Hospital readmission rates are considered to be an important indicator of quality of care because they may be a consequence of actions of commission or omission made during the initial hospitalization of the patient, or as a consequence of poorly managed transition of the patient back into the community. The negative impact on patient quality of life and huge burden on healthcare system have made reducing hospital readmissions a central goal of healthcare delivery and payment reform efforts. In this study, we will be proposing a framework on how the readmission analysis and other healthcare models could be deployed in real world and a Machine learning based solution which uses patients discharge summaries as a dataset to train and test the machine learning model created. Current systems does not take into consideration one of the very important aspect of solving readmission problem by taking Big data into consideration. This study also takes into consideration Big data aspect of solutions which can be deployed in the field for real world use. We have used HPCC compute platform which provides distributed parallel programming platform to create, run and manage applications which involves large amount of data. We have also proposed some feature engineering and data balancing techniques which have shown to greatly enhance the machine learning model performance. This was achieved by reducing the dimensionality in the data and fixing the imbalance in the dataset. The system presented in this study provides a real world machine learning based predictive modeling for reducing readmissions which could be templatized for other diseases. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

Telehealth initiated a transformation in the realm of innovative strategies to meet the demands of an ever-changing health care system. Adapting provisions to new delivery care models such as telehealth is one way to improve access to care. The purpose of this project was to explore evidence of best practices in telehealth through an extensive, systematic literature review. The practice-focused question focused on identifying advantages of and barriers to the use of telehealth for improving patient satisfaction and quality of care. The plan-do-study-act cycle served as a model for accelerating quality improvement through improved systems of practice, and the Critical Appraisal Skills Program tool was used to identify factors in the literature that indicated the clinical effectiveness of telehealth and the contributions of information technology to patient outcomes throughout the care continuum. Applying Melnyk and Fineout-Overholt's model, which consists of 7 levels for grading evidence, 11 articles were identified as meeting the inclusion criteria. With respect to comparing telehealth services, this review identified areas for future research, including how telehealth can be used to bridge the gap between hospital and home with the integration of telehealth being integrated into routine care as a means to deliver medical, health, and educational services that contribute to improving patient outcomes. The implications of this project related to social change include supporting evidence that positive change is possible when modalities of health care delivery include the patient as part of care, benefiting both patient and provider.

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During transitions of care, great opportunity exists for miscommunication, poor care coordination, adverse events, medication errors and unnecessary healthcare utilization costing billions of dollars annually. An Interprofessional Transitions of Care (IPTC) clinic was developed utilizing a Family Medicine team that included physicians, nurses, a clinical social worker, and a clinical pharmacist. The purpose of this study was to determine if utilization of an IPTC clinic prevented hospital readmission, and to identify factors that predict most benefit from an interprofessional approach to transitions of care. A retrospective chart review of 1,001 patients was completed. A treatment group (TG) of 501 patients were offered IPTC clinic appointments following hospital discharge. A control group (CG) of 500 patients were hospitalized and received traditional follow-up prior to development of the IPTC clinic. Traditional follow-up typically consisted of an automated appointment reminder and a physician office visit. Outcomes assessed included 30-day hospital readmission of TG versus CG, and whether patient characteristics predisposed specific patient groups to attend IPTC appointments or benefit more from IPTC participation. Compared with CG, patients who completed an IPTC appointment were 48% less likely to be readmitted to the hospital within 30 days. Patients with congestive heart failure and cellulitis particularly benefited from IPTC. Telephone contact within two business days of discharge was the greatest predictor of patients attending an IPTC appointment. These results demonstrate that an interprofessional approach to transitions in care effectively addresses this high risk for error and high cost time in the continuum of care.

Recent progress in treatment interventions has resulted in increased survival rates and longevity for diagnosed heart failure patients. However, heart failure still remains one of the leading causes of rehospitalization worldwide, where emergency readmissions continue to be a common occurrence. The multifactorial complexity of heart failure makes clinical judgment difficult and may lead to erroneous discharge prognoses and estimates in recovery trajectories. Recognizing emergency readmissions among heart failure patients who have been discharged is crucial within the critical six-month post-discharge period to proactively address additional support needs. To address the research question, “To what extent can machine learning models predict emergency readmissions in Chinese heart failure patients within six months post-discharge?”, this paper uses electronic health records obtained from a single healthcare center in China, containing 2,008 validated heart failure patients. This study adopts an experimental research methodology, where four machine learning models are developed to explore the research question. To ensure robustness, 10-fold cross-validation with stratified sampling and a two-step feature selection process is performed in addition to evaluation through metrics such as the area under the receiving operating curve and F1 Score. The findings indicate only modest predictive capability among the classifiers in the validation cohort. The best-achieved area under the receiving operating curve and F1 Score are obtained from separate classifiers with scores of 0.682 and 0.577, respectively. The findings provide valuable insights into future research on the effectiveness of ML-based prediction models for emergency readmission in Chinese heart failure patients.

Background: Patients with left ventricular ejection fractions between 40% and 49% either discovered de novo, having declined from ≥50%, or improved from <40% have been described as heart failure (HF) with mid-range ejection fraction (HFmrEF). Though clinical signs and symptoms are similar to other phenotypes, possible prognostic differences and therapeutic responses reinforce the need for further understanding of patients’ characteristics especially in a rural community based population. The purpose of this study is to evaluate the clinical characteristics, comorbidities and prognosis of a rural patient population with HFmrEF. Materials and Methods: We queried the electronic medical record from a community based university practice for all patients with a HF diagnosis. We included only those patients with >3 months follow-up and interpretable Doppler echocardiograms. We recorded demographic, Doppler-echo, and outcome variables (up to 2,083 days). Results: There were 633 HF patients: 42.4% with preserved ejection fraction (HFpEF, EF ≥50%), 36.4% with HFmrEF, and 21.0% with reduced ejection fraction (HFrEF, EF <40%). HFmrEF patients were older, had greater coronary disease prevalence, lower systolic blood pressure, elevated brain natriuretic peptide, lower hemoglobin, and higher creatinine than HFpEF. All-cause mortality was intermediate between HFrEF and HFpEF but was not significantly different. Landmark analysis revealed a trend toward greater second readmission in HFmrEF as compared to HFpEF (hazard ratio: 1.43 [0.96-2.14],P = 0.0767). Conclusions: Rural patients with HFmrEF without an ambulatory HF clinic represent a higher percentage of HF patients than previously reported with greater coronary disease prevalence with comparable readmission rates and nonsignificantly different all-cause mortality.

Indiana University-Purdue University Indianapolis (IUPUI) / Postdischarge follow-up appointments are widely thought to improve the safety of transition for patients moving from the hospital to home. They provide an opportunity for outpatient primary care providers to detect problems or failures of postdischarge care. Readmissions can be used to reflect the quality of postdischarge or transitional care. This study evaluated whether patients with an outpatient follow-up appointment scheduled with their primary care provider within five calendar days of discharge had fewer 30-day readmissions than those patients who had appointments scheduled six days or longer from discharge. No difference in readmission rate was detected between the two groups.

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