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Aim: This study explores the potential benefit of combining clinicians' risk assessments and the automated 30‐day readmission prediction model. Background: Automated readmission prediction models based on electronic health records are increasingly applied as part of prevention efforts, but their accuracy is moderate. Methods: This prospective multisource study was based on self‐reported surveys of clinicians and data from electronic health records. The survey was performed at 15 internal medicine wards of three general Clalit hospitals between May 2016 and June 2017. We examined the degree of concordance between the Preadmission Readmission Detection Model, clinicians' readmission risk classification and the likelihood of actual readmission. Decision trees were developed to classify patients by readmission risk. Results: A total of 694 surveys were collected for 371 patients. The disagreement between clinicians' risk assessment and the model was 34.5% for nurses and 33.5% for physicians. The decision tree algorithms identified 22% and 9% (based on nurses and physicians, respectively) of the model's low–medium‐risk patients as high risk (accuracy 0.8 and 0.76, respectively). Conclusions: Combining the Readmission Model with clinical insight improves the ability to identify high‐risk elderly patients. Implications for Nursing Management: This study provides algorithms for the decision‐making process for selecting high‐risk readmission patients based on nurses' evaluations. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to test the feasibility of a new model of health care designed to improve the quality of life and the efficiency of resource use for older adults with multimorbidity. Design and Methods: Guided Care enhances primary care by infusing the operative principles of seven chronic care innovations: disease management, self-management, case management, lifestyle modification, transitional care, caregiver education and support, and geriatric evaluation and management. To practice Guided Care, a registered nurse completes an educational program and uses a customized electronic health record in working with two to five primary care physicians to meet the health care needs of 50 to 60 older patients with multimorbidity. For each patient, the nurse performs a standardized comprehensive home assessment and then collaborates with the physician, the patient, and the caregiver to create two comprehensive, evidence-based management plans: a Care Guide for health care professionals, and an Action Plan for the patient and caregiver. Based in the primary care office, the nurse then regularly monitors the patient's chronic conditions, coaches the patient in self-management, coordinates the efforts of all involved health care professionals, smoothes the patient's transitions between sites of care, provides education and support for family caregivers, and facilitates access to community resources. Results: A 1-year pilot test in a community-based primary care practice suggested that Guided Care is feasible and acceptable to physicians, patients, and caregivers. Implications: If successful in a controlled trial, Guided Care could improve the quality of life and efficiency of health care for older adults with multimorbidity. Key Words: Chronic disease, Nursing, Primary care, Multimorbidity

Objective Vocational rehabilitation for people with severe mental illness (SMI) has many benefits. Among the existing models, supported employment has consistently shown to have better impact on vocational outcomes while the findings on non-vocational outcomes are inconsistent. One source of variation with regard to non-vocational outcomes could be related to differences between consumers' self-reports and the providers' point of view. Design A cross-sectional study of people with SMI consuming three different vocational services and their service providers. Setting Data were collected as part of the Israeli Psychiatric Rehabilitation Patient Reported Outcome Measurement project. Participants The current data is based on 3666 pairs of people with SMI consuming vocational services and their service providers. Interventions Vocational services included supported employment, sheltered workshops and vocational support centers. Main Outcome Measures The consumers-filled self-report questionnaires, which consisted of the following patient-reported outcome measurements (PROMs): quality of life, functioning and illness management. Primary professional providers were given instruments that mirrored the ones designed for self-report. Results According to providers' ratings, supported employment was associated with higher functioning (F  = 78.6, P  < 0.001) and illness management (F  = 33.0, P  < 0.001) compared to other vocational services. PROMs revealed that supported employment was associated with higher functioning only (F  = 31.5, P  < 0.001). Consumers rated themselves higher compared to providers on all measures. Conclusions This study provides a deeper insight into non-vocational outcomes of people with SMI participating in vocational services and suggests differences in perspectives between consumers and providers with regard to outcome measures. [ABSTRACT FROM AUTHOR]

Objective Persons with serious mental illnesses are at increased risk for co-occurring physical comorbidities. Patient-reported outcome measures are increasingly used in routine assessments of persons with serious mental illnesses, yet the relation of patient-reported outcome measures to physical health outcomes has not been comprehensively investigated. We examined the association between patient-reported outcome measures and self-reported physical health at 1-year follow-up. Design A retrospective cohort study. Setting Data were collected as part of the Israeli Psychiatric Rehabilitation Patient-Reported Outcome Measurement program in Israel. Participants A total of 2581 psychiatric rehabilitation service users assessed between April 2013 and January 2016. Main Outcome Measures Self-reports on two consecutive years of physical health dichotomized as poor versus good. Results More than one-third of participants reported having poor physical health. Multivariate regression analysis showed that quality of life (odds ratio [OR] = 0.71; 95% confidence interval [CI]: 0.60–0.84) and lack of effect of symptoms on functioning (OR = 0.81; 95%CI: 0.74–0.89) predict subsequent physical health, controlling for all other factors. Compared to a multivariate model with personal characteristics and self-reports on physical health at baseline (Model A), the model which also included patient-reported outcome measures (Model B) showed slightly better discrimination (c-statistic: 0.74 vs. 0.76, respectively). Conclusions These results suggest that patient-reported outcome measures contribute to the prediction of poor physical health and thus can be useful as an early screening tool for people with serious mental illnesses living in the community, who are at risk of physical health problems. [ABSTRACT FROM AUTHOR]

