A theoretical framework for research on readmission risk prediction

On the one hand, predictive analytics is an important field of research in Information Systems (IS); however, research on predictive analytics in healthcare is still scarce in IS literature. One area where predictive analytics can be of great benefit is with regard to unplanned readmissions. While a number of studies on readmission prediction already exists in related research areas, there are few guidelines to date on how to conduct such analytics projects. To address this gap the paper presents the general process to develop empirical models by Shmueli and Koppius (2011) and extends this to the specific requirements of readmission risk prediction. Based on a systematic literature review, the resulting process defines important aspects of readmission prediction. It also structures relevant questions and tasks that need to be taken care of in this context. This extension of the guidelines by Shmueli and Koppius (2011) provides a best practice as well as a template that can be used in future studies on readmission risk prediction, thus allowing for more comparable results across various research fields

Using Shock Index as a Predictor of ICU Readmission: A Quality Iimprovement Project

Background: Adverse events will occur in one-third of patients discharged from the intensivecare unit (ICU) and evidence shows that ICU readmissions increase a patient’s length of stay,mortality, hospital costs, and nosocomial infections, as well as decrease long-term survival.Specific predictive factors that will accurately predict which patients are at risk of adverseevents requiring readmission are needed.Aim: The specific aim of this project was to identify if shock index (SI) values higher than 0.7at the time of transfer from the ICU are a useful predictor of ICU readmission.Methods: Using the Plan, Do, Study, Act (PDSA) framework, a retrospective chart review wasperformed using a matched cohort of 34 patients readmitted with 72 hours of discharge from theICU and 34 controls to obtain SI values at admission, transfer from and readmission to the ICU.A second PDSA cycle looked for SI trends within 24 hours prior to discharge from the ICU.Results: An odds ratio calculating the risk of readmission of patients with an elevated SI was2.96 (Confidence Interval (CI) 1.1 to 7.94, p-value=0.03). The odds ratio for an 80% SIelevation over 24 hours prior to discharge was 1.56 (CI 0.36 to 6.76, p-value=0.55).Conclusion and Implications for CNL Practice: Patients with elevated SIs at the time oftransfer are three times more likely to be readmitted to the ICU. Patients with elevations in atleast 80% of the 24 hour pre-discharge SIs showed no significant differences between thecontrol and readmitted cohorts. Implications of these results for the clinical nurse leader will bediscussed

A novel framework for predicting patients at risk of readmission

Uncertainty in decision-making for patients’ risk of re-admission arises due to non-uniform data and lack of knowledge in health system variables. The knowledge of the impact of risk factors will provide clinicians better decision-making and in reducing the number of patients admitted to the hospital. Traditional approaches are not capable to account for the uncertain nature of risk of hospital re-admissions. More problems arise due to large amount of uncertain information. Patients can be at high, medium or low risk of re-admission, and these strata have ill-defined boundaries. We believe that our model that adapts fuzzy regression method will start a novel approach to handle uncertain data, uncertain relationships between health system variables and the risk of re-admission. Because of nature of ill-defined boundaries of risk bands, this approach does allow the clinicians to target individuals at boundaries. Targeting individuals at boundaries and providing them proper care may provide some ability to move patients from high risk to low risk band. In developing this algorithm, we aimed to help potential users to assess the patients for various risk score thresholds and avoid readmission of high risk patients with proper interventions. A model for predicting patients at high risk of re-admission will enable interventions to be targeted before costs have been incurred and health status have deteriorated. A risk score cut off level would flag patients and result in net savings where intervention costs are much higher per patient. Preventing hospital re-admissions is important for patients, and our algorithm may also impact hospital income

A design science framework for research in health analytics

Data analytics provide the ability to systematically identify patterns and insights from a variety of data as organizations pursue improvements in their processes, products, and services. Analytics can be classified based on their ability to: explore, explain, predict, and prescribe. When applied to the field of healthcare, analytics presents a new frontier for business intelligence. In 2013 alone, the Centers for Medicare and Medicaid Services (CMS) reported that the national health expenditure was $2.9 trillion, representing 17.4% of the total United States GDP. The Patient Protection and Affordable Care Act of 2010 (ACA) requires all hospitals to implement electronic medical record (EMR) technologies by year 2014 (Patient Protection and Affordable Care Act, 2010). Moreover, the ACA makes healthcare process and outcomes more transparent by making related data readily available for research. Enterprising organizations are employing analytics and analytical techniques to find patterns in healthcare data (I. R. Bardhan & Thouin, 2013; Hansen, Miron-Shatz, Lau, & Paton, 2014). The goal is to assess the cost and quality of care and identify opportunities for improvement for organizations as well as the healthcare system as a whole. Yet, there remains a need for research to systematically understand, explain, and predict the sources and impacts of the widely observed variance in the cost and quality of care available. This is a driving motivation for research in healthcare. This dissertation conducts a design theoretic examination of the application of advanced data analytics in healthcare. Heart Failure is the number one cause of death and the biggest contributor healthcare costs in the United States. An exploratory examination of the application of predictive analytics is conducted in order to understand the cost and quality of care provided to heart failure patients. The specific research question is addressed: How can we improve and expand upon our understanding of the variances in the cost of care and the quality of care for heart failure? Using state level data from the State Health Plan of North Carolina, a standard readmission model was assessed as a baseline measure for prediction, and advanced analytics were compared to this baseline. This dissertation demonstrates that advanced analytics can improve readmission predictions as well as expand understanding of the profile of a patient readmitted for heart failure. Implications are assessed for academics and practitioners

