The Paradox of Readmission Prevention Interventions: Missing Those Most in Need.

BACKGROUND: Post-hospitalization transition interventions remain a priority in preventing rehospitalization. However, not all patients referred for readmission prevention interventions receive them. We sought to 1) define patient characteristics associated with non-receipt of readmission prevention interventions (among those eligible for them), and 2) determine whether these same patient characteristics are associated with hospital readmission at the state level. METHODS: We used state-wide data from the Maryland Health Services Cost Review Commission to determine patient-level factors associated with state-wide readmissions. Concurrently, we conducted a retrospective analysis of discharged patients referred to receive 1 of 3 post-discharge interventions between January 2013 and July 2019-a nurse transition guide, post-discharge phone call, or follow-up appointment in our post-discharge clinic-to determine patient-level factors associated with not receiving the intervention. Multivariable generalized estimating equation logistic regression models were used to calculate the odds of not accepting or not receiving the interventions. RESULTS: Older age, male gender, black race, higher expected readmission rate, and lower socioeconomic status were significantly associated with 30-day readmission in hospitalized Maryland patients. Most of these variables (age, sex, race, payer type [Medicaid or non-Medicaid], and socioeconomic status) were also associated with non-receipt of intervention. CONCLUSIONS: We found that many of the same patient-level characteristics associated with the highest readmission risk are also associated with non-receipt of readmission reduction interventions. This highlights the paradox that patients at high risk of readmission are least likely to accept or receive interventions for preventing readmission. Identifying strategies to engage hard-to-reach high-risk patients continues to be an unmet challenge in readmission prevention.

Implementation of a comprehensive program to improve coordination of care in an urban academic health care system.

Purpose Academic healthcare systems face great challenges in coordinating services across a continuum of care that spans hospital, community providers, home and chronic care facilities. The Johns Hopkins Community Health Partnership (J-CHiP) was created to improve coordination of acute, sub-acute and ambulatory care for patients, and improve the health of high-risk patients in surrounding neighborhoods. The paper aims to discuss this issue. Design/methodology/approach J-CHiP targeted adults admitted to the Johns Hopkins Hospital and Johns Hopkins Bayview Medical Center, patients discharged to participating skilled nursing facilities (SNFs), and high-risk Medicare and Medicaid patients receiving primary care in eight nearby outpatient sites. The primary drivers of the program were redesigned acute care delivery, seamless transitions of care and deployment of community care teams. Findings Acute care interventions included risk screening, multidisciplinary care planning, pharmacist-driven medication management, patient/family education, communication with next provider and care coordination protocols for common conditions. Transition interventions included post-discharge health plans, hand-offs and follow-up with primary care providers, Transition Guides, a patient access line and collaboration with SNFs. Community interventions involved forming multidisciplinary care coordination teams, integrated behavioral care and new partnerships with community-based organizations. Originality/value This paper offers a detailed description of the design and implementation of a complex program to improve care coordination for high-risk patients in an urban setting. The case studies feature findings from each intervention that promoted patient engagement, strengthened collaboration with community-based organizations and improved coordination of care.

Patient Perceptions of Readmission Risk: An Exploratory Survey.

Interventions to prevent readmissions often rely upon patient participation to be successful. We surveyed 895 general medicine patients slated for hospital discharge to (1) assess patient attitudes surrounding readmission, (2) ascertain whether these attitudes were associated with actual readmission, and (3) determine whether patients can estimate their own readmission risk. Actual readmissions and other clinical variables were captured from administrative data and linked to individual survey responses. We found that actual readmissions were not correlated with patients' interest in preventing readmission, sense of control over readmission, or intent to follow discharge instructions. However, patients were able to predict their own readmissions (P = .005) even after adjusting for predicted readmission rate, race, sex, age, and payer. Reassuringly, over 80% of respondents reported that they would be frustrated or disappointed to be readmitted and almost 90% indicated that they planned to follow all of their discharge instructions. Whether assessing patient-perceived readmission risk might help to target preventive interventions warrants further study.

A Method for Attributing Patient-Level Metrics to Rotating Providers in an Inpatient Setting.

