCs individually and collectively. They found that although Medicare HMO rates were consistently lower than Medicare FFS, the Medicare HMO From the School of Policy, Planning, and Development, University of Southern California, and therand Corporation, Santa Monica, CA, andwashington, DC. Vol. 12, No. 11 November 2005 JCOM 559
2 rates varied as expected with regard to geographic region and demographic characteristics. For example, higher ACSC hospitalization rates were observed in the Northeast and among those aged 85 years and older. Many studies have investigated inpatient utilization differences between Medicare FFS and HMO populations, with much of the difference attributed to healthier HMO enrollees (ie, favorable selection) and some to managed care practices of HMOs [6 14]. While differences in enrollee characteristics and utilization among Medicare HMOs are expected to be less than the selection difference between Medicare HMOs and FFS, these differences by plan have not been well documented. Understanding these differences by plan is important because rates of hospitalization for ACSCs have been shown to be sensitive to patient characteristics such as age, race/ethnicity, and income [4,15 19]. In the current study, we examine rates of hospitalization for 15 ACSCs using data on beneficiaries aged 65 years and older enrolled in Medicare HMOs in California during The specific objectives of the study were to compare annual hospitalization rates in Medicare HMOs for all conditions and for ACSC conditions by beneficiary characteristics and to estimate rates of ACSC hospitalizations at the plan level for Medicare HMOs. Methods Data Sources Data for this study were derived from linking Medicare enrollment data on all beneficiaries in California between January and December 2001 from the Centers for Medicare & Medicaid Services (CMS) denominator files to inpatient discharge data for short-term stays from the California Office of Statewide Health Planning and Development. All nonfederal hospitals in California submit discharge records to the state agency irrespective of payer source. Social Security number served as the starting point for the linkage, and if it was missing from either the Medicare data or discharge data, the record was dropped (< 2% of records from either file). Records were linked using probabilistic matching based on Social Security number, zip code of residence, date of birth, date of death, gender, and race/ethnicity [20 24]. The level of agreement between Social Security numbers in the final linked file was nearly 99%. The linkages were performed by Health Information Solutions with approvals from the Committee for the Protection of Human Subjects, California Health and Human Services Agency, and the University Park Institutional Review Board, University of Southern California (USC), under a CMS data use agreement between all parties with access to the confidential data. The linked data were returned to USC after all potential identifiers were stripped. CMS plan contract numbers from our data were linked to model type from CMS [25]. Selection of Ambulatory Care Sensitive Conditions We identified ACSC hospitalizations based on the principal diagnosis using codes from the ICD-9-CM. We selected the 15 ACSCs that were identified by 2 clinical consultants as particularly relevant to the elderly in a study by McCall et al [1]. The 15 ACSCs were bacterial pneumonia, cellulitis, dehydration, gastric and duodenal ulcer, hypoglycemia, hypokalemia, severe ENT (ear/nose/throat) infections, urinary tract infections (UTIs), asthma/chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), diabetes, hypertension, seizure disorder, influenza, and malnutrition. Individual condition rates as well as a total rate that combined all 15 conditions were estimated. In addition, we aggregated conditions into 3 indices as defined by McCall et al [1]: acute (bacterial pneumonia, cellulitis, dehydration, hypoglycemia, hypokalemia, gastric and duodenal ulcer, severe ENT infections, and UTIs), chronic (asthma/copd, CHF, diabetes, hypertension, and seizure disorder), and preventable (influenza and malnutrition). The ICD-9-CM codes used to identify ACSC hospitalizations are provided in Table 1. Sample Selection The study period extends from 1 January through 31 December Medicare HMO enrollees were eligible for the study if they met the following criteria: (1) age 65 by 1 January 2001, (2) did not have end-stage renal disease, (3) continuously eligible for both Medicare Part A and Part B, and (4) enrolled in the same California Medicare HMO for the entire study period or until death, whichever came first. Any beneficiaries who disenrolled to the traditional FFS Medicare plan, switched between HMOs, or moved during the study period were excluded. In 2001, there were 15 Medicare HMOs operating under risk contracts in California [25]. Enrollees of HMOs outside California as well as enrollees of plans with nonrisk contracts (eg, HMOs with cost contracts, demonstrations, health care prepayment plans) were excluded. Calculation of Hospitalization Rates Rates of hospitalization for ACSCs were calculated as the number of ACSC hospitalizations per 1000 Medicare HMO person-years. Person-years were used to adjust for partial year enrollment due to death. For example, 2 enrollees who both died on 1 July 2001 each contribute 6 months of enrollment or together 12 months of data, which yields 1 personyear. All hospitalization rates are unadjusted. Statistical significance of differences between groups was tested using analysis of variance, performed using SAS version 8.0 (SAS Institute, Inc, Cary, NC) [26]. Results More than 1.2 million Medicare beneficiaries were enrolled 560 JCOM November 2005 Vol. 12, No. 11
3 ORIGINAL RESEARCH Table 1. ICD-9-CM Diagnosis Codes for 15 Ambulatory Care Sensitive Conditions Condition Diagnosis Codes Asthma/chronic obstruc- 491xx, 492xx, 493xx, 494xx, 496xx tive pulmonary disease Bacterial pneumonia 481, 482xx, 483xx, 485, 486 Congestive heart failure 40201, 40211, 40291, 428xx, 5184 Cellulitis 681xx, 682xx, 683, 686xx Dehydration 2765 Diabetes mellitus 2500x, 2501x, 2502x, 2503x, 2508x, 2509x Gastric and duodenal 531xx, 532xx, 533xx ulcer Hypertension 401x, 40200, 40210, 40290, 403xx, 40400, 40402, 40410, 40412, 40490, 40492, 405xx, 4372 Hypoglycemia 2510, 2511, 2512 Hypokalemia 2768 Influenza 487x Malnutrition 260, 261, 262, 263x, 264x, 265x, 266x, 267, 268x, 269x Seizure disorder 3450x, 3451x, 3452x, 3453x, 3454x, 3455x, 3457x, 3458x, 3459x, 7803x Severe ear, nose, and 382xx, 462, 463, 465x, 4721 throat infection Urinary tract infection 590xx, 5990, 5950, 5970, 5978, 6010, 6012, 6013 during 2001 in the 15 California HMOs and included in the analysis (Table 2). Of these, more than half were in an IPA (independent practice association) and about a quarter were aged 80 years or older. About 5% were African American while 11% were classified in another or unknown racial category. Almost 6% were eligible for Medicaid (ie, dual