Holland, Diane E.

Background: Electronic health records (EHRs) provide a clinical view of patient health. EHR data are becoming available in large data sets and enabling research that will transform the landscape of healthcare research. Methods are needed to incorporate wellbeing dimensions and strengths in large data sets. The purpose of this study was to examine the potential alignment of the Wellbeing Model with a clinical interface terminology standard, the Omaha System, for documenting wellbeing assessments. Objective: To map the Omaha System and Wellbeing Model for use in a clinical EHR wellbeing assessment and to evaluate the feasibility of describing strengths and needs of seniors generated through this assessment. Methods: The Wellbeing Model and Omaha System were mapped using concept mapping techniques. Based on this mapping, a wellbeing assessment was developed and implemented within a clinical EHR. Strengths indicators and signs/symptoms data for 5 seniors living in a residential community were abstracted from wellbeing assessments and analyzed using standard descriptive statistics and pattern visualization techniques. Results: Initial mapping agreement was 93.5%, with differences resolved by consensus. Wellbeing data analysis showed seniors had an average of 34.8 (range=22–49) strengths indicators for 22.8 concepts. They had an average of 6.4 (range=4–8) signs/symptoms for an average of 3.2 (range=2–5) concepts. The ratio of strengths indicators to signs/symptoms was 6:1 (range 2.8–9.6). Problem concepts with more signs/symptoms had fewer strengths. Conclusion: Together, the Wellbeing Model and the Omaha System have potential to enable a whole-person perspective and enhance the potential for a wellbeing perspective in big data research in healthcare.

AbstractPurposeTo develop and test a decision support tool that identifies patients who would benefit from early consult with discharge planners.Design and MethodsA predictive, correlational design was used with parents/guardians of children (1 month to 18 years; N = 197). Data were collected by interviews and record reviews. Expert consensus determined referral to discharge planning.ResultsMean age was 8.7 years; mean length of stay was 7.5 days. Forty percent (n = 79) were identified for early referral. The variable “substantial post‐acute care needs” had the strongest association with expert consensus (internally validated AUC = 0.79).Practice ImplicationsFindings from this study provide preliminary evidence for a decision support tool to improve the discharge planning process by reducing individual decision‐making variability through systematic matching of patient needs to service delivery.

Health care reform focuses on primary care and development of Health Care Homes to improve patient-centered chronic illness care. This pilot study evaluated a community care team intervention that linked chronically ill older patients, support persons, and nurse care coordinators from a Health Care Home with community resources using an adaptation of the Wraparound process. A pragmatic clinical trial design was used. Patient-centered chronic illness care; physical, mental, and social health; service use; and study feasibility were evaluated. Differences between groups were compared using two-sample t, Wilcoxon rank sum, chi-square, or Fisher’s exact tests. At 3 months, the intervention group reported higher patient-centered chronic illness care (mean total Patient Assessment of Chronic Illness Care change scores were 0.39 for the intervention group and −0.11 for the control group, p = .03). Results indicate that the integrated community care team intervention is a promising strategy to support patient-centered chronic illness care.

We compared the effects of evidence-based disease management guidelines delivered to patients with heart failure and diabetes using three different modalities: in-person visits alone (Control), in-person visits and a telephone intervention (Telephone), and in-person visits and telemonitoring (Telemonitoring). Patients were randomized to the three groups. There were 112 patients in the Control group, 93 in the Telephone group and 98 in the Telemonitoring group. During the first 60 days, 10% of the Control group were rehospitalized, 17% of the Telephone group and 16% of the Telemonitoring group. Having heart failure and receiving more in-person visits were significantly related to readmission and time to readmission. However, after adjusting for diagnosis and visits, the differences between the three groups were non-significant. There was a trend for increased risk of readmission for the Telephone group compared to Control alone ( P = 0.07, risk ratio 2.2, 95% CI: 0.9 to 5.2) and for readmission sooner ( P = 0.02, risk ratio 2.3, 95% CI: 1.2 to 4.6). Patient rehospitalization and emergency department visit rates were lower than the national average, making it difficult to detect a difference between groups. Previous rehospitalization was a consistent predictor of those who were rehospitalized, suggesting that it may be a useful indicator for identifying patients likely to need additional attention.