BACKGROUND: Individual provider performance drives group metrics, and increasingly, individual providers are held accountable for these metrics. However, appropriate attribution can be challenging, particularly when multiple providers care for a single patient. OBJECTIVE: We sought to develop and operationalize individual provider scorecards that fairly attribute patient-level metrics, such as length of stay and patient satisfaction, to individual hospitalists involved in each patient's care. DESIGN: Using patients cared for by hospitalists from July 2010 through June 2014, we linked billing data across each hospitalization to assign "ownership" of patient care based on the type, timing, and number of charges associated with each hospitalization (referred to as "provider day weighted "). These metrics were presented to providers via a dashboard that was updated quarterly with their performance (relative to their peers). For the purposes of this article, we compared the method we used to the traditional method of attribution, in which an entire hospitalization is attributed to 1 provider, based on the attending of record as labeled in the administrative data. RESULTS: Provider performance in the 2 methods was concordant 56% to 75% of the time for top half versus bottom half performance (which would be expected to occur by chance 50% of the time). While provider percentile differences between the 2 methods were modest for most providers, there were some providers for whom the methods yielded dramatically different results for 1 or more metrics. CONCLUSION: We found potentially meaningful discrepancies in how well providers scored (relative to their peers) based on the method used for attribution. We demonstrate that it is possible to generate meaningful provider-level metrics from administrative data by using billing data even when multiple providers care for 1 patient over the course of a hospitalization.

Improving Outcomes After Hospitalization: A Prospective Observational Multicenter Evaluation of Care Coordination Strategies for Reducing 30-Day Readmissions to Maryland Hospitals.

BACKGROUND: Patients frequently experience suboptimal transitions from the hospital to the community, which can increase the likelihood of readmission. It is not known which care coordination services can lead to improvements in readmission rates. OBJECTIVE: To evaluate the effects of two care coordination interventions on 30-day readmission rates. DESIGN: Prospective multicenter observational study of hospitalized patients eligible for two care coordination services between January 1, 2013, and October 31, 2015. Readmission rates were compared for patients who received each care coordination intervention versus those who did not using multivariable generalized estimating equation logistic regression models. PARTICIPANTS: A total of 25,628 patients hospitalized in medicine, neurosciences, or surgical sciences units. INTERVENTIONS: Patients discharged home and deemed to be at high risk for readmission were assigned a nurse Transition Guide (TG) for 30 days post-discharge. All other patients were assigned the Patient Access Line (PAL) intervention, which provided a post-discharge phone call from a registered nurse. SETTING: Two large academic hospitals in Baltimore, MD. MAIN MEASURES: Thirty-day all-cause readmission to any Maryland hospital. KEY RESULTS: Among all patients, 14.2% (2409/16,993) of those referred for the PAL intervention and 22.8% (1973/8635) of those referred for the TG intervention were readmitted. PAL-referred patients who did not receive the intervention had an adjusted odds ratio (aOR) for readmission of 1.27 (95% confidence interval [95% CI] 1.12-1.44, p < 0.001) compared with patients who did. TG-referred patients who did not receive the TG intervention had an aOR of 1.83 (95% CI 1.60-2.10, p < 0.001) compared with patients who received the intervention. Younger age, male sex, having more comorbidities, and being discharged from a medicine unit were associated with not receiving an assigned intervention. These characteristics were also associated with higher readmission rates. CONCLUSIONS: PAL and TG care coordination interventions were associated with lower rates of 30-day readmission. Our findings underscore the importance of determining the appropriate intervention for the hardest-to-reach patients, who are also at the highest risk of being readmitted.

Patterns of Hospital Performance on the Hospital-Wide 30-Day Readmission Metric: Is the Playing Field Level?