eligible). These statistics mask vastly different characteristics across plans (Table 2), whose enrollments ranged from less than 2000 to over 450,000. Among the 15 plans, the youngest beneficiary group (65 69 years) made up 18% to 43% of total enrollment, whereas the oldest beneficiary group ( 85 years) accounted for 6% to 16%. Some plans enrolled relatively few African Americans whereas others had enrollments up to 25%. The percentage of Medicare enrollees also eligible for Medicaid varied from 3% to 16% among the 15 plans. The Medicare beneficiaries enrolled in California HMOs during 2001 experienced 315,503 hospitalizations (Table 3). Of these, 1 of every 5 hospitalizations was for an ACSC. With respect to beneficiary characteristics, the pattern of ACSC hospitalization rates closely followed the hospitalization rates for all conditions. ACSC hospitalization rates were higher among enrollees of IPAs (versus group model HMOs), males, and those eligible for Medicaid (P < 0.01). Table 2. Medicare HMO Enrollee Characteristics for All Plans Combined and Minimum and Maximum by Plan, California 2001 All Plans Combined By Plan Minimum Maximum Characteristic Number Percent Number Number Total beneficiaries 1,208, ,097 HMO model type IPA 703, ,097 Group 504, ,187 Minimum Maximum Age, yr Percent Percent , , , , , Gender Female 693, Male 515, Race African 65, American White 1,006, Other/unknown 136, Medicaid* Eligible 65, Not eligible 1,143, IPA = independent practice association. *Includes those eligible for Medicaid for at least 1 month during ACSC hospitalization rates increased markedly with age (more than 4 times as high among those 85 years compared with those years; P < 0.01). Those classified in the other or unknown race category experienced fewer ACSC hospitalizations than those in the white (27% more; P < 0.01) or African American categories (61% more; P < 0.01). Medicare beneficiaries were hospitalized infrequently for several of the ACSCs during 2001, resulting in a small number of hospitalizations for individual plans (Table 4). For 4 of the ACSC conditions (hypoglycemia, severe ENT infections, influenza, and malnutrition), 5 or more of the 15 plans had no hospitalizations during the study year. For another 4 conditions (hypokalemia, diabetes mellitus, hypertension, and seizure disorder), at least 1 plan did not have a hospitalization during For each of these 8 conditions, the median annual hospitalization rate for the 15 plans was less than 2 per 1000 person-years. For the remaining 7 more frequent Vol. 12, No. 11 November 2005 JCOM 561
4 Table 3. Medicare HMO Hospitalization Rates for All Conditions and 15 ACSCs, California 2001 All Conditions 15 ACSCs Characteristic No. of Hospitalizations Person-years No. of Hospitalizations Person-years Total person-years 315, , Total beneficiaries 315, , HMO model type IPA 204, , Group 111, , Age, yr , , , , , , , , , , Gender Female 167, , Male 147, , Race African American 18, White 268, ,178, Other/unknown 28, Medicaid* Eligible 28, Not eligible 286, ,872, Note: All comparisons are statistically significant at the 0.01 level based on analysis of variance. ACSC = ambulatory care sensitive conditions; IPA = independent practice association. *Includes those eligible for Medicaid for at least 1 month during ACSCs, large differences between the minimum and maximum rates illustrate the wide variation in ACSC hospitalization rates among plans. As shown in Table 5, correlations among the more common ACSC hospitalization rates and indices ranged from 0.53 (pneumonia and chronic ACSCs) to 0.99 (CHF and chronic ACSCs). The correlation between the acute and chronic ACSC indices was 0.82, while the correlation among the single most common conditions within the acute and chronic indices (bacterial pneumonia and CHF, respectively) was Discussion This study of beneficiaries in Medicare HMOs revealed large differences in patient sociodemographic characteristics across 15 plans in California. We also found that ACSC hospitalization rates varied substantially by patient characteristic and by plan. This wide variation in rates of ACSC hospitalization across plans is likely due, at least in part, to the differences in enrollee characteristics. Our results also indicate that estimating statistically reliable rates for the less frequently occurring 15 individual ACSCs may not be possible for plans with fewer enrollees (6 of 15 plans had less than 10,000 enrollees; data not shown). For these plans, estimating the rate for the 15 ACSCs combined or the acute and chronic ACSC indices may be necessary. Correlations between the individual ACSC rates and their corresponding indices also indicate that the indices are dominated by a few ACSCs (as was also demonstrated by the rates provided in Table 4). In addition, the high correlation between the acute and chronic indices (0.82) suggests a consistency in style of care across plans. The data also show that except for the very largest HMOs (ie, over 200,000 enrollees), there are few hospitalizations for the 2 preventable conditions (influenza and malnutrition). Seven and 5 of the 15 HMOs had zero hospitalizations during the study period for influenza and malnutrition, respectively. The median number of hospitalizations for these 2 conditions for all plans was 1. This suggests that for these 2 conditions, monitoring access and quality of care in plans may not be feasible. The pattern of ACSC hospitalization rates by patient characteristics in our results is consistent with previous studies [4,15 19]. In a study of Medicare FFS beneficiaries, Blustein et (continued on page 566) 562 JCOM November 2005 Vol. 12, No. 11
5 (continued from page 562) Table 4. Distribution of Number and Unadjusted Rate of ACSC Hospitalizations for 15 Medicare HMOs, California 2001 No. of Plans with Zero No. of ACSC Hospitalizations by Plan Rate per 1000 Person-years by Plan ACSC Hospitalizations Median Range Mean SD Median Range All 15 ACSCs , Acute ACSC , Bacterial pneumonia Cellulitis Dehydration Gastric and duodenal ulcer Hypoglycemia Hypokalemia Severe ENT infections UTI Chronic ACSC , Asthma/COPD CHF Diabetes mellitus Hypertension Seizure disorder Preventable ACSC Influenza Malnutrition ACSC= ambulatory care sensitive condition; CHF = congestive heart failure; COPD = chronic obstructive pulmonary disease; ENT = ear, nose, and throat; UTI = urinary tract infection. Table 5. Correlation Matrix of Unadjusted ACSC Hospitalization Rates for 15 Medicare HMOs, California 2001 Acute Chronic Asthma/ Bacterial All 15 ACSCs ACSCs ACSCs COPD CHF Pneumonia UTI All 15 ACSCs Acute ACSCs* Chronic ACSCs* Asthma/COPD CHF Bacterial pneumonia UTI 1.00 ACSC = ambulatory care sensitive condition; CHF = congestive heart failure; COPD = chronic obstructive pulmonary disease; UTI = urinary tract infection. *Acute ACSCs include bacterial pneumonia, cellulitis, dehydration, gastric and duodenal ulcer, hypoglycemia, hypokalemia, severe ear/ nose/throat infections and urinary tract infections. Chronic ACSCs include asthma/chronic obstructive pulmonary disease, congestive heart failure, diabetes mellitus, hypertension, and seizure disorder. al [16] demonstrated that sequentially adjusting ACSC hospitalization rates for several demographic and utilization variables can have a large effect on differences between rates for different socioeconomic subgroups (ie, race, education, and income). Based on a model using survey and Medicare claims data, Culler et al [17] identified age over 75; being black; in fair or poor health; having coronary heart disease, myocardial infarction, or diabetes; being limited in 2 or more activities of daily living; and living in a core standard metropolitan statistical area or rural county as associated with increased ACSC hospitalization. Using a similar model, Laditka [18] found ACSC hospitalization rates were higher among elderly 566 JCOM November 2005 Vol. 12, No. 11
