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

Analytics and Healthcare Costs (A Three Essay Dissertation)

Both literature and practice have looked at different strategies to diminish healthcare associated costs. As an extension to this stream of research, the present three paper dissertation addresses the issue of reducing elevated healthcare costs using analytics. The first paper looks at extending the benefits of auditing algorithms from mere detection of fraudulent providers to maximizing the deterrence from inappropriate behavior. Using the structure of the physicians\u27 network, a new auditing algorithm is developed. Evaluation of the algorithm is performed using an agent-based simulation and an analytical model. A case study is also included to illustrate the application of the algorithm in the warranty domain. The second paper relies on experimental data to build a personalized medical recommender system geared towards re-enforcing price-sensitive prescription behavior. The study analyzes the impact of time pressure, and procedure cost and prescription prevalence/popularity on the physicians\u27 use of the system\u27s recommendations. The third paper investigates the relationship between patients\u27 compliance and healthcare costs. The study includes a survey of the literature along with a longitudinal analysis of patients\u27 data to determine factors leading to patients\u27 non-compliance, and ways to alleviate it

Can Recommender Systems Reduce Healthcare Costs? The Role of Time Pressure and Cost Transparency in Prescription Choice

This paper presents and synthesizes results from three studies (two controlled experiments and one interview) on using recommender systems to reduce healthcare costs at prescription time, while taking time pressure into account. All of our subjects were real practicing physicians, nurse practitioners, or physician assistants. Across these studies, a total of 160 medical practitioners used a system that provides recommendations for medications along with associated cost information. The main finding was a general tendency among practitioners to reduce healthcare costs by prescribing lower cost medications when cost information is provided by a recommender system. The time pressure faced daily by prescribers, however, appears to impact the use of recommendations by nurse practitioners and physician assistants more than it does physicians. These results have significant implications for cost reduction in healthcare and for the design of effective real-time healthcare recommender systems

The Value of Extracting Clinician-Recorded Affect for Advancing Clinical Research on Depression: Proof-of-Concept Study Applying Natural Language Processing to Electronic Health Records

BackgroundAffective characteristics are associated with depression severity, course, and prognosis. Patients’ affect captured by clinicians during sessions may provide a rich source of information that more naturally aligns with the depression course and patient-desired depression outcomes. ObjectiveIn this paper, we propose an information extraction vocabulary used to pilot the feasibility and reliability of identifying clinician-recorded patient affective states in clinical notes from electronic health records. MethodsAffect and mood were annotated in 147 clinical notes of 109 patients by 2 independent coders across 3 pilots. Intercoder discrepancies were settled by a third coder. This reference annotation set was used to test a proof-of-concept natural language processing (NLP) system using a named entity recognition approach. ResultsConcepts were frequently addressed in templated format and free text in clinical notes. Annotated data demonstrated that affective characteristics were identified in 87.8% (129/147) of the notes, while mood was identified in 97.3% (143/147) of the notes. The intercoder reliability was consistently good across the pilots (interannotator agreement [IAA] >70%). The final NLP system showed good reliability with the final reference annotation set (mood IAA=85.8%; affect IAA=80.9%). ConclusionsAffect and mood can be reliably identified in clinician reports and are good targets for NLP. We discuss several next steps to expand on this proof of concept and the value of this research for depression clinical research