BACKGROUND: Hospital performance on the 30-day hospital-wide readmission (HWR) metric as calculated by the Centers for Medicare and Medicaid Services (CMS) is currently reported as a quality measure. Focusing on patient-level factors may provide an incomplete picture of readmission risk at the hospital level to explain variations in hospital readmission rates. OBJECTIVE: To evaluate and quantify hospital-level characteristics that track with hospital performance on the current HWR metric. DESIGN: Retrospective cohort study. SETTING/PATIENTS: A total of 4785 US hospitals. METRICS: We linked publically available data on individual hospitals published by CMS on patient-level adjusted 30-day HWR rates from July 1, 2011, through June 30, 2014, to the 2014 American Hospital Association annual survey. Primary outcome was performance in the worst CMS-calculated HWR quartile. Primary hospital-level exposure variables were defined as: size (total number of beds), safety net status (top quartile of disproportionate share), academic status [member of the Association of American Medical Colleges (AAMC)], National Cancer Institute Comprehensive Cancer Center (NCI-CCC) status, and hospital services offered (e.g., transplant, hospice, emergency department). Multilevel regression was used to evaluate the association between 30-day HWR and the hospital-level factors. RESULTS: Hospital-level characteristics significantly associated with performing in the worst CMS-calculated HWR quartile included: safety net status [adjusted odds ratio (aOR) 1.99, 95% confidence interval (95% CI) 1.61-2.45, p < 0.001], large size (> 400 beds, aOR 1.42, 95% CI 1.07-1.90, p = 0.016), AAMC alone status (aOR 1.95, 95% CI 1.35-2.83, p < 0.001), and AAMC plus NCI-CCC status (aOR 5.16, 95% CI 2.58-10.31, p < 0.001). Hospitals with more critical care beds (aOR 1.26, 95% CI 1.02-1.56, p = 0.033), those with transplant services (aOR 2.80, 95% CI 1.48-5.31,p = 0.001), and those with emergency room services (aOR 3.37, 95% CI 1.12-10.15, p = 0.031) demonstrated significantly worse HWR performance. Hospice service (aOR 0.64, 95% CI 0.50-0.82, p < 0.001) and having a higher proportion of total discharges being surgical cases (aOR 0.62, 95% CI 0.50-0.76, p < 0.001) were associated with better performance. LIMITATION: The study approach was not intended to be an alternate readmission metric to compete with the existing CMS metric, which would require a re-examination of patient-level data combined with hospital-level data. CONCLUSION: A number of hospital-level characteristics (such as academic tertiary care center status) were significantly associated with worse performance on the CMS-calculated HWR metric, which may have important health policy implications. Until the reasons for readmission variability can be addressed, reporting the current HWR metric as an indicator of hospital quality should be reevaluated.

Association of a Care Coordination Model With Health Care Costs and Utilization: The Johns Hopkins Community Health Partnership (J-CHiP).

Importance: The Johns Hopkins Community Health Partnership was created to improve care coordination across the continuum in East Baltimore, Maryland. Objective: To determine whether the Johns Hopkins Community Health Partnership (J-CHiP) was associated with improved outcomes and lower spending. Design, Setting, and Participants: Nonrandomized acute care intervention (ACI) and community intervention (CI) Medicare and Medicaid participants were analyzed in a quality improvement study using difference-in-differences designs with propensity score-weighted and matched comparison groups. The study spanned 2012 to 2016 and took place in acute care hospitals, primary care clinics, skilled nursing facilities, and community-based organizations. The ACI analysis compared outcomes of participants in Medicare and Medicaid during their 90-day postacute episode with those of a propensity score-weighted preintervention group at Johns Hopkins Community Health Partnership hospitals and a concurrent comparison group drawn from similar Maryland hospitals. The CI analysis compared changes in outcomes of Medicare and Medicaid participants with those of a propensity score-matched comparison group of local residents. Interventions: The ACI bundle aimed to improve transition planning following discharge. The CI included enhanced care coordination and integrated behavioral support from local primary care sites in collaboration with community-based organizations. Main Outcomes and Measures: Utilization measures of hospital admissions, 30-day readmissions, and emergency department visits; quality of care measures of potentially avoidable hospitalizations, practitioner follow-up visits; and total cost of care (TCOC) for Medicare and Medicaid participants. Results: The CI group had 2154 Medicare beneficiaries (1320 [61.3%] female; mean age, 69.3 years) and 2532 Medicaid beneficiaries (1483 [67.3%] female; mean age, 55.1 years). For the CI group's Medicaid participants, aggregate TCOC reduction was $24.4 million, and reductions of hospitalizations, emergency department visits, 30-day readmissions, and avoidable hospitalizations were 33, 51, 36, and 7 per 1000 beneficiaries, respectively. The ACI group had 26 144 beneficiary-episodes for Medicare (13 726 [52.5%] female patients; mean patient age, 68.4 years) and 13 921 beneficiary-episodes for Medicaid (7392 [53.1%] female patients; mean patient age, 52.2 years). For the ACI group's Medicare participants, there was a significant reduction in aggregate TCOC of $29.2 million with increases in 90-day hospitalizations and 30-day readmissions of 11 and 14 per 1000 beneficiary-episodes, respectively, and reduction in practitioner follow-up visits of 41 and 29 per 1000 beneficiary-episodes for 7-day and 30-day visits, respectively. For the ACI group's Medicaid participants, there was a significant reduction in aggregate TCOC of $59.8 million and the 90-day emergency department visit rate decreased by 133 per 1000 episodes, but hospitalizations increased by 49 per 1000 episodes and practitioner follow-up visits decreased by 70 and 182 per 1000 episodes for 7-day and 30-day visits, respectively. In total, the CI and ACI were associated with $113.3 million in cost savings. Conclusions and Relevance: A care coordination model consisting of complementary bundled interventions in an urban academic environment was associated with lower spending and improved health outcomes.