6 ORIGINAL RESEARCH African American and Hispanic women than among non- Hispanic white women. Arecently published study [19] based on data collected as part of the Healthcare Cost and Utilization Project of the Agency for Healthcare Research and Quality indicates that rates of hospitalization for ACSCs have been changing over time and vary by geographic region, age, gender, income, and urban versus rural residence. Previous research has also shown large risk selection differences between the traditional Medicare FFS population and the Medicare HMO population [6 13]. Our study showed large variation in patient characteristics among the individual plans in California, indicating that risk differences exist not only between FFS and HMOs but also between individual plans in the HMO population. ACSC hospitalization rates are an attractive option for screening for potential access and quality of care problems in HMOs for several reasons. First, they can be generated with relative ease using readily available inpatient encounter data. Second, they are simple measures that are easy to understand and easy to explain. Third, hospitalization rates in general are familiar to clinicians and administrators and therefore will be more likely to be accepted and used. Finally, due to the cost and inherent risks associated with hospitalization, minimizing potentially avoidable hospitalizations is an important public health goal. Comparisons of unadjusted rates should not be used as a means to infer access or quality of care differences among plans. Instead, the monitoring of rates for an individual plan over time is the preferred method. For example, in the first year of estimation, a plan might compare their plan s ACSC hospitalization rates with published estimates. We found, similar to the results of others [1,17,19], that bacterial pneumonia and CHF tend to yield the highest hospitalization rates when compared with the other selected ACSCs. A change in a plan s ranking from 1 year to the next might indicate that a particular subgroup of enrollees needs further assessment (eg, patients with diabetes). Changes in rates of hospitalization for a single ACSC requires further investigation by plan administrators and providers to understand why the change occurred. It might reflect the effectiveness of a quality improvement program, the removal of certain services, or a change in enrollee sociodemographics. Several limitations of the study are worth mentioning. First, our results may not represent the experience of HMOs in other states. The sample is derived from 15 HMOs in California with large numbers of Medicare enrollees. These HMOs may have more experience in the delivery of coordinated, integrated ambulatory care and provision of preventive care than HMOs in states with lower Medicare HMO penetration. Second, the ACSC hospitalization rates have not been adjusted for demographic characteristics (ie, age, gender, or race), or socioeconomic characteristics (eg, education, income), all of which influence ACSC hospitalization rates. Finally, our 2001 data represents a period of time when Medicare managed care enrollment was starting to experience plan withdrawals and disenrollment rather than expansion in California [27] and the rest of the country [28]. Although this trend has continued since 2001 [28], our results may not be generalizable to Medicare HMOs today for this and other reasons. Further research related to adjusting ACSC hospitalization rates is needed to better understand how much of the difference in unadjusted rates among plans is due to patient characteristics and how much reflects an underlying difference in access and quality of care. In addition, studying how changes in care or enrollee characteristics affect ACSC hospitalization rates within the same plan over time will improve our understanding of how ACSC hospitalization rates relate to access and quality of care. Acknowledgments: The authors are grateful to the Agency for Healthcare Research and Quality; Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services; and The Robert Wood Johnson Foundation for support of this work. They thank Robert Reddick for the computer programming of the original data files and Beate Danielson of Health Information Solutions for linking the data files. They are also grateful to the Centers for Medicare and Medicaid Services and the California Office of Statewide Health Planning and Development for providing the data that made this study possible. Corresponding author: Feng Zeng, PhD, School of Policy, Planning and Development, University of Southern California, Los Angeles, CA Funding/support: Grant #R01HS10256 from the Agency for Healthcare Research and Quality; Office of the Assistant Secretary for Planning and Evaluation, U.S. Dept. of Health and Human Services; and grant #41289 from the Robert Wood Johnson Foundation, Princeton, NJ. Financial disclosures: None. Author contributions: conception and design, FZ, JFO, EMS, ND, GM; analysis and interpretation of data, FZ, JFO, EMS; drafting of the article, FZ, JFO, EMS; statistical expertise, FZ; obtaining of funding, ND, GM; administrative support, JFO. References 1. McCall N, Harlow J, Dayhoff D. Rates of hospitalization for ambulatory care sensitive conditions in the Medicare+Choice population. Health Care Financ Rev 2001;22: Millman M, editor. Access to health care in America. Committee on Monitoring Access to Personal Health Care Services, Institute of Medicine. Washington (DC): National Academy Press; Billings J, Zeitel L, Lukomnik J, et al. Impact of socioeconomic Vol. 12, No. 11 November 2005 JCOM 567
