Population Health Informatics: Challenges, Opportunities, and Case Studies

Dr. Hadi Kharrazi’s presentation focused on “population health informatics,” a growing field of research in the informatics community. He introduced his center at Johns Hopkins; provided a working definition for population health informatics and discussed its top challenges and opportunities. Dr. Kharrazi presented a number of research projects to demonstrate the importance and potential long-term benefits of population health informatics in the overall field of healthcare and beyond. The objectives of the presentation were: Define population health informatics and describe its role in the context of value-based care Explain the effect of different data types, sources and qualities in population stratification and risk prediction Discuss the challenges and opportunities of population health informatics Dr. Hadi Kharrazi is an assistant professor of health policy and management at the Johns Hopkins School of Public Health, and the research director of the Center for Population Health IT. His research focuses on the application of informatics in risk stratification, and the effect of data type and quality in predicting utilization. Dr. Hadi Kharrazi is an assistant professor of health policy and management at the Johns Hopkins School of Public Health, and the research director of the Center for Population Health IT. His research focuses on the application of informatics in risk stratification, and the effect of data type and quality in predicting utilization. In addition, Dr. Kharrazi has developed more than a dozen courses in health informatics and is currently the director of the DrPH Informatics track program at the Johns Hopkins School of Public Health, and the co-director of the PhD program in Health Informatics at the Johns Hopkins School of Medicine’s Division of Health Sciences Informatics. He is a senior clinical informatician with specialization in EHR platforms, Health Information Exchange (HIE), and Clinical Decision Support Systems (CDSS). Dr. Kharrazi\u27s longterm research interest is in contextualizing CDSS in PHI platforms to be utilized at different HIT levels of managed care such as EHR platforms or consumer health informatics solutions. Presentation: 51:19 Note: PowerPoint presentation slide deck is at the bottom of the page

Facilitators and Barriers to Adopting Robotic-Assisted Surgery: Contextualizing the Unified Theory of Acceptance and Use of Technology

Robotic-assisted surgical techniques are