Reconsidering Hospital Readmission Measures.

Current hospital readmission measures are part of the Centers for Medicare & Medicaid Services Five-Star Quality Rating System but are inadequate for reporting hospital quality. We review potential biases in the readmission measures and offer policy recommendations to address these biases. Hospital readmission rates are influenced by multiple sources of variation (eg, mix of patients served, bias in the performance measure); true differences in quality of care are often a much smaller source of this variation. Thus, variation from caring for large proportions of socioeconomically disadvantaged or tertiary-care patients will bias a hospital's ratings. Ratings aside, readmission measures may indirectly harm patients because low readmission rates do not correlate with reduced mortality, yet the Five-Star Quality Rating System weighs readmission equally with mortality. We propose that hospital quality rankings not use readmission measures as currently constructed.

A Concise Tool for Measuring Care Coordination from the Provider's Perspective in the Hospital Setting.

BACKGROUND: To support hospital efforts to improve coordination of care, a tool is needed to evaluate care coordination from the perspective of inpatient healthcare professionals. OBJECTIVES: To develop a concise tool for assessing care coordination in hospital units from the perspective of healthcare professionals, and to assess the performance of the tool in measuring dimensions of care coordination in 2 hospitals after implementation of a care coordination initiative. METHODS: We developed a survey consisting of 12 specific items and 1 global item to measure provider perceptions of care coordination across a variety of domains, including teamwork and communication, handoffs, transitions, and patient engagement. The questionnaire was distributed online between October 2015 and January 2016 to nurses, physicians, social workers, case managers, and other professionals in 2 tertiary care hospitals. RESULTS: A total of 841 inpatient care professionals completed the survey (response rate = 56.6%). Among respondents, 590 (75%) were nurses and 37 (4.7%) were physicians. Exploratory factor analysis revealed 4 subscales: (1) Teamwork, (2) Patient Engagement, (3) Handoffs, and (4) Transitions (Cronbach's alpha 0.84-0.90). Scores were fairly consistent for 3 subscales but were lower for patient engagement. There were minor differences in scores by profession, department, and hospital. CONCLUSIONS: The new tool measures 4 important aspects of inpatient care coordination with evidence for internal consistency and construct validity, indicating that the tool can be used in monitoring, evaluating, and planning care coordination activities in hospital settings.

Hospitalizations With Observation Services and the Medicare Part A Complex Appeals Process at Three Academic Medical Centers.

Hospitalists and other providers must classify hospitalized patients as inpatient or outpatient, the latter of which includes all observation stays. These orders direct hospital billing and payment, as well as patient out-of-pocket expenses. The Centers for Medicare & Medicaid Services (CMS) audits hospital billing for Medicare beneficiaries, historically through the Recovery Audit program. A recent U.S. Government Accountability Office (GAO) report identified problems in the hospital appeals process of Recovery Audit program audits to which CMS proposed reforms. In the context of the GAO report and CMS's proposed improvements, we conducted a study to describe the time course and process of complex Medicare Part A audits and appeals reaching Level 3 of the 5-level appeals process as of May 1, 2016 at 3 academic medical centers. Of 219 appeals reaching Level 3, 135 had a decision--96 (71.1%) successful for the hospitals. Mean total time since date of service was 1663.3 days, which includes mean days between date of service and audit (560.4) and total days in appeals (891.3). Government contractors were responsible for 70.7% of total appeals time. Overall, government contractors and judges met legislative timeliness deadlines less than half the time (47.7%), with declining compliance at successive levels (discussion, 92.5%; Level 1, 85.4%; Level 2, 38.8%; Level 3, 0%). Most Level 1 and Level 2 decision letters (95.2%) cited time-based (24-hour) criteria for determining inpatient status, despite 70.3% of denied appeals meeting the 24-hour benchmark. These findings suggest that the Medicare appeals system merits process improvement beyond current proposed reforms. Journal of Hospital Medicine 2017;12:251-255.

Novel Emergency Department Risk Score Discriminates Acute Coronary Syndrome Among Chest Pain Patients With Known Coronary Artery Disease.