7 status on hospital use in New York City. Health Aff (Millwood) 1993;12: Bindman AB, Grumbach K, Osmond D, et al. Preventable hospitalizations and access to health care. JAMA 1995;274: Garg A, Probst JC, Sease T, Samuels ME. Potentially preventable care: ambulatory care-sensitive pediatric hospitalizations in South Carolina in South Med J 2003;96: Riley G, Lubitz J, Rabey E. Enrollee health status under Medicare risk contracts: an analysis of mortality rates. Health Serv Res 1991;26: Miller RH, Luft HS. Managed care plan performance since A literature analysis. JAMA 1994;271: Riley G, Tudor C, Chiang YP, Ingber M. Health status of Medicare enrollees in HMOs and fee-for-service in Health Care Financ Rev 1996;17: Cox DF, Hogan C. Biased selection and Medicare HMOs: analysis of the experience. Med Care Res Rev 1997;54: Morgan RO, Virnig BA, DeVito CA, Persily NA. The Medicare- HMO revolving door the healthy go in and the sick go out. N Engl J Med 1997;337: Hamilton BH. HMO selection and Medicare costs: Bayesian MCMC estimation of a robust panel data tobit model with survival. Health Econ 1999;8: Call KT, Dowd B, Feldman R, Maciejewski M. Selection experiences in Medicare HMOs: pre-enrollment expenditures. Health Care Financ Rev 1999;20: Dhanani N, O Leary JF, Keeler E, et al. The effect of HMOs on the inpatient utilization of medicare beneficiaries. Health Serv Res 2004;39: Revere L, Large J, Langland-Orban B. A comparison of inpatient severity, average length of stay, and cost for traditional fee-for-service medicare and medicare HMOs in Florida. Health Care Manage Rev 2004;29: Billings J, Anderson GM, Newman LS. Recent findings on preventable hospitalizations. Health Aff (Millwood) 1996;15: Blustein J, Hanson K, Shea S. Preventable hospitalizations and socioeconomic status. Health Aff (Millwood) 1998;17: Culler SD, Parchman ML, Przybylski M. Factors related to potentially preventable hospitalizations among the elderly. Med Care 1998;36: Laditka JN. Hazards of hospitalization for ambulatory care sensitive conditions among older women: evidence of greater risks for African Americans and Hispanics. Med Care Res Rev 2003;60: Kruzikas DT, Jiang HJ, Remus D, et al. Preventable hospitalizations: window into primary and preventive care, 2000: HCUP Fact Book No. 5. Rockville (MD): Department of Health and Human Services, Agency for Healthcare Research and Quality; AHRQ Publication No Kaiser Family Foundation. State health facts online. Undated. Menlo Park, California. Henry J. Kaiser Family Foundation. Available at Accessed 13 Sep Fellegi IP. Section II: overview of applications and introduction to theory. Tutorial on the Fellegi-Sunter model for record linkage. In: Kliss B, Alvey W, editors. Record linkage techniques. 1985: Proceedings of the Workshop on Exact Matching Methodologies; 1985 May 9 10; Arlington, VA. Washington (DC): Dept. of the Treasury, Internal Revenue Service; 1985: Fellegi IP, Sunter A. A theory for record linkage. JASA 1969; 64: Newcombe HB. Handbook of record linkage: methods for health and statistical studies, administration, and business. New York: Oxford University Press; Jaro MA. Advances in record-linkage methodology as applied to matching the 1985 Census of Tampa, Florida. JASA 1989; 84: Centers for Medicare and Medicaid Services. Monthly report Medicare coordinated care health plans. Available at Accessed 26 Oct SAS Institute. SAS procedure guide, version 8.0. Cary (NC): SAS Institute; Gold M, Lake T. Medicare+Choice in California: lessons and insights. Kaiser Family Foundation. Available at medicare/6047-index.cfm. Accessed 26 Oct Kaiser Family Foundation. Medicare advantage fact sheet. Available at Accessed 26 Oct Copyright 2005 by Turner White Communications Inc., Wayne, PA. All rights reserved. 568 JCOM November 2005 Vol. 12, No. 11
Potentially Avoidable Hospitalizations in Tennessee, Final Report. May 2006
The Methodist LeBonheur Center for Healthcare Economics 312 Fogelman College of Business & Economics Memphis, Tennessee 38152-3120 Office: 901.678.3565 Fax: 901.678.2865 Potentially Avoidable Hospitalizations
Ambulatory-care-sensitive admission rates: A key metric in evaluating health plan medicalmanagement effectiveness
Milliman Prepared by: Kathryn Fitch, RN, MEd Principal, Healthcare Management Consultant Kosuke Iwasaki, FIAJ, MAAA Consulting Actuary Ambulatory-care-sensitive admission rates: A key metric in evaluating
Executive Summary MEDICARE FEE-FOR-SERVICE (FFS) HOSPITAL READMISSIONS: QUARTER 4 (Q4) 2012 Q STATE OF CALIFORNIA
MEDICARE FEE-FOR-SERVICE (FFS) HOSPITAL READMISSIONS: QUARTER 4 (Q4) 2012 Q3 2013 Executive Summary STATE OF CALIFORNIA The Centers for Medicare & Medicaid Services (CMS) has tasked Health Services Advisory
Potentially Avoidable Hospitalizations among Dual Eligible Beneficiaries in Medicaid Home and Community-Based Services Waivers
Potentially Avoidable Hospitalizations among Dual Eligible Beneficiaries in Medicaid Home and Community-Based Services Waivers Edith G Walsh, PhD Joshua Wiener, PhD Marc Freiman, PhD Susan Haber, PhD Arnold
Chapter VII. Health Data Warehouse
Broward County Health Plan Chapter VII Health Data Warehouse CHAPTER VII: THE HEALTH DATA WAREHOUSE Table of Contents INTRODUCTION... 3 ICD-9-CM to ICD-10-CM TRANSITION... 3 PREVENTION QUALITY INDICATORS...
Center for State Health Policy
Center for State Health Policy A Unit of the Institute for Health, Health Care Policy and Aging Research Opportunities for Better Care and Lower Cost: Data Book on Hospital Utilization and Cost in Camden
30-day Hospital Readmissions in Washington State
30-day Hospital Readmissions in Washington State May 28, 2015 Seattle Readmissions Summit 2015 The Alliance: Who We Are Multi-stakeholder. More than 185 member organizations representing purchasers, plans,
Health Indicators. for the Dallas/Fort Worth Combined Metropolitan Statistical Area Brad Walsh and Sue Pickens Owens
Health Indicators Our Community Health for the Dallas/ Fort Worth Combined Metropolitan Statistical Area Checkup 2007 for the Dallas/Fort Worth Combined Metropolitan Statistical Area Brad Walsh and Sue
HIDD 101 HOSPITAL INPATIENT AND DISCHARGE DATA IN NEW MEXICO
HIDD 101 HOSPITAL INPATIENT AND DISCHARGE DATA IN NEW MEXICO Health Information System Act (24-14A-1, et seq. NMSA 1978) Provides authority for the Department of Health to collect health data. NMDOH had
DAHL: Demographic Assessment for Health Literacy. Amresh Hanchate, PhD Research Assistant Professor Boston University School of Medicine
DAHL: Demographic Assessment for Health Literacy Amresh Hanchate, PhD Research Assistant Professor Boston University School of Medicine Source The Demographic Assessment for Health Literacy (DAHL): A New
Medicare Spending and Rehospitalization for Chronically Ill Medicare Beneficiaries: Home Health Use Compared to Other Post-Acute Care Settings
Medicare Spending and Rehospitalization for Chronically Ill Medicare Beneficiaries: Home Health Use Compared to Other Post-Acute Care Settings Executive Summary The Alliance for Home Health Quality and
Selected Measures United States, 2011
Disparities in Nursing Home Quality Selected Measures United States, 2011 Disparities National Coordinating Center Spring 2014 This material was prepared by the Delmarva Foundation for Medical Care (DFMC)
Reducing Readmissions: Potential Measurements
Reducing Readmissions: Potential Measurements Avoid Readmissions Through Collaboration October 27, 2010 Denise Remus, PhD, RN Chief Quality Officer BayCare Health System Overview Why Focus on Readmissions?
