Who has higher readmission rates for heart failure, and why?: Implications for efforts to improve care using financial incentives” by, Joynt, K. and Jha, A Article Critique

The article titled “Who has higher readmission rates for heart failure, and why?: Implications for efforts to improve care using financial incentives” by, Joynt, K. and Jha, A. (2010), will be reviewed and critiqued for this paper. The purpose of this article was to explore factors, which allow some hospitals more successful outcomes at preventing heart failure readmissions than others. The authors evaluated hospital characteristics in the hopes of determining how to “most effectively facilitate quality improvement initiatives to decrease heart failure readmissions.”  (Joynt&Jha, 2010).  The authors imply that understanding what hospital characteristics influence heart failure readmissions can aid in the development of programs to avoid pay-for-performance (P4P) penalties.  Joynt&Jha (2010) also hypothesize hospitals with limited monetary and medical resources will experience higher penalties.

Evaluation of the Concept

Patient Protection and Affordable Care Act of 2010 implemented P4P as a method for influencing quality improvement initiatives for hospitals by offering rewards for those who are highly successful (Medicare.gov, 2015).  Designed to improve quality, these payment programs contain the potential to penalize hospitals who are already resource limited and experiencing financial hardships.  According to the authors, heart failure is the most common cause of admissions and readmissions among Medicare patients costing the nation more than 17 billion dollars a year (Joynt&Jha, 2010).  Estimates claim that one in four patients with heart failure will be readmitted to the hospital within 30 days of discharge.  “These rates suggest tremendous opportunity for improvement.”  (Joynt&Jha, 2010).

Reviewing the literature, the authors found studies have looked at patients at risk for readmission due to heart failure, but little work has evaluated the hospitals themselves (Joynt&Jha, 2010).  Joynt&Jha (2010) found two studies that concluded no difference between types of hospitals; however, the authors felt both studies used a method designed to reduce variability that could unduly influence the findings of the study.  These hypothesizes are written to lead the reader to believing variability may be the answer to what influences readmission rates among hospitals and support the idea of the study.  With little data to define which hospitals would experience higher readmission rates and would face penalties, the authors furtherconsidered physical resources in an attempt to establish a link to readmitted patients.  In order to establish which hospitals to evaluate for limited resources, Joynt and Jha (2010) appraised public hospitals, hospitals in poor counties nationally, as well as, hospitals without cardiac services.  Small hospitals were also gauged based on the assumption they have few financial and clinical resources and face challenges similar to public and poor hospitals (Joynt&Jha, 2010).  In 2014, Anderson wrote a commentary that discussed the first year of the Hospital Readmissions Reduction Program which issued penalties totaling more than 227 million dollars.  This had the greatest impact on of poorest hospitals in the country and further confirmedJoynt&Jha’sconcern written in 2010.

Utilizing Medicare Provider Analysis Review (MedPAR) data, the authors examined heart failure patient hospitalizations for 23 months and each patient for thirty days post discharge excluding federal hospitals, those outside the United States, and the District of Columbia.  23 months was used as the study took place over two physical years and required a full 30 day review.  Recommendations from the Joint Commission advised that hospitals with less than 25 discharges over the specified time frame also be excluded leaving a total of 4091 hospitals for analysis, a significant sample size (Joynt&Jha, 2010).  Using published survey results from the American Hospital Association of 2007, characteristics of those 4091 hospitals were collected “including hospital size, cardiac services, ownership, nurses on staff, and proportion of hospitalized patients with Medicaid or Medicare insurance, membership in the Council of Teaching Hospitals, location, and region.” (Joynt&Jha, 2010).  Staffing ratios and county median income where the hospital was physically located was also collected to include in the analysis.  Providing analysis around this number of characteristics gave the authors the ability to look at how variable resources may affect readmission rates versus a methodology that normalizes the data in an effort to equalize hospital performance for public reporting (Joynt&Jha, 2010).  Ash, Fienberg, Louis, Normand, Stukel, and Utts (2012) discussed how the Bayesian shrinkage model used by CMS eliminates volume or the ability to adapt for low volumes placing smaller capacity hospitals at higher risk for failure related to performance.  A metric measure where small hospitals are not truly equal or capable to other hospitals; however, are judged in the same manner again echoesJoynt and Jha concerns from 2010.

