Higher Quality and Lower Cost from Improving Hospital Discharge Decision Making.

This paper reports research on improving decisions about hospital discharges - decisions that are now made by physicians based on mainly subjective evaluations of patients' discharge status. We report an experiment on uptake of our clinical decision support software (CDSS) which presents physicians with evidence-based discharge criteria that can be effectively utilized at the point of care where the discharge decision is made. One experimental treatment we report prompts physician attentiveness to the CDSS by replacing the default option of universal "opt in" to patient discharge with the alternative default option of "opt out" from the CDSS recommendations to discharge or not to discharge the patient on each day of hospital stay. We also report results from experimental treatments that implement the CDSS under varying conditions of time pressure on the subjects. The experiment was conducted using resident physicians and fourth-year medical students at a university medical school as subjects.

Incentivizing Cost-Effective Reductions in Hospital Readmission Rates.

The recent regulatory changes enacted by the Centers for Medicare and Medicaid Services (CMS) have identified hospital readmission rates as a critical healthcare quality metric. This research focuses on the utilization of pay-for-performance (P4P) mechanisms to cost effectively reduce hospital readmission rates and meet the regulatory standards set by CMS. Using the experimental economics laboratory we find that both of the P4P mechanisms researched, bonus and bundled payments, cost-effectively meet the performance criteria set forth by CMS. The bundled payment mechanism generates the largest reduction in patient length of stay (LOS) without altering the probability of readmission. Combined these results indicate that utilizing P4P mechanisms incentivizes cost effective reductions in hospital readmission rates.

Toward innovative, cost-effective, and systemic solutions to improve outcomes and well-being of military families affected by autism spectrum disorder.

The burdens faced by military families who have a child with autism are unique. The usual challenges of securing diagnostic, treatment, and educational services are compounded by life circumstances that include the anxieties of war, frequent relocation and separation, and a demand structure that emphasizes mission readiness and service. Recently established military autism-specific health care benefits set the stage for community-viable and cost-effective solutions that can achieve better outcomes for children and greater well-being for families. Here we argue for implementation of evidence-based solutions focused on reducing age of diagnosis and improving access to early intervention, as well as establishment of a tiered menu of services, individualized to the child and family, that fit with the military ethos and system of health care. Absence of this new model of care could compromise the utility and sustainability of the autism-specific benefit.

Assessing clinical discharge data preferences among practicing surgeons.

BACKGROUND: It is believed that many postoperative patient readmissions can be curbed via optimization of a patient's discharge from hospital, but little is known about how surgeons make the decision to discharge a patient. This study explored the criteria that surgeons preferentially value in their discharge decision-making process. MATERIALS AND METHODS: All surgical faculty and residents at a U.S. academic medical center were surveyed about the relative importance of specific criteria regularly used to make a discharge decision. Demographic and professional information was collected about each surgeon as well. A Kruskal-Wallis and Fisher's exact test were used to describe one-way analysis of variance between groupings of surgeons. Ordered logit regressions were used to analyze variations across multiple subgroups. Factor analysis was used to further characterize statistically relevant groupings of criteria. RESULTS: In total, 88 (49%) of the invited surgeons responded to the survey. Respondents reported statistically less reliance on common Laboratory tests and Patient demographics when making discharge decisions preferring Vital signs, Perioperative factors, and Functional criteria. Surgeon-specific factors that influenced discharge criteria preferences included years of clinical education and gender. Factor analysis further identified subtle variations in preferences for specific criteria groupings based on clinical education, gender, and race. CONCLUSIONS: Surgeons use a wide range of clinical data when making discharge decisions. Typical measures of patient condition also appear to be used heterogeneously with a preference for binary rather than continuous measures. Further understanding the nature of these preferences may suggest novel ways of presenting discharge-relevant information to clinical decision makers to optimize discharge outcomes.

Effects of patients' hospital discharge preferences on uptake of clinical decision support.