Reasons why care does not conform to single-disease guideline recommendations for multimorbid patients have not been systematically measured in practice. Using a mixed methods approach, we identified and quantified types of reasons why care deviates from nine sets of disease guideline recommendations for multimorbid patients. Utilizing a focus group concept mapping technique, we built on a categorization of reasons explaining guideline deviation, and surveyed treating nurses about these reasons for patients' specific care processes. Directed content analysis was conducted to classify the responses into reasons categories. Of 4,386 guideline-recommended care processes evaluated, 920 were not guideline-concordant (944 reasons). Three broad categories of reasons and 18 specific reasons were identified: Biomedical-related occurred 35.2% of the time, patient personal-related (30.4%), context-related (18.4%), and unknown (16.0%). Patient- and context-related factors are prevalent drivers for guideline deviation in multimorbidity, demonstrating that patient-centered aspects are as much a part of care decisions as biomedical aspects. [ABSTRACT FROM AUTHOR]

Abstract Readmission reduction is at the focus of health care systems worldwide in efforts to improve efficiency across care settings. Yet, setting targets for readmission reduction is complicated due to inconsistencies in evidence pointing to effective organization-wide interventions and because of inverse incentives (such as maintaining high occupancy rates). Nonetheless, readmission reduction is one of the few quality measures that, if implemented properly, can serve as a catalyst for system integration. Appropriate mechanisms should be applied to hospitals as well as ambulatory settings to ensure that accountability is assigned to all stakeholders.

Background The ability to accurately detect differential resource use between persons of different socioeconomic status relies on the accuracy of health-needs adjustment measures. This study tests different approaches to morbidity adjustment in explanation of health care utilization inequity. Methods A representative sample was selected of 10 percent (~270,000) adult enrolees of Clalit Health Services, Israel's largest health care organization. The Johns-Hopkins University Adjusted Clinical Groups® were used to assess each person's overall morbidity burden based on one year's (2009) diagnostic information. The odds of above average health care resource use (primary care visits, specialty visits, diagnostic tests, or hospitalizations) were tested using multivariate logistic regression models, separately adjusting for levels of health-need using data on age and gender, comorbidity (using the Charlson Comorbidity Index), or morbidity burden (using the Adjusted Clinical Groups). Model fit was assessed using tests of the Area Under the Receiver Operating Characteristics Curve and the Akaike Information Criteria. Results Low socioeconomic status was associated with higher morbidity burden (1.5-fold difference). Adjusting for health needs using age and gender or the Charlson index, persons of low socioeconomic status had greater odds of above average resource use for all types of services examined (primary care and specialist visits, diagnostic tests, or hospitalizations). In contrast, after adjustment for overall morbidity burden (using Adjusted Clinical Groups), low socioeconomic status was no longer associated with greater odds of specialty care or diagnostic tests (OR: 0.95, CI: 0.94-0.99; and OR: 0.91, CI: 0.86-0.96, for specialty visits and diagnostic respectively). Tests of model fit showed that adjustment using the comprehensive morbidity burden measure provided a better fit than age and gender or the Charlson Index. Conclusions Identification of socioeconomic differences in health care utilization is an important step in disparity reduction efforts. Adjustment for health-needs using a comprehensive morbidity burden diagnoses-based measure, this study showed relative underutilization in use of specialist and diagnostic services, and thus allowed for identification of inequity in health resources use, which could not be detected with less comprehensive forms of health-needs adjustments.

Aims: To (a) assess patients' interest and perceived capability of participating in haemodialysis; (b) assess nurses' perceptions of patients' interest and perceived capability of participating in haemodialysis; and (c) examine associations between patient characteristics and interest and perceived capability of performing haemodialysis self‐care. Design: Cross‐sectional, questionnaire‐based study. Methods: Data were collected from dialysis patients and their nurses between October 2018–May 2019. Patients' interest and perceived capability of participation were assessed by a 10‐item Likert‐type scale developed and tested for this study, with responses ranging from 1 (not interested/no perceived capability) to 5 (already doing task independently). Multivariate linear regression was used to assess the relationship between patient characteristics, including age, sex, education level and severity of illness to ratings of activation level and haemodialysis self‐care scale scores. The STROBE checklist was used as a guideline for this study. Results: Ninety‐one patients and 31 nurses participated. Overall, patients expressed interest (2.43 ± 0.93) and perceived themselves capable (2.34 ± 0.9) of participating in various haemodialysis‐related tasks. Nurses assessed lower interest (2.19 ± 0.77) than patients, but similar average capability (2.31 ± 0.8). Both greater interest and perceived capability were correlated with more years of education and higher patient activation; additionally, interest was associated with disease severity and perceived capability was associated with age. Conclusion: Haemodialysis patients are interested and perceive themselves capable of participating in the tasks involved in dialysis care. Nurses underestimate patient interest in participation. Relevance to clinical practice: Self‐care behaviours among haemodialysis patients are important, as they may affect quality of life and survival. Determining interest and perceived capability of participation is a first step towards evaluating the feasibility of self‐care in a supervised haemodialysis setting. [ABSTRACT FROM AUTHOR]