BACKGROUND: Patients with known coronary artery disease presenting to the emergency department (ED) with chest pain are often admitted, yet may not be having an acute coronary syndrome (ACS). METHODS: We assessed whether the use of a novel risk score and a modified thrombolysis in myocardial infarction risk score obtained in the ED could discriminate which of these high-risk patients have ACS. Chart review was performed on a cohort of 285 patients with known coronary artery disease presenting to the ED with chest pain thought to be of ischemic origin and admitted to the hospital. The ED variables were assessed with logistic regression for their association with eventual ACS diagnosis at hospital discharge. ACS was diagnosed in 74 (26%) of the patients. RESULTS: Non-ACS patients had a 2-day median length of stay and $6875 median inpatient (post ED) hospital charges (not including physician fees), totaling 566 hospital bed days and $1,871,250 for the 211 (74%) non-ACS patients. A novel risk score, including (1) history of prior revascularization, (2) comorbid chronic kidney disease, (3) onset of chest discomfort at rest, (4) dynamic electrocardiogram changes in the ED, (5) elevated troponin I (>0.05 ng/mL) in the ED, and (6) associated illness at presentation, discriminated ACS and non-ACS with a c statistic of 0.767; the c statistic for a modified thrombolysis in myocardial infarction risk score was 0.712. CONCLUSIONS: Application of these risk scores may reduce the number of potentially avoidable admissions and their associated hazards and costs.

Case Study: Johns Hopkins Community Health Partnership: A model for transformation.

To address the challenging health care needs of the population served by an urban academic medical center, we developed the Johns Hopkins Community Health Partnership (J-CHiP), a novel care coordination program that provides services in homes, community clinics, acute care hospitals, emergency departments, and skilled nursing facilities. This case study describes a comprehensive program that includes: a community-based intervention using multidisciplinary care teams that work closely with the patient's primary care provider; an acute care intervention bundle with collaborative team-based care; and a skilled nursing facility intervention emphasizing standardized transitions and targeted use of care pathways. The program seeks to improve clinical care within and across settings, to address the non-clinical determinants of health, and to ultimately improve healthcare utilization and costs. The case study introduces: a) main program features including rationale, goals, intervention design, and partnership development; b) illness burden and social barriers of the population contributing to care challenges and opportunities; and c) lessons learned with steps that have been taken to engage both patients and providers more actively in the care model. Urban health systems, including academic medical centers, must continue to innovate in care delivery through programs like J-CHiP to meet the needs of their patients and communities.

Unplanned 30-day hospital readmission as a quality measure in gynecologic oncology.

OBJECTIVES: Thirty-day readmission is used as a quality measure for patient care and Medicare-based hospital reimbursement. The primary study objective was to describe the 30-day readmission rate to an academic gynecologic oncology service. Secondary objectives were to identify risk factors and costs related to readmission. METHODS: This was a retrospective, concurrent cohort study of all surgical admissions to an academic, high volume gynecologic oncology service during a two-year period (2013-2014). Data were collected on patient demographics, medical comorbidities, psychosocial risk factors, and results from a hospital discharge screening survey. Mixed logistic regression was used to identify factors associated with 30-day readmission and costs of readmission were assessed. RESULTS: During the two-year study period, 1605 women underwent an index surgical admission. Among this population, a total of 177 readmissions (11.0%) in 135 unique patients occurred. In a surgical subpopulation with >1 night stay, a readmission rate of 20.9% was observed. The mean interval to readmission was 11.8days (SD 10.7) and mean length of readmission stay was 5.1days (SD 5.0). Factors associated with readmission included radical surgery for ovarian cancer (OR 2.87) or cervical cancer (OR 4.33), creation of an ostomy (OR 11.44), a Charlson score of ≥5 (OR 2.15), a language barrier (OR 3.36), a median household income in the lowest quartile (OR 6.49), and a positive discharge screen (OR 2.85). The mean cost per readmission was $25,416 (SD $26,736), with the highest costs associated with gastrointestinal complications at $32,432 (SD $32,148). The total readmission-related costs during the study period were $4,523,959. CONCLUSIONS: Readmissions to a high volume gynecologic oncology service were costly and related to radical surgery for ovarian and cervical cancer as well as to medical, socioeconomic and psychosocial patient variables. These data may inform interventional studies aimed at decreasing unplanned readmissions in gynecologic oncology surgical populations.

