Developing a personal health record for community-dwelling older adults and clinicians: technology and content

Research Types and New Trends on the Omaha System Published From 2012 to 2019: A Scoping Review

The Omaha System is a popular and standard term used in community health. This scoping review aimed to update the research types and identify new usage trends for the Omaha System through articles published between 2012 and 2019. The bibliography databases PubMed, CINAHL, Scopus, PsycInfo, Ovid, and ICHUSHI and the Omaha System's Web site were used to search for publications. Research articles published between 2012 and 2019 that included "Omaha System" in the title or abstract and were written in English or Japanese were included in this review. After excluding duplicate articles, 305 articles were screened and 82 were included in our analysis. There was a median of 10.3 articles per year. The percentages for each type of use of the Omaha System to "analyze client problem," "analyze clinical process," "analyze client outcomes," and "advanced classification research" were 18.3%, 12.2%, 23.2%, and 4.9%, respectively. The reclassification of the type "others" (41.5%) included "use the Omaha System data for assessment for other than clients," "use the Omaha System data as structured data," "encode by the Omaha System code," "adopt the OS framework," "clinical information system," and "literature review." This newly reclassified category will help capture future research trends using the Omaha System.

Patient Health Record Systems Scope and Functionalities: Literature Review and Future Directions

BACKGROUND

A new generation of user-centric information systems is emerging in health care as patient health record (PHR) systems. These systems create a platform supporting the new vision of health services that empowers patients and enables patient-provider communication, with the goal of improving health outcomes and reducing costs. This evolution has generated new sets of data and capabilities, providing opportunities and challenges at the user, system, and industry levels.

OBJECTIVE

The objective of our study was to assess PHR data types and functionalities through a review of the literature to inform the health care informatics community, and to provide recommendations for PHR design, research, and practice.

METHODS

We conducted a review of the literature to assess PHR data types and functionalities. We searched PubMed, Embase, and MEDLINE databases from 1966 to 2015 for studies of PHRs, resulting in 1822 articles, from which we selected a total of 106 articles for a detailed review of PHR data content.

RESULTS

We present several key findings related to the scope and functionalities in PHR systems. We also present a functional taxonomy and chronological analysis of PHR data types and functionalities, to improve understanding and provide insights for future directions. Functional taxonomy analysis of the extracted data revealed the presence of new PHR data sources such as tracking devices and data types such as time-series data. Chronological data analysis showed an evolution of PHR system functionalities over time, from simple data access to data modification and, more recently, automated assessment, prediction, and recommendation.

CONCLUSIONS

Efforts are needed to improve (1) PHR data quality through patient-centered user interface design and standardized patient-generated data guidelines, (2) data integrity through consolidation of various types and sources, (3) PHR functionality through application of new data analytics methods, and (4) metrics to evaluate clinical outcomes associated with automated PHR system use, and costs associated with PHR data storage and analytics.

The Gap in Big Data: Getting to Wellbeing, Strengths, and a Whole-person Perspective

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.

Seeing the whole person: feasibility of using the Omaha System to describe strengths of older adults with chronic illness

A promising strategy for enhancing care and self-management of chronic illness is an integrative, whole-person approach that recognizes and values well-being. Assessment tools are needed that will enable health care professionals to perceive patients as whole persons, with strengths as well as problems. The purpose of this study was to examine the feasibility of using a standardized terminology (the Omaha System) to describe strengths of older adults with chronic illness. The Omaha System assessment currently consists of identifying signs/symptoms for 42 health concepts. Researchers mapped self-reported strengths phrases to Omaha System concepts using existing narratives of 32 older adults with 12-15 comorbid conditions. Results demonstrated the feasibility of describing strengths of patients with chronic illness. Exploratory analysis showed that there were 0-9 strengths per patient, with unique strengths profiles for 30 of 32 patients. Given that older adults with multiple chronic illnesses also have strengths that can be classified and quantified using the Omaha System, there is potential to use the Omaha System as a whole-person assessment tool that enables perception of both problems and strengths. Further research is needed to enhance the Omaha System to formally represent strengths-based as well as a problem-focused perspectives.

The digital health divide: evaluating online health information access and use among older adults

OBJECTIVE

Innovations in health information technology (HIT) provide opportunities to reduce health care spending, improve quality of care, and improve health outcomes for older adults. However, concerns relating to older adults' limited access and use of HIT, including use of the Internet for health information, fuel the digital health divide debate. This study evaluated the potential digital health divide in relation to characteristic and belief differences between older adult users and nonusers of online health information sources.

METHODS

A cross-sectional survey design was conducted using a random sample of older adults. A total of 225 older adults (age range = 50-92 years, M = 68.9 years, SD = 10.4) participated in the study.

RESULTS

Seventy-six percent of all respondents had Internet access. Users and nonusers of online health information differed significantly on age (M = 66.29 vs. M = 71.13), education, and previous experience with the health care system. Users and nonusers of online health information also differed significantly on Internet and technology access, however, a large percentage of nonusers had Internet access (56.3%), desktop computers (55.9%), and laptop computers or netbooks (43.2%). Users of online health information had higher mean scores on the Computer Self-Efficacy Measure than nonusers, t(159) = -7.29, p < .0001.

CONCLUSION

This study found significant differences between older adult users and nonusers of online health information. Findings suggest strategies for reducing this divide and implications for health education programs to promote HIT use among older adults.

Exploring the value of clinical data standards to predict hospitalization of home care patients

BACKGROUND

There is a critical need to reduce hospitalizations for Medicare patients and electronic health record (EHR) home care data provide new opportunities to evaluate risk of hospitalization for patients.

OBJECTIVES

The objectives of this study were to 1) develop a measure to predict risk of hospitalization among home care patients, the Hospitalization Risk Score (HRS), and 2) compare it with an existing severity of illness measure, the Charlson Index of Comorbidity (CIC).

METHODS

A convenience sample of clinical data from 14 home care agencies' EHRs, representing 1,643 home care patient episodes was used for the study. The development of the HRS was based on review of the literature, and expert panel evaluation to construct the HRS. Descriptive statistics and generalized linear models were used for comparative analysis; areas under curve (AUC) values were compared for receiver operating curves (ROC), and cut points predicting hospitalization were evaluated.

RESULTS

The HRS for this sample ranged from 0 to 5.6, with a median of 1.25. The CIC for this sample ranged from 0 to 9 and with a median of 0. Nearly three fourths of the sample was hospitalized at an HRS of 2, and a CIC of 1. AUC values for ROC were 0.63 for HRS and 0.59 for the CIC. The ROC curves were significantly different (t = -7.59, p <0.003).

CONCLUSIONS

This preliminary study demonstrates the potential value of the HRS using Omaha System EHR data. There was a statistically significant difference for predicting hospitalization of home care patients with the HRS versus the CIC; however the AUC values for both were low. Continued research is needed to further refine the HRS, determine whether it is more sensitive for particular subgroups of patients, and combine it with additional risk factors in understanding rehospitalization.