Medicare Spending and Rehospitalization for Chronically Ill Medicare Beneficiaries: Home Health Use Compared to Other Post-Acute Care Settings
Medicare Spending and Rehospitalization for Chronically Ill Medicare Beneficiaries: Home Health Use Compared to Other Post-Acute Care Settings May 11, 2009 Avalere Health LLC Avalere Health LLC The intersection
I t is generally recognised that hospitalisations for undesirable
PUBLIC HEALTH POLICY AND PRACTICE Avoidable hospitalisation rates in Singapore, 1991 1998: assessing trends and inequities of quality in primary care M Niti, TPNg... See end of article for authors affiliations...
HEDIS Ad-Hoc Public Comment: Table of Contents
HEDIS 1 2018 Ad-Hoc Public Comment: Table of Contents HEDIS Overview... 1 The HEDIS Measure Development Process... Synopsis... Submitting Comments... NCQA Review of Public Comments... Value Set Directory...
Community Performance Report
: Wenatchee Current Year: Q1 217 through Q4 217 Qualis Health Communities for Safer Transitions of Care Performance Report : Wenatchee Includes Data Through: Q4 217 Report Created: May 3, 218 Purpose of
Updated validation of AHRQ Prevention Quality Indicators in the USA
Updated validation of AHRQ Prevention Quality Indicators in the USA Patrick S. Romano, MD MPH UC Davis Center for Healthcare Policy and Research Organization for Economic Cooperation and Development October
The Role of Public Clinics in. Vulnerable Populations. Preventable Hospitalizations Among. AndrewJ. Epstein
The Role of Public Clinics in Preventable Hospitalizations Among Vulnerable Populations AndrewJ. Epstein Objective. To determine if the availability of public ambulatory clinics affects preventable hospitalization
2014 MASTER PROJECT LIST
Promoting Integrated Care for Dual Eligibles (PRIDE) This project addressed a set of organizational challenges that high performing plans must resolve in order to scale up to serve larger numbers of dual
MEDICARE ENROLLMENT, HEALTH STATUS, SERVICE USE AND PAYMENT DATA FOR AMERICAN INDIANS & ALASKA NATIVES
American Indian & Alaska Native Data Project of the Centers for Medicare and Medicaid Services Tribal Technical Advisory Group MEDICARE ENROLLMENT, HEALTH STATUS, SERVICE USE AND PAYMENT DATA FOR AMERICAN
SAMHSA Primary and Behavioral Health Care Integration (PBHCI) Program Grantees: Part 2
SAMHSA Primary and Behavioral Health Care Integration (PBHCI) Program Grantees: Part 2 Ken Bachrach, Ph.D., Clinical Director Jim Sorg, Ph.D., Director of Care Integration and IT Tarzana Treatment Centers
Comparison of Care in Hospital Outpatient Departments and Physician Offices
Comparison of Care in Hospital Outpatient Departments and Physician Offices Final Report Prepared for: American Hospital Association February 2015 Berna Demiralp, PhD Delia Belausteguigoitia Qian Zhang,
Hospital Discharge Data, 2005 From The University of Memphis Methodist Le Bonheur Center for Healthcare Economics
Hospital Discharge Data, 2005 From The University of Memphis Methodist Le Bonheur Center for Healthcare Economics August 22, 2008 Potentially Avoidable Pediatric Hospitalizations in Tennessee, 2005 Cyril
Research Design: Other Examples. Lynda Burton, ScD Johns Hopkins University
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this
Newly Medicare-Eligible Disabled Data Points # 11
Newly Medicare-eligible disabled: comparison of duals and nonduals Newly Medicare-Eligible Disabled Data Points # 11 In 1972, Congress expanded the Medicare program to provide health care benefits for
Nebraska Final Report for. State-based Cardiovascular Disease Surveillance Data Pilot Project
Nebraska Final Report for State-based Cardiovascular Disease Surveillance Data Pilot Project Principle Investigators: Ming Qu, PhD Public Health Support Unit Administrator Nebraska Department of Health
Policy Brief October 2014
Policy Brief October 2014 Does ity Affect Observation Care Services Use in CAHs for Medicare Beneficiaries? Yvonne Jonk, PhD; Heidi O Connor, MS; Walter Gregg, MA, MPH Key Findings Medicare claims data
South Carolina Rural Health Research Center. Findings Brief April, 2018
South Carolina Health Research Center Findings Brief April, 2018 Kevin J. Bennett, PhD Karen M. Jones, MSPH Janice C. Probst, PhD. Health Care Utilization Patterns of Medicaid Recipients, 2012, 35 States
July Avalere Health T avalere.com An Inovalon Company F Connecticut Ave, NW Washington, DC 20036
Medicare Advantage Achieves Cost-Effective Care and Better Outcomes for with Chronic Conditions Relative to Fee-for-Service Medicare EMBARGOED FOR RELEASE UNTIL WEDNESDAY, JULY 11 AT 12AM July 2018 Avalere
June 25, Shamis Mohamoud, David Idala, Parker James, Laura Humber. AcademyHealth Annual Research Meeting
Evaluation of the Maryland Health Home Program for Medicaid Enrollees with Severe Mental Illnesses or Opioid Substance Use Disorder and Risk of Additional Chronic Conditions June 25, 2018 Shamis Mohamoud,
Factors that Impact Readmission for Medicare and Medicaid HMO Inpatients
The College at Brockport: State University of New York Digital Commons @Brockport Senior Honors Theses Master's Theses and Honors Projects 5-2014 Factors that Impact Readmission for Medicare and Medicaid
The Florida KidCare Evaluation: Statistical Analyses
The Florida KidCare Evaluation: Statistical Analyses Betsy Shenkman, PhD Jana Col, MA Heather Steingraber Christine Bono Purpose To build from the descriptive reports of past three state and federal fiscal
Balanced Scorecards & Population Health
Balanced Scorecards & Population Health Presentation Outline of Work In Progress 1. Collaborators & Funding 2. Initial Four Questions & Underlying Assumption 3. Initial Findings 4. IOM Vision for American
Medicaid HCBS/FE Home Telehealth Pilot Final Report for Study Years 1-3 (September 2007 June 2010)
Medicaid HCBS/FE Home Telehealth Pilot Final Report for Study Years 1-3 (September 2007 June 2010) Completed November 30, 2010 Ryan Spaulding, PhD Director Gordon Alloway Research Associate Center for
Section 2703: State Option to Provide Health Homes for Enrollees with Chronic Conditions