The Centers for Medicare and Medicaid Services identified unplanned hospital readmissions as a critical healthcare quality and cost problem. Improvements in hospital discharge decision-making and post-discharge care are needed to address the problem. Utilization of clinical decision support (CDS) can improve discharge decision-making but little is known about the empirical significance of two opposing problems that can occur: (1) negligible uptake of CDS by providers or (2) over-reliance on CDS and underuse of other information. This paper reports an experiment where, in addition to electronic medical records (EMR), clinical decision-makers are provided subjective reports by standardized patients, or CDS information, or both. Subjective information, reports of being eager or reluctant for discharge, was obtained during examinations of standardized patients, who are regularly employed in medical education, and in our experiment had been given scripts for the experimental treatments. The CDS tool presents discharge recommendations obtained from econometric analysis of data from de-identified EMR of hospital patients. 38 clinical decision-makers in the experiment, who were third and fourth year medical students, discharged eight simulated patient encounters with an average length of stay 8.1 in the CDS supported group and 8.8 days in the control group. When the recommendation was "Discharge," CDS uptake of "Discharge" recommendation was 20% higher for eager than reluctant patients. Compared to discharge decisions in the absence of patient reports: (i) odds of discharging reluctant standardized patients were 67% lower in the CDS-assisted group and 40% lower in the control (no-CDS) group; whereas (ii) odds of discharging eager standardized patients were 75% higher in the control group and similar in CDS-assisted group. These findings indicate that participants were neither ignoring nor over-relying on CDS.

Discharge decision-making after complex surgery: Surgeon behaviors compared to predictive modeling to reduce surgical readmissions.

BACKGROUND: Little is known about how information available at discharge affects decision-making and its effect on readmission. We sought to define the association between information used for discharge and patients' subsequent risk of readmission. METHODS: 2009-2014 patients from a tertiary academic medical center's surgical services were analyzed using a time-to-event model to identify criteria that statistically explained the timing of discharges. The data were subsequently used to develop a time-varying prediction model of unplanned hospital readmissions. These models were validated and statistically compared. RESULTS: The predictive discharge and readmission regression models were generated from a database of 20,970 patients totaling 115,976 patient-days with 1,565 readmissions (7.5%). 22 daily clinical measures were significant in both regression models. Both models demonstrated good discrimination (C statistic = 0.8 for all models). Comparison of discharge behaviors versus the predictive readmission model suggested important discordance with certain clinical measures (e.g., demographics, laboratory values) not being accounted for to optimize discharges. CONCLUSIONS: Decision-support tools for discharge may utilize variables that are not routinely considered by healthcare providers. How providers will then respond to these atypical findings may affect implementation.

Asymmetrically dominated choice problems, the isolation hypothesis and random incentive mechanisms.

This paper presents an experimental study of the random incentive mechanisms which are a standard procedure in economic and psychological experiments. Random incentive mechanisms have several advantages but are incentive-compatible only if responses to the single tasks are independent. This is true if either the independence axiom of expected utility theory or the isolation hypothesis of prospect theory holds. We present a simple test of this in the context of choice under risk. In the baseline (one task) treatment we observe risk behavior in a given choice problem. We show that by integrating a second, asymmetrically dominated choice problem in a random incentive mechanism risk behavior can be manipulated systematically. This implies that the isolation hypothesis is violated and the random incentive mechanism does not elicit true preferences in our example.

Risk factors for 30-day hospital readmission among general surgery patients.

BACKGROUND: Hospital readmission within 30 days of an index hospitalization is receiving increased scrutiny as a marker of poor-quality patient care. This study identifies factors associated with 30-day readmission after general surgery procedures. STUDY DESIGN: Using standard National Surgical Quality Improvement Project protocol, preoperative, intraoperative, and postoperative outcomes were collected on patients undergoing inpatient general surgery procedures at a single academic center between 2009 and 2011. Data were merged with our institutional clinical data warehouse to identify unplanned 30-day readmissions. Demographics, comorbidities, type of procedure, postoperative complications, and ICD-9 coding data were reviewed for patients who were readmitted. Univariate and multivariate analysis was used to identify risk factors associated with 30-day readmission. RESULTS: One thousand four hundred and forty-two general surgery patients were reviewed. One hundred and sixty-three (11.3%) were readmitted within 30 days of discharge. The most common reasons for readmission were gastrointestinal problem/complication (27.6%), surgical infection (22.1%), and failure to thrive/malnutrition (10.4%). Comorbidities associated with risk of readmission included disseminated cancer, dyspnea, and preoperative open wound (p < 0.05 for all variables). Surgical procedures associated with higher rates of readmission included pancreatectomy, colectomy, and liver resection. Postoperative occurrences leading to increased risk of readmission were blood transfusion, postoperative pulmonary complication, wound complication, sepsis/shock, urinary tract infection, and vascular complications. Multivariable analysis demonstrates that the most significant independent risk factor for readmission is the occurrence of any postoperative complication (odds ratio = 4.20; 95% CI, 2.89-6.13). CONCLUSIONS: Risk factors for readmission after general surgery procedures are multifactorial, however, postoperative complications appear to drive readmissions in surgical patients. Taking appropriate steps to minimize postoperative complications will decrease postoperative readmissions.