LACE Score-Based Risk Management Tool for Long-Term Home Care Patients: A Proof-of-Concept Study in Taiwan

The design of studies testing the effectiveness of risk-guided care has many challenges: a scoping review addressing key considerations

OBJECTIVES

Studies evaluating the effectiveness of care based on patients' risk of adverse outcomes (risk-guided care) use a variety of study designs. In this scoping review, using examples, we review characteristics of relevant studies and present key design features to optimize the trustworthiness of results.

STUDY DESIGN AND SETTING

We searched five online databases for studies evaluating the effect of risk-guided care among adults on clinical outcomes, process, or cost. Pairs of reviewers independently performed screening and data abstraction. We descriptively summarized the study design and characteristics.

RESULTS

Among 14,561 hits, we identified 116 eligible studies. Study designs included randomized controlled trials (RCTs), post hoc analysis of RCTs, and retrospective or prospective cohort studies. Challenges and sources of bias in the design included limited performance of predictive models, contamination, inadequacy to address the credibility of subgroup effects, absence of differences in care across risk strata, reporting only process measures as opposed to clinical outcomes, and failure to report benefits and harms.

CONCLUSION

To assess the benefit of risk-guided care, RCTs provide the most trustworthy evidence. Observational studies offer an alternative but are hampered by confounding and other limitations. Reaching valid conclusions when testing risk-guided care requires addressing the challenges identified in our review.

Advances in Population-Based Healthcare Research: From Measures to Evidence

Whether "population health" encompasses a concept of health or a field of study of health determinants is not yet defined, though the term is widely used in healthcare and research worldwide [...].

Analysis of Older Adults under Home Care in Taiwan's Ageing Society

As of January 2022, 16.91% of Taiwan's population was over the age of 65, and a 2017 study indicated that 94.2% of patients who required long-term care in Taiwan received home care. This study produced a "post-home care patient information survey" to understand the characteristics of home care patients and the volume and results of home care and investigate the relationships between them. Different diagnoses were found to have no significant effect on the volume or results of home care. Positive correlations were found between the services patients required and the volume of home care and specific results. Volume and specific results were also positively correlated. The termination of home care was primarily due to medical needs (98.6%). As the Taiwanese population ages, home care must be improved, and the conditions for which patients can receive home care should be expanded. Care services should replace diagnoses in determining benefit standards for home care payments.

Strengths, Weaknesses, Threats, and Opportunities a Pediatric Home Care Program in Covid 19 Virus Pandemic: A Qualitative Study

Covid 19 has made a huge difference in all aspects of life, especially in care and treatment. Hospitalization is limited because of infected family members and fear of getting Covid 19 has limited. The purpose of this study is to analyze the existing conditions based on the SWOT analysis for the home care program for children in Coronavirus crisis. This study is a qualitative study with a conventional content analysis approach. Participants were 18 nurses, physicians, and faculty members, selected based on their willingness to participate in the study and through purposeful sampling. Two specialized panels and 10 presence and in-presence interview sessions were held to collect data. Then, the data were analyzed using SWOT analysis. Four main categories were emerged of the study including: (a) need for a legal protocol, (b) mutual fear of Covid-19, (c) self-responsibility in Corona, and (d) team working approach in the program development. In addition, solutions based on the SWOT analytical were suggested. The results of the study showed that it is necessary to develop a formal protocol, along with self-responsibility, and a program based on the needs of the community and the Covid crisis incorporating the team opinion.

Development and validation of an interpretable machine learning scoring tool for estimating time to emergency readmissions

BACKGROUND

Emergency readmission poses an additional burden on both patients and healthcare systems. Risk stratification is the first step of transitional care interventions targeted at reducing readmission. To accurately predict the short- and intermediate-term risks of readmission and provide information for further temporal risk stratification, we developed and validated an interpretable machine learning risk scoring system.

METHODS

In this retrospective study, all emergency admission episodes from January 1st 2009 to December 31st 2016 at a tertiary hospital in Singapore were assessed. The primary outcome was time to emergency readmission within 90 days post discharge. The Score for Emergency ReAdmission Prediction (SERAP) tool was derived via an interpretable machine learning-based system for time-to-event outcomes. SERAP is six-variable survival score, and takes the number of emergency admissions last year, age, history of malignancy, history of renal diseases, serum creatinine level, and serum albumin level during index admission into consideration.

FINDINGS

A total of 293,589 ED admission episodes were finally included in the whole cohort. Among them, 203,748 episodes were included in the training cohort, 50,937 episodes in the validation cohort, and 38,904 in the testing cohort. Readmission within 90 days was documented in 80,213 (27.3%) episodes, with a median time to emergency readmission of 22 days (Interquartile range: 8-47). For different time points, the readmission rates observed in the whole cohort were 6.7% at 7 days, 10.6% at 14 days, 13.6% at 21 days, 16.4% at 30 days, and 23.0% at 60 days. In the testing cohort, the SERAP achieved an integrated area under the curve of 0.737 (95% confidence interval: 0.730-0.743). For a specific 30-day readmission prediction, SERAP outperformed the LACE index (Length of stay, Acuity of admission, Charlson comorbidity index, and Emergency department visits in past six months) and the HOSPITAL score (Hemoglobin at discharge, discharge from an Oncology service, Sodium level at discharge, Procedure during the index admission, Index Type of admission, number of Admissions during the last 12 months, and Length of stay). Besides 30-day readmission, SERAP can predict readmission rates at any time point during the 90-day period.

INTERPRETATION

Better performance in risk prediction was achieved by the SERAP than other existing scores, and accurate information about time to emergency readmission was generated for further temporal risk stratification and clinical decision-making. In the future, external validation studies are needed to evaluate the SERAP at different settings and assess their real-world performance.

FUNDING

This study was supported by the Singapore National Medical Research Council under the PULSES Center Grant, and Duke-NUS Medical School.