Section 2703: State Option to Provide Health Homes for Enrollees with Chronic Conditions Center for Medicaid, CHIP, and Survey & Certification Centers for Medicare & Medicaid Services Background. A goal
CER Module ACCESS TO CARE January 14, AM 12:30 PM
CER Module ACCESS TO CARE January 14, 2014. 830 AM 12:30 PM Topics 1. Definition, Model & equity of Access Ron Andersen (8:30 10:30) 2. Effectiveness, Efficiency & future of Access Martin Shapiro (10:30
IN EFFORTS to control costs, many. Pediatric Length of Stay Guidelines and Routine Practice. The Case of Milliman and Robertson ARTICLE
Pediatric Length of Stay Guidelines and Routine Practice The Case of Milliman and Robertson Jeffrey S. Harman, PhD; Kelly J. Kelleher, MD, MPH ARTICLE Background: Guidelines for inpatient length of stay
Medicare Fee-For-Service (FFS) Hospital Readmissions: Q Q2 2014
Medicare Fee-For-Service (FFS) Hospital Readmissions: Q3 2013 Q2 2014 State of Florida Data Dictionary Provided on Page A Please contact Peggy Loesch via email at Peggy.Loesch@HCQIS.org or by phone at
Hospitalizations for Ambulatory Care Sensitive Conditions (ACSC)
Hospitalizations for Ambulatory Care Sensitive Conditions (ACSC) Resource for Indicator Standards (RIS) Health Analytics Branch, Ministry of Health and Long-Term Care Indicator description RIS indicator
Findings Brief. NC Rural Health Research Program
Safety Net Clinics Serving the Elderly in Rural Areas: Rural Health Clinic Patients Compared to Federally Qualified Health Center Patients BACKGROUND Andrea D. Radford, DrPH; Victoria A. Freeman, RN, DrPH;
STATISTICAL BRIEF #9. Hospitalizations among Males, Highlights. Introduction. Findings. June 2006
HEALTHCARE COST AND UTILIZATION PROJECT STATISTICAL BRIEF #9 Agency for Healthcare Research and Quality June 2006 Hospitalizations among Males, 2003 C. Allison Russo, M.P.H. and Anne Elixhauser, Ph.D.
DATA Briefing. Emergency hospital admissions for ambulatory care-sensitive conditions: identifying the potential for reductions.
DATA Briefing April 2012 Emergency hospital admissions for ambulatory care-sensitive conditions: identifying the potential for reductions Authors Yang Tian Anna Dixon Haiyan Gao Summary Ambulatory care-sensitive
medicaid commission on a n d t h e uninsured May 2009 Community Care of North Carolina: Putting Health Reform Ideas into Practice in Medicaid SUMMARY
kaiser commission on medicaid SUMMARY a n d t h e uninsured Community Care of North Carolina: Putting Health Reform Ideas into Practice in Medicaid Why is Community Care of North Carolina (CCNC) of Interest?
A Miracle of Modern Medicine. What medical discovery touches everyone in the United States?
Primary Care: A Miracle of Modern Medicine What medical discovery touches everyone in the United States? What medical breakthrough is proven to reduce the galloping growth of health care spending? What
Dual-eligible SNPs should complete and submit Attachment A and, if serving beneficiaries with end-stage renal disease (ESRD), Attachment D.
Attachment A: Model of Care for Dual-eligible SNPs MA Contract Name: Geisinger Health Plan MA Contract Number: H3954-097 Type of Dual-eligible SNP: Full The model of care describes the MAO's approach to
William B. Saunders, PhD, MPH Program Director, Health Informatics PSM & Certificate Programs. Laura J. Dunlap, RN
William B. Saunders, PhD, MPH Program Director, Health Informatics PSM & Certificate Programs Laura J. Dunlap, RN Background Research Questions Methods Results for North Carolina Results for Specific Counties
Using Secondary Datasets for Research. Learning Objectives. What Do We Mean By Secondary Data?
Using Secondary Datasets for Research José J. Escarce January 26, 2015 Learning Objectives Understand what secondary datasets are and why they are useful for health services research Become familiar with
Using An APCD to Inform Healthcare Policy, Strategy, and Consumer Choice. Maine s Experience
Using An APCD to Inform Healthcare Policy, Strategy, and Consumer Choice Maine s Experience What I ll Cover Today Maine s History of Using Health Care Data for Policy and System Change Health Data Agency
Health and Long-Term Care Use Patterns for Ohio s Dual Eligible Population Experiencing Chronic Disability
Health and Long-Term Care Use Patterns for Ohio s Dual Eligible Population Experiencing Chronic Disability Shahla A. Mehdizadeh, Ph.D. 1 Robert A. Applebaum, Ph.D. 2 Gregg Warshaw, M.D. 3 Jane K. Straker,
Hot Spotter Report User Guide
PATIENT-CENTERED CARE Hot Spotter Report User Guide Overview The Hot Spotter Report is designed to give providers and care team members a heads up when their attributed patients appear to be at risk for
2017 Quality Reporting: Claims and Administrative Data-Based Quality Measures For Medicare Shared Savings Program and Next Generation ACO Model ACOs
2017 Quality Reporting: Claims and Administrative Data-Based Quality Measures For Medicare Shared Savings Program and Next Generation ACO Model ACOs June 15, 2017 Rabia Khan, MPH, CMS Chris Beadles, MD,
WilCo Wellness Alliance. Summit Presentation. Cara Woodard Account Manager. April 25, 2017
WilCo Wellness Alliance Summit Presentation Cara Woodard Account Manager April 25, 2017 Background and Overview Healthy Communities Institute Mission Headquarters Improve the health, vitality and environmental
Variation in pediatric gastroenteritis admissions among Florida counties,
University of South Florida Scholar Commons Graduate Theses and Dissertations Graduate School 2006 Variation in pediatric gastroenteritis admissions among Florida counties, 1995-2002 Jean Lee University
Record Linkages in Project Talent
Record Linkages in Project Talent Copyright 2011 American Institutes for Research All rights reserved. Kelly Peters Principal Psychometrician June 5, 2017 Agenda Project Talent History and Objectives Enhancing
2016 Edition. Upper Payment Limits and Medicaid Capitation Rates for Programs of All-Inclusive Care for the Elderly (PACE )
2016 Edition Upper Payment Limits and Medicaid Capitation Rates for Programs of All-Inclusive Care for the Elderly (PACE ) R ABSTRACT The Program of All-Inclusive Care for the Elderly (PACE ) is a federal