Reducing Thirty-Day Hospital Readmissions in Drug and Medication Poisoning: An Observational Study

A common and costly occurrence in the United States is thirty-day hospital readmissions. Awareness of 30-day hospital readmissions is currently a national priority. To reduce avoidable readmissions, the Patient Protection and Affordable Care Act of 2010 established a “Hospital Readmission Reduction Program” implemented to provide possible solutions for preventable thirty-day readmissions. Part of this policy states that hospitals with higher than expected adjusted re-hospitalization rates have lower reimbursement rates. One specific area known to be a cause of thirty-day hospital readmissions is drug and medication poisoning. An observational study of data from the Nationwide Readmissions Database is being used to help identify contributing factors and provide suggestions for preventable thirty-day readmissions relative to drug and medication poisoning. Factors that include: gender; demographics; cost index; socio-economic, and hospital factors are identified to aid in the understanding of thirty-day hospital readmission of drug and medication poisoning. Finally, suggestions based on quantitative analyses contribute to the understanding of risk factors of thirty-day readmissions in drug and medication poisoning occurrences. Outcomes include statistical significance in gender and significance in the cost index of the individual patient; such as the ability to pay or not to pay for services rendered. Certain socio-economic factors whereas contributed, however, overall socioeconomic status was not significant along with hospital specific factors being insignificant. The study resulted in the identification of factors to aid in drug/medication episodic occurrences in a patient population experiencing thirty-day readmissions. Prevention strategy from both a clinical and practical application may be used to initiate cost saving applications. Future studies suggest expanding on drug and medication poisoning in certain sub-specific populations, further identifying illegal vs. legal drug/medication differentiation, and conducting international comparisons based on current findings

Lessons Learned from the Quality Improvement Process in a Community Based Hospital: The Dissection of Implementation Failure of Use of the PRISM Mortality Risk Tool and Standardization of Case Management to Reduce Readmissions in High Risk Patients

Hospital readmission, particularly within 30 days of discharge, is a wicked problem. Effective case management is an imperative component of high quality healthcare for the successful transition of patients across acute and post-acute settings. Patients with complex care needs endure an increased risk for negative outcomes, mortality, and hospital readmission. A small body of evidence suggests that early, targeted interventions aimed at high risk patients can mitigate complications and poor transitions. Patient complexity is an important consideration when establishing a comprehensive care management plan. Risk prediction tools are valuable for ensuring that high risk patients receive appropriate resource allocation. Case management processes must promote identification of patients with the most complex needs for the timely delivery of services that are nurse-coordinated, collaborative, and patient-centered. The purpose of this scholarly project was to collaborate with the Case Management and Clinical Quality Management teams at an urban community-based hospital (CBH) to establish a standardized case management protocol for patients determined to be at high risk for mortality and readmission. Using the scores derived from a 30-day mortality risk prediction tool, PRISM, the project plan was to prioritize patients for case management services. The goal of this project was to augment current case management services to ensure that PRISM 1, 2, and 3 patients concurrently receive a standardized bundle of care and person-centered transition planning, beginning at the onset of the inpatient stay

The effects of a video intervention on posthospitalization pulmonary rehabilitation uptake

Rationale: Pulmonary rehabilitation (PR) after hospitalizations for exacerbations of chronic obstructive pulmonary disease (COPD) improves exercise capacity and health-related quality of life and reduces readmissions. However, posthospitalization PR uptake is low. To date, no trials of interventions to increase uptake have been conducted.Objectives: To study the effect of a codesigned education video as an adjunct to usual care on posthospitalization PR uptake.Methods: The present study was an assessor- and statistician-blinded randomized controlled trial with nested, qualitative interviews of participants in the intervention group. Participants hospitalized with COPD exacerbations were assigned 1:1 to receive either usual care (COPD discharge bundle including PR information leaflet) or usual care plus the codesigned education video delivered via a handheld tablet device at discharge. Randomization used minimization to balance age, sex, FEV1 % predicted, frailty, transport availability, and previous PR experience.Measurements and Main Results: The primary outcome was PR uptake within 28 days of hospital discharge. A total of 200 patients were recruited, and 196 were randomized (51% female, median FEV1% predicted, 36 [interquartile range, 27-48]). PR uptake was 41% and 34% in the usual care and intervention groups, respectively (P = 0.37), with no differences in secondary (PR referral and completion) or safety (readmissions and death) endpoints. A total of 6 of the 15 participants interviewed could not recall receiving the video.Conclusions: A codesigned education video delivered at hospital discharge did not improve posthospitalization PR uptake, referral, or completion