The authors chose to create “a set of multivariate logistic regression models, using generalized estimating equations, in which the response probability distribution was binary; allowed us to use logistic regression but also to account for clustering of patients within hospitals.” (Joynt&Jha, 2010).  Logistic regression methodology allowed for evaluation of each of hospital characteristics and to then compare the findings to hospitals that would be at risk for penalties.  Both the Bayesian shrinkage model and the multivariable model was used to run the data setting up a compare and contrast.  Differences were obvious and confirmed the author’s hypothesis that the Bayesian shrinkage model penalized smaller hospitals when volume is eliminated.  Joynt&Jha (2010) found that performance was likely to remain in the worst quartile if the hospital was public or lacked the clinical and financial resources just as they had predicted.  All of these characteristics demonstrated a statistical significance when outlined in themultivariate analysis giving the study more credibility and confirming the hypothesis of Joynt and Jha (2010).

Application of Concept

            About five million people are currently diagnosed with heart failure in the United States according to the Centers for Disease Control and Prevention (2014).  Due to the dynamics of heart failure, more than 32 billion dollars will be spent in an effort to provide care and medications for these patients each year (CDC, 2014).  Based upon these costs, Centers for Medicaid and Medicare Services passed a rule identifying heart failure readmissions within 30 days as an element for penalty under the Hospital Readmissions Reduction Program (CMS, 2015).  Using the findings of Joynt&Jha (2010) hospitals at risk for penalties should evaluate hospitals that were considered resource-poor or financially poor and performed well on avoiding readmissions.  Identifying these differing hospital characteristics allows a smaller organization or an organization with limited financial resources to restructure or redesign their approach to avoiding readmissions.  In an article by Bradley et al. (2013), six strategies were identified which may require little fiscal investment such as medication reconciliation by a registered nurse, arranging a follow up appointment prior to discharge, and partnerships with community physicians to develop solutions to avoid readmissions.As pay-for-performance matures, patients, physicians, and nurses must urge legislation to evaluate the impact of penalties.  Communities with financial and clinical resource limitations must demand that investments be made to enrich the opportunity to obtain quality care in their town or city.  According to James (2012), if resources are not allocated to lower income areas, P4P penalties will only exacerbate the lack of care provided to the patients who need it most.

 

References

Anderson, A. (2014).  The impact of the affordable care act on the healthcare workforce.The

Backgrounder 2887:  1-20.

Ash, A., Fienber, S., Louis, T., Norman, S., Stukel, T., and Utts, J. (2012).  Statistical issues in

assessing hospital performance.  Quantitative Health Sciences Publications and Presentations.  Paper 1114.  Retrieved on September 8, 2015 from http://escholarship.umassmed.edu/qhs_pp/1114.

Bradley, E., Curry, L., Horwitz, L., Sipsma, H., Wang, Y., Walsh, M…Krumholz, H. (2013).

Hospitals strategies associated with 30-day readmission rates for patients with heart failure.  Circulation:  Cardiovascular Quality and Outcomes 2013(6):  444-450.  doi:  10.1161/CIRCOUTCOMES.111.0001.01.

CDC (2014).  Division for heart disease and stroke prevention:  heart failure fact sheet.

Retrieved on September 9, 2015 from http://www.cdc.gov/dhdsp/data_statistics/fact_sheets/fs_heart_failure.htm.

CMS (2015).Readmissions reduction program.  Retrieved on September 9, 2015 from

https://www.cms.gov/medicare/medicare-fee-for-service-payment/acuteinpatientpps/readmissions-reduction-program.html.

James, J. (2012).  Pay for performance: new payment systems reward doctors and hospitals for

improving the quality of care, but studies to date show mixed results.  Health Policy Brief 2012:  1-6.

Joynt, K., and Jha, A. (2010).  Who has higher readmission rates for heart failure, and why?:

implications for efforts to improve care using financial incentives.  Circulation:  Cardiovascular Quality Outcomes 2011(4):  53-39.  doi:  10.1161/CIRCOUTCOMES.110.950964.

Medicare.gov (2015).  Hospital Compare:  linking quality to payment.  Retrieved on September

8, 2015 from https://www.medicare.gov/hospitalcompare/linking-quality-to-payment.html.

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