Self‐management, or self‐care, by individuals and/or families is a critical element in chronic illness management as more care shifts to the home setting. Mobile device‐enhanced health care, or mHealth, is being touted as a means to support self‐care. Previous mHealth reviews examined the effect of mHealth on patient outcomes, however, none used a theoretical lens to examine the interventions themselves. The aims of this integrative review were to examine recent (e.g., last 10 years) chronic illness mHealth empiric studies and (1) categorize self‐care behaviors engaged in the intervention according to the Middle‐Range Theory of Self‐care of Chronic Illness, and (2) conduct an analysis of gaps in self‐care theory domains and behaviors utilized. Methods included: (1) Best practice study identification, collection, and data extraction procedures and (2) realist synthesis techniques for within and across case analysis. From a pool of 652 records, 33 primarily North American clinical trials, published between 2010 and 2019 were examined. Most mHealth interventions used apps, clinician contact, and behavioral prompts with some wireless devices. Examination found self‐care maintenance behaviors were supported in most (n = 30) trials whereas self‐care monitoring (n = 12) and self‐care management behaviors (n = 8) were less so. Few trials (n = 2) targeted all three domains. Investigation of specific behaviors uncovered an overexamination of physical activity and diet behaviors and an underexamination of equally important behaviors. By examining chronic illness mHealth interventions using a theoretical lens we have categorized current interventions, conducted a gap analysis uncovering areas for future study, and made recommendations to move the science forward. [ABSTRACT FROM AUTHOR]

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Objective: Vocational rehabilitation for people with severe mental illness (SMI) has many benefits. Among the existing models, supported employment has consistently shown to have better impact on vocational outcomes while the findings on non-vocational outcomes are inconsistent. One source of variation with regard to non-vocational outcomes could be related to differences between consumers' self-reports and the providers' point of view.Design: A cross-sectional study of people with SMI consuming three different vocational services and their service providers.Setting: Data were collected as part of the Israeli Psychiatric Rehabilitation Patient Reported Outcome Measurement project.Participants: The current data is based on 3666 pairs of people with SMI consuming vocational services and their service providers.Interventions: Vocational services included supported employment, sheltered workshops and vocational support centers.Main Outcome Measures: The consumers-filled self-report questionnaires, which consisted of the following patient-reported outcome measurements (PROMs): quality of life, functioning and illness management. Primary professional providers were given instruments that mirrored the ones designed for self-report.Results: According to providers' ratings, supported employment was associated with higher functioning (F = 78.6, P < 0.001) and illness management (F = 33.0, P < 0.001) compared to other vocational services. PROMs revealed that supported employment was associated with higher functioning only (F = 31.5, P < 0.001). Consumers rated themselves higher compared to providers on all measures.Conclusions: This study provides a deeper insight into non-vocational outcomes of people with SMI participating in vocational services and suggests differences in perspectives between consumers and providers with regard to outcome measures. [ABSTRACT FROM AUTHOR]

Objective: Persons with serious mental illnesses are at increased risk for co-occurring physical comorbidities. Patient-reported outcome measures are increasingly used in routine assessments of persons with serious mental illnesses, yet the relation of patient-reported outcome measures to physical health outcomes has not been comprehensively investigated. We examined the association between patient-reported outcome measures and self-reported physical health at 1-year follow-up.Design: A retrospective cohort study.Setting: Data were collected as part of the Israeli Psychiatric Rehabilitation Patient-Reported Outcome Measurement program in Israel.Participants: A total of 2581 psychiatric rehabilitation service users assessed between April 2013 and January 2016.Main Outcome Measures: Self-reports on two consecutive years of physical health dichotomized as poor versus good.Results: More than one-third of participants reported having poor physical health. Multivariate regression analysis showed that quality of life (odds ratio [OR] = 0.71; 95% confidence interval [CI]: 0.60-0.84) and lack of effect of symptoms on functioning (OR = 0.81; 95%CI: 0.74-0.89) predict subsequent physical health, controlling for all other factors. Compared to a multivariate model with personal characteristics and self-reports on physical health at baseline (Model A), the model which also included patient-reported outcome measures (Model B) showed slightly better discrimination (c-statistic: 0.74 vs. 0.76, respectively).Conclusions: These results suggest that patient-reported outcome measures contribute to the prediction of poor physical health and thus can be useful as an early screening tool for people with serious mental illnesses living in the community, who are at risk of physical health problems. [ABSTRACT FROM AUTHOR]