Medicare. Costs and Financing of Medicare Enrollees Living with HIV/AIDS in California by June Eichner and James G. Kahn
August 2001 No. 8 Medicare Brief Costs and Financing of Medicare Enrollees Living with HIV/AIDS in California by June Eichner and James G. Kahn Summary Because Medicare does not cover a large part of the
Baseline and 9-Month Follow-Up Outcomes of Health Care for Iowa Medicaid Health Home Program Enrollees
Health Policy 11-1-2013 Baseline and 9-Month Follow-Up Outcomes of Health Care for Iowa Medicaid Health Home Program Enrollees Elizabeth T. Momany University of Iowa Peter C. Damiano University of Iowa
2012 Community Health Needs Assessment
2012 Community Health Needs Assessment University Hospitals (UH) long-standing commitment to the community spans more than 145 years. This commitment has grown and evolved through significant thought and
Same Disease, Different Care: How Patient Health Coverage Drives Treatment Patterns in California. The analysis includes:
Same Disease, Different Care: How Patient Health Coverage Drives Treatment Patterns in California C A L I FOR N I A HEALTHCARE FOUNDATION Introduction As shown in The 2005 Dartmouth Atlas of Health Care,
CALIFORNIA HEALTHCARE FOUNDATION. Medi-Cal Versus Employer- Based Coverage: Comparing Access to Care JULY 2015 (REVISED JANUARY 2016)
CALIFORNIA HEALTHCARE FOUNDATION Medi-Cal Versus Employer- Based Coverage: Comparing Access to Care JULY 2015 (REVISED JANUARY 2016) Contents About the Authors Tara Becker, PhD, is a statistician at the
Understanding Risk Adjustment in Medicare Advantage
Understanding Risk Adjustment in Medicare Advantage ISSUE BRIEF JUNE 2017 Risk adjustment is an essential mechanism used in health insurance programs to account for the overall health and expected medical
Statistical Analysis Plan
Statistical Analysis Plan CDMP quantitative evaluation 1 Data sources 1.1 The Chronic Disease Management Program Minimum Data Set The analysis will include every participant recorded in the program minimum
From Risk Scores to Impactability Scores:
From Risk Scores to Impactability Scores: Innovations in Care Management Carlos T. Jackson, Ph.D. September 14, 2015 Outline Population Health What is Impactability? Complex Care Management Transitional
Chronic Disease Surveillance and Office of Surveillance, Evaluation, and Research
Chronic Disease Surveillance and Office of Surveillance, Evaluation, and Research Potentially Preventable Hospitalizations Program 2015 Annual Meeting Nimisha Bhakta, MPH September 29, 2015 Presentation
Hospital Readmissions
Article Title Hospital Readmissions Published By Pramit Sengupta, Georgia Institute of Technology Hospital Readmissions Overview of Hospital Readmission A readmission is defined as a hospitalization that
The Impact of Healthcare-associated Infections in Pennsylvania 2010
The Impact Healthcare-associated Infections in Pennsylvania 2010 Pennsylvania Health Care Cost Containment Council February 2012 About PHC4 The Pennsylvania Health Care Cost Containment Council (PHC4)
Understanding Readmissions after Cancer Surgery in Vulnerable Hospitals
Understanding Readmissions after Cancer Surgery in Vulnerable Hospitals Waddah B. Al-Refaie, MD, FACS John S. Dillon and Chief of Surgical Oncology MedStar Georgetown University Hospital Lombardi Comprehensive
National Hospice and Palliative Care OrganizatioN. Facts AND Figures. Hospice Care in America. NHPCO Facts & Figures edition
National Hospice and Palliative Care OrganizatioN Facts AND Figures Hospice Care in America 2017 Edition NHPCO Facts & Figures - 2017 edition Table of Contents 2 Introduction 2 About this report 2 What
STEUBEN COUNTY HEALTH PROFILE. Finger Lakes Health Systems Agency, 2017
STEUBEN COUNTY HEALTH PROFILE Finger Lakes Health Systems Agency, 2017 About the Report The purpose of this report is to provide a summary of health data specific to Steuben County. Where possible, benchmarks
Paying for Outcomes not Performance
Paying for Outcomes not Performance 1 3M. All Rights Reserved. Norbert Goldfield, M.D. Medical Director 3M Health Information Systems, Inc. #Health Information Systems- Clinical Research Group Created
Rates of Emergency Department Use for Ambulatory Sensitive Conditions in the Los Angeles Healthy Kids Program
Rates of Emergency Department Use for Ambulatory Sensitive Conditions in the Los Angeles Healthy Kids Program Prepared for: Prepared By Moira Inkelas and Patricia Barreto The University of California at
Dual Eligibles: Medicaid s Role in Filling Medicare s Gaps
I S S U E P A P E R kaiser commission on medicaid and the uninsured March 2004 Dual Eligibles: Medicaid s Role in Filling Medicare s Gaps In 2000, over 7 million people were dual eligibles, low-income
Use of Health Information Technology to Reduce Health Risk
Use of Health Information Technology to Reduce Health Risk Sandra M. Foote Senior Advisor, Chronic Care Improvement Centers for Medicare & Medicaid Services September 9, 2005 The MHS Challenge Develop
1. Measures within the program measure set are NQF-endorsed or meet the requirements for expedited review
MAP Working Measure Selection Criteria 1. Measures within the program measure set are NQF-endorsed or meet the requirements for expedited review Measures within the program measure set are NQF-endorsed,
Rural-Relevant Quality Measures for Critical Access Hospitals
Rural-Relevant Quality Measures for Critical Access Hospitals Ira Moscovice PhD Michelle Casey MS University of Minnesota Rural Health Research Center Minnesota Rural Health Conference Duluth, Minnesota
Racial disparities in ED triage assessments and wait times
Racial disparities in ED triage assessments and wait times Jordan Bleth, James Beal PhD, Abe Sahmoun PhD June 2, 2017 Outline Background Purpose Methods Results Discussion Limitations Future areas of study
Linkage between the Israeli Defense Forces Primary Care Physician Demographics and Usage of Secondary Medical Services and Laboratory Tests