Association between days to complete inpatient discharge summaries with all-payer hospital readmissions in Maryland.

OBJECTIVE: Hospital discharge summaries can provide valuable information to future providers and may help to prevent hospital readmissions. We sought to examine whether the number of days to complete hospital discharge summaries is associated with 30-day readmission rate. PATIENTS AND METHODS: This was a retrospective cohort study conducted on 87,994 consecutive discharges between January 1, 2013 and December 31, 2014, in a large urban academic hospital. We used multivariable logistic regression models to examine the association between days to complete the discharge summary and hospital readmissions while controlling for age, gender, race, payer, hospital service (gynecology-obstetrics, medicine, neurosciences, oncology, pediatrics, and surgical sciences), discharge location, length of stay, expected readmission rate in Maryland based on diagnosis and illness severity, and the Agency for Healthcare Research and Quality Comorbidity Index. Days to complete the hospital discharge summary-the primary exposure variable-was assessed using the 20th percentile (>3 vs ≤3 days) and as a continuous variable (odds ratio expressed per 3-day increase). The main outcome was all-cause readmission to any acute care hospital in Maryland within 30 days. RESULTS: Among the 87,994 patients, there were 14,248 (16.2%) total readmissions. Discharge summary completion >3 days was significantly associated with readmission, with adjusted odds ratio (OR) (95% confidence interval [CI]) of 1.09 (1.04 to 1.13, P = 0.001). We also found that every additional 3 days to complete the discharge summary was associated with an increased adjusted odds of readmission by 1% (OR: 1.01, 95% CI: 1.00 to 1.01, P < 0.001). CONCLUSION: Longer days to complete discharge summaries were associated with higher rates of all-cause hospital readmissions. Timely discharge summary completion time may be a quality indicator to evaluate current practice and as a potential strategy to improve patient outcomes. Journal of Hospital Medicine 2016;11:393-400. 2016 Society of Hospital Medicine.

Recovery Audit Contractor audits and appeals at three academic medical centers.

BACKGROUND: Outpatient (observation) and inpatient status determinations for hospitalized Medicare beneficiaries have generated increasing concern for hospitals and patients. Recovery Audit Contractor (RAC) activity alleging improper status, however, has received little attention, and there are conflicting federal and hospital reports of RAC activity and hospital appeals success. OBJECTIVE: To detail complex Medicare Part A RAC activity. DESIGN, SETTING AND PATIENTS: Retrospective descriptive study of complex Medicare Part A audits at 3 academic hospitals from 2010 to 2013. MEASUREMENTS: Complex Part A audits, outcome of audits, and hospital workforce required to manage this process. RESULTS: Of 101,862 inpatient Medicare encounters, RACs audited 8110 (8.0%) encounters, alleged overpayment in 31.3% (2536/8110), and hospitals disputed 91.0% (2309/2536). There was a nearly 3-fold increase in RAC overpayment determinations in 2 years, although the hospitals contested and won a larger percent of cases each year. One-third (645/1935, 33.3%) of settled claims were decided in the discussion period, which are favorable decisions for the hospitals not reported in federal appeals data. Almost half (951/1935, 49.1%) of settled contested cases were withdrawn by the hospitals and rebilled under Medicare Part B to avoid the lengthy (mean 555 [SD 255] days) appeals process. These original inpatient claims are considered improper payments recovered by the RAC. The hospitals also lost appeals (0.9%) by missing a filing deadline, yet there was no reciprocal case concession when the appeals process missed a deadline. No overpayment determinations contested the need for care delivered, rather that care should have been delivered under outpatient, not inpatient, status. The institutions employed an average 5.1 full-time staff in the audits process. CONCLUSIONS: These findings suggest a need for RAC reform, including improved transparency in data reporting.

Changes to inpatient versus outpatient hospitalization: Medicare's 2-midnight rule.

Outpatient versus inpatient status determinations for hospitalized patients impact how hospitals bill Medicare for hospital services. Medicare policies related to status determinations and the Recovery Audit Contractor (RAC) program charged with postpayment review of such determinations are of increasing concern to hospitals and physicians. We present an overview and discussion of these policies, including the recent 2-midnight rule, the effect on status determinations by the RAC program, and other recent and pertinent legislative and regulatory activity. Finally, we discuss the future direction of Medicare status determination policies and the RAC program, so that physicians and other healthcare providers caring for hospitalized Medicare beneficiaries may better understand these important and dynamic topics.