Does scoring patient complexity using COMPRI predict the length of hospital stay? A multicentre case-control study in Japan

Association of Timely Outpatient Follow-Up and Readmission Risk in a Mobile Integrated Health Program

The objective was to identify medical conditions associated with 30-day readmission, determine patient characteristics for which outpatient follow-up is most associated with reduced readmission, and evaluate how readmission risk changes with time to outpatient follow-up within a mobile integrated health-community paramedicine (MIH-CP) program. This retrospective observational study used data from 1,118 adult patient enrollments in a MIH-CP program operating in Baltimore, Maryland, from May 14, 2018, to December 21, 2021. Bivariate analysis identified chronic disease exacerbations associated with higher 30-day readmission risk. Kaplan-Meier curves and Cox proportional hazard regressions were used to measure how hazard of readmission changes with outpatient follow-up and how that association may vary with other factors. Receiver operating characteristic analysis was used to evaluate how well time to follow-up could predict 30-day readmission. Timely outpatient follow-up was associated with a significant reduction in hazard of readmission for patients aged 50 and younger and for patients with fewer than 5 social determinants of health needs identified. No significant association between readmission and specific chronic disease exacerbations was observed. An optimal follow-up time frame to reduce readmissions could not be identified. Timely outpatient follow-up may be effective for reducing readmissions in younger patients and patients who are less socially complex. Programs and policies aiming to reduce 30-day readmissions may have more success by expanding efforts to include these patients.

Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis

Background

Unwarranted extended length of stay (LOS) increases the risk of hospital-acquired complications, morbidity, and all-cause mortality and needs to be recognized and addressed proactively.

Objective

This systematic review aimed to identify validated prediction variables and methods used in tools that predict the risk of prolonged LOS in all hospital admissions and specifically General Medicine (GenMed) admissions.

Method

LOS prediction tools published since 2010 were identified in five major research databases. The main outcomes were model performance metrics, prediction variables, and level of validation. Meta-analysis was completed for validated models. The risk of bias was assessed using the PROBAST checklist.

Results

Overall, 25 all admission studies and 14 GenMed studies were identified. Statistical and machine learning methods were used almost equally in both groups. Calibration metrics were reported infrequently, with only 2 of 39 studies performing external validation. Meta-analysis of all admissions validation studies revealed a 95% prediction interval for theta of 0.596 to 0.798 for the area under the curve. Important predictor categories were co-morbidity diagnoses and illness severity risk scores, demographics, and admission characteristics. Overall study quality was deemed low due to poor data processing and analysis reporting.

Conclusion

To the best of our knowledge, this is the first systematic review assessing the quality of risk prediction models for hospital LOS in GenMed and all admissions groups. Notably, both machine learning and statistical modeling demonstrated good predictive performance, but models were infrequently externally validated and had poor overall study quality. Moving forward, a focus on quality methods by the adoption of existing guidelines and external validation is needed before clinical application.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021272198.

Frequency of Difficult Patient Encounters in a Japanese University Hospital and Community Hospitals: A Cross-sectional Study

Objective Difficult patient encounters (DPEs) are defined as encounters with patients causing strong negative feelings in physicians. In primary care settings, DPEs account for approximately 15% of visits among outpatients. To our knowledge, this is the first epidemiological study of DPEs in Japan. Methods We conducted a survey of 8 physicians (5.0±2 years of clinical experience) who examined first-visit patients ≥15 years old with clinical symptoms at the Department of General Medicine in Chiba University Hospital and 4 community hospitals over a 2-month period since December 2015. Materials We evaluated 10-Item Difficult Doctor-Patient Relationship Questionnaire (DDPRQ-10) scores (DPE ≥31 points; non-DPE ≤30 points) and patient age, sex, and presence of psychological or social problems. Results The valid response rate was 98.9% (94/95) and 98.4% (189/192) in the university and community hospitals, respectively. The percentage of DPEs was 39.8% (37/93) and 15.0% (26/173) in the university and community hospitals, respectively; the percentage of DPEs was significantly higher at the university hospital than at the community hospitals (p<0.001). The proportion of patients with psychosocial problems was significantly higher in the DPE group than in the non-DPE group (93.7% vs. 40.4%, p<0.001). Conclusion Our findings were similar to those reported in primary care settings in other countries in community hospital outpatient and general internal medicine departments, where patients are mostly non-referrals, although the values were higher in university hospital general medicine departments, where patients were mostly referrals. Patients involved in DPEs have a high rate of psychological and social problems.

Evaluation of Complexity Measurement Tools for Correlations with Health-Related Outcomes, Health Care Costs and Impacts on Healthcare Providers: A Scoping Review

Various tools to measure patient complexity have been developed. Primary care physicians often deal with patient complexity. However, their usefulness in primary care settings is unclear. This study explored complexity measurement tools in general adult and patient populations to investigate the correlations between patient complexity and outcomes, including health-related patient outcomes, healthcare costs, and impacts on healthcare providers. We used a five-stage scoping review framework, searching MEDLINE and CINAHL, including reference lists of identified studies. A total of 21 patient complexity management tools were found. Twenty-five studies examined the correlation between patient complexity and health-related patient outcomes, two examined healthcare costs, and one assessed impacts on healthcare providers. No studies have considered sharing information or action plans with multidisciplinary teams while measuring outcomes for complex patients. Of the tools, eleven used face-to-face interviews, seven extracted data from medical records, and three used self-assessments. The evidence of correlations between patient complexity and outcomes was insufficient for clinical implementation. Self-assessment tools might be convenient for conducting further studies. A multidisciplinary approach is essential to develop effective intervention protocols. Further research is required to determine these correlations in primary care settings.