MILITARY MEDICINE, 170, 10:836, 2005 Linkage between the Israeli Defense Forces Primary Care Physician Demographics and Usage of Secondary Medical Services and Laboratory Tests Guarantor: LTC Ilan Levy,
Total Cost of Care Technical Appendix April 2015
Total Cost of Care Technical Appendix April 2015 This technical appendix supplements the Spring 2015 adult and pediatric Clinic Comparison Reports released by the Oregon Health Care Quality Corporation
MONROE COUNTY HEALTH PROFILE. Finger Lakes Health Systems Agency, 2017
MONROE COUNTY HEALTH PROFILE Finger Lakes Health Systems Agency, 2017 About the Report The purpose of this report is to provide a summary of health data specific to Monroe County. Where possible, benchmarks
OptumRx: Measuring the financial advantage
OptumRx: Measuring the financial advantage New study shows $11-16 PMPM medical savings when Optum care management and Optum pharmacy are provided together with medical benefits. Page 1 Synopsis Optum recently
AHRQ Quality Indicators. Maryland Health Services Cost Review Commission October 21, 2005 Marybeth Farquhar, AHRQ
AHRQ Quality Indicators Maryland Health Services Cost Review Commission October 21, 2005 Marybeth Farquhar, AHRQ Overview AHRQ Quality Indicators Current Uses of the Quality Indicators Case Studies of
Progress in closing the gap in British Columbia
Progress in closing the gap in British Columbia 1 Using evidence to identify the need for investments in primary healthcare services on BC First Nations reserves Josée Lavoie (UofM), Amanda Ward (FNHA),
Risk Adjusted Diagnosis Coding:
Risk Adjusted Diagnosis Coding: Reporting ChronicDisease for Population Health Management Jeri Leong, R.N., CPC, CPC-H, CPMA, CPC-I Executive Director 1 Learning Objectives Explain the concept Medicare
Supplementary Online Content
Supplementary Online Content Kaukonen KM, Bailey M, Suzuki S, Pilcher D, Bellomo R. Mortality related to severe sepsis and septic shock among critically ill patients in Australia and New Zealand, 2000-2012.
FOOD INSECURITY, FOOD BANKS, & HEALTH CARE: A JOURNEY HILARY SELIGMAN MD MAS
FOOD INSECURITY, FOOD BANKS, & HEALTH CARE: A JOURNEY HILARY SELIGMAN MD MAS Triple Aim of Health Care Lower Costs Triple Aim Better care for the whole population at the lowest cost Improve Patient Care
Introduction to the Malnutrition Quality Improvement Initiative (MQii)
Introduction to the Malnutrition Quality Improvement Initiative (MQii) 1 Overview The Case for Malnutrition Quality Improvement Background on the Malnutrition Quality Improvement Initiative (MQii) The
3M Health Information Systems. 3M Clinical Risk Groups: Measuring risk, managing care
3M Health Information Systems 3M Clinical Risk Groups: Measuring risk, managing care 3M Clinical Risk Groups: Measuring risk, managing care Overview The 3M Clinical Risk Groups (CRGs) are a population
LIVINGSTON COUNTY HEALTH PROFILE. Finger Lakes Health Systems Agency, 2017
LIVINGSTON COUNTY HEALTH PROFILE Finger Lakes Health Systems Agency, 2017 About the Report The purpose of this report is to provide a summary of health data specific to Livingston County. Where possible,
Joseph W. Thompson, MD, MPH Arkansas Surgeon General Director, Arkansas Center for Health Improvement
Joseph W. Thompson, MD, MPH Arkansas Surgeon General Director, Arkansas Center for Health Improvement Arkansas Health System Improvement Workforce Payment System Health Information Technology Insurance
From SAS Programming with Medicare Administrative Data. Full book available for purchase here.
From SAS Programming with Medicare Administrative Data. Full book available for purchase here. Contents About This Book... ix About The Author... xiii Acknowledgments...xv Chapter 1: Introduction... 1
Managing Patients with Multiple Chronic Conditions
Best Practices Managing Patients with Multiple Chronic Conditions Dartmouth-Hitchcock Physicians Case Study Organization Profile Headquartered in Bedford, New Hampshire, Dartmouth-Hitchcock is a large
Papers. Hospital bed utilisation in the NHS, Kaiser Permanente, and the US Medicare programme: analysis of routine data. Abstract.
Hospital bed utilisation in the NHS, Kaiser Permanente, and the US Medicare programme: analysis of routine data Chris Ham, Nick York, Steve Sutch, Rob Shaw Abstract Objective To compare the utilisation
CHEMUNG COUNTY HEALTH PROFILE. Finger Lakes Health Systems Agency, 2017
CHEMUNG COUNTY HEALTH PROFILE Finger Lakes Health Systems Agency, 2017 About the Report The purpose of this report is to provide a summary of health data specific to Chemung County. Where possible, benchmarks
The Future of Post-Acute Care Under Value-Based Payment
The Future of Post-Acute Care Under Value-Based Payment Robert Mechanic, MBA Brandeis University Northeast Home Health Leadership Summit January 22, 2015 Medicare Margins for Freestanding Home Health Agencies
Very large per-capita Medicaid population.
MEDICAID INSTITUTE AT UNITED HOSPITAL FUND Medicaid Regional Data Compendium, 214 Chartbook 2: New York City This chartbook is part of a broader data compendium from the Medicaid Institute at United Hospital
Outcomes for Iowa Medicaid Chronic Condition Health Home Program Enrollees. Policy Report. SFYs February 2017
Policy Report February 2017 Outcomes for Iowa Medicaid Chronic Condition Health Home Program Enrollees Ss 2012-2015 Elizabeth Momany Assistant Director, Health Policy Research Program* Associate Research
For more than 3 decades, public policies have
Health Insurance, Primary Care, and Preventable Hospitalization of Children in a Large State Bernard Friedman, PhD; and Jayasree Basu, PhD Objectives: To analyze variations in the admission rate to hospitals
A PRELIMINARY CASE MIX MODEL FOR ADULT PROTECTIVE SERVICES CLIENTS IN MAINE
A PRELIMINARY CASE MIX MODEL FOR ADULT PROTECTIVE SERVICES CLIENTS IN MAINE A PRELIMINARY CASE MIX MODEL FOR ADULT PROTECTIVE SERVICES CLIENTS IN MAINE Prepared by: Kimberly Mooney Murray and Elise Bolda
Preventable Readmissions
Preventable Readmissions Strategy to reduce readmissions and increase quality needs to have the following elements A tool to identify preventable readmissions Payment incentives Public reporting Quality