National Quality Measures Clearinghouse | Expert Commentaries: Feasibility of Preventable Readmission Rate as a Quality Measure

National Quality Measures Clearinghouse | Expert Commentaries: Feasibility of Preventable Readmission Rate as a Quality Measure

June 20, 2016

 

Feasibility of Preventable Readmission Rate as a Quality Measure

By: Christine Soong, MD, MSc

Beginning in 2008, the Affordable Care Act (ACA) reduced reimbursement to hospitals with excessive all-cause readmission rates for 6 specified diagnoses (1). As a result, unplanned hospital readmission rates have been a subject of intense debate and research. Because contributors to hospital readmission are numerous and subject to complex interplay, all-cause readmission rates are considered a blunt measure, lacking specificity and leaving clinicians uncertain of how to interpret results meaningfully. Academics and administrators have since turned their attention toward the promise of a more sensitive measure in preventable readmission rates. However, years of exhaustive efforts have yet to bear fruit.

Recently, several promising studies have shed light on the preventability of readmissions. Auerbach and colleagues conducted a cohort study of 1000 patients discharged and readmitted within 30 days from 12 large academic medical centres in the United States (2). The authors performed retrospective chart reviews, interviews, and surveys of patients and primary care providers to collect data on known potential contributors to readmission, including patient factors (e.g., disease severity, health literacy), hospital factors (e.g., transitions of care processes, length of stay), and health system factors (e.g., access to primary care post discharge). Blinded clinicians then used a scale similar to that used in the adverse events literature to adjudicate whether the readmission was preventable. They found that nearly 27% of readmissions in the cohort were potentially avoidable. The factors most commonly associated with preventability were the following: emergency department physicians making decisions to readmit patients who may not have required hospitalization, inability to keep post-discharge follow-up appointments, premature discharge, and inability to identify a clinician responsible for post-discharge care. While the study was limited in methodology (retrospective design subject to recall and hindsight bias), it provided new information on important readmission factors not previously considered (such as the decision to admit in the emergency department). However, some results, such as a patient's functional status bearing no impact on readmission, was in conflict with previous studies, leaving us with more questions than answers.

At the same time, a second study validating a potentially preventable readmission risk prediction score (the HOSPITAL score) among 9 large hospitals in 4 countries was released (3). The authors found that the HOSPITAL score had good discriminatory power with a C-statistic of 0.72. While the HOSPITAL score may be a promising tool to aid efforts to reduce potentially preventable readmissions, whether these represent truly avoidable readmissions is unknown.

Finally, Jenq and colleagues conducted a quasi-experimental evaluation of an intense transitional care program targeting over 10,000 patients at high risk of readmission (4). The intervention consisted of "Transitional Care Consultants" who followed the patients during admission and in the post-discharge period. The consultants conducted follow-up phone calls to assist patients with post-discharge instructions and plans. In addition, they made recommendations when appropriate. Using time series analysis, the authors found a significant reduction in readmission rates by over 9%. However, the authors noted that the intervention required significant human resources and effort, and although a reduction was attained, they failed to meet the goal of a relative reduction of 20% set out by the Center for Medicare and Medicaid Services (CMS). In addition, each component was carried out with varying fidelity resulting in only 58% of the patients in the experimental arm actually receiving the intervention as intended. This calls into question the strength of the causal association between intervention and outcome and whether a target reduction of 20% is reasonable or attainable.

While we have witnessed some recent progress in identifying preventable readmissions, whether they are actually preventable remains up for debate. The evidence is unclear in this regard with systematic reviews demonstrating weak association between transitions of care interventions and readmissions (5). A recent time series analysis on readmission trends suggests that under CMS's Hospitals Readmissions Reduction Program, rates declined by just under 4% from 21.5% to 17.8% for targeted conditions (6). As a measure of performance, avoidable readmission rates in comparison to all-cause readmission rates may still lack specificity and actionability. Ultimately, the determination of preventability is subjective with extremely low inter-rater agreement among blinded adjudicators (7). According to the Agency for Healthcare Research and Quality (AHRQ), a measure to drive quality should have robust attributes such as reliability (a measure must have reproducibility irrespective of who or when it is made), feasibility (a measure must have an explicit and measurable numerator and denominator), and susceptibility (the results of the measure relate to actions or interventions that are under the control of those providers whose performance is being measured, so that it is possible for them to improve that performance) (8). Potentially avoidable readmission fails to meet all three of these standards.

While variation in healthcare is cause for concern, we should carefully consider our choice of metrics. All too often, process measures (such as all-cause or preventable readmission rates) are used in place of meaningful clinical outcomes to incentivize improvement. For example, it is entirely reasonable to use preventable readmission rates to drive local quality improvement projects in transitions of care. However, holding hospitals accountable to a measure not entirely within their control can have the unintended consequence of slowing real progress through diversion of resources. Despite spending the last 6 years investing significant effort to reduce readmission rates, hospitals in the country have only achieved a mean reduction of 3.7% (6). Whether there has been true positive impact on patients is unknown. One cannot help but think of the missed opportunity to engage in meaningful patient-centered activities. It is time to re-align incentives with appropriate outcome measures starting with the first step of developing quality indicators that matter to patients.

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Author

Christine Soong, MD, MSc
Division of General Internal Medicine, Sinai Health System, Toronto, Ontario
Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario
Centre for Quality Improvement and Patient Safety, University of Toronto, Toronto, Ontario

Disclaimer

The views and opinions expressed are those of the author and do not necessarily state or reflect those of the Department of Veterans Affairs, the National Quality Measures Clearinghouse™ (NQMC), the Agency for Healthcare Research and Quality (AHRQ), or its contractor ECRI Institute.

Potential Conflicts of Interest

Dr. Christine Soong has declared no conflicts of interest with respect to this expert commentary.

References

1. Centers for Medicare & Medicaid Services (CMS). Readmissions Reduction Program. [internet]. Atlanta (GA): Centers for Medicare & Medicaid Services (CMS); [accessed 2014 Jul 16]. Available: http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.html .
2. Auerbach AD, Kripalani S, Vasilevskis EE, et al. Preventability and causes of readmissions in a national cohort of general medicine patients. JAMA Intern Med 2016;176(4):484-93.
3. Donzé JD, Williams MV, Robinson EJ, et al. International validity of the HOSPITAL score to predict 30-day potentially avoidable hospital readmissions. JAMA Intern Med. 2016 Apr 1;176(4):496-502.
4. Jenq GY, Doyle MM, Belton BM, Herrin J, Horwitz LI. Quasi-experimental evaluation of the effectiveness of a large-scale readmission reduction program. JAMA Intern Med 2016;:1-10.
5. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med 2011;155(8):520-8.
6. Zuckerman RB, Sheingold SH, Orav EJ, Ruhter J, Epstein AM. Readmissions, observation, and the Hospital Readmissions Reduction Program. N Engl J Med 2016;374(16):1543-51.
7. van Walraven C, Jennings A, Taljaard M, et al. Incidence of potentially avoidable urgent readmissions and their relation to all-cause urgent readmissions. CMAJ. 2011;183(14):E1067-72.
8. National Quality Measures Clearinghouse (NQMC). Desirable attributes of a quality measure. [internet]. Rockville (MD): Agency for Healthcare Research and Quality (AHRQ); 2015 Jun 18 [accessed 2015 Jul 2]. Available:http://www.qualitymeasures.ahrq.gov/tutorial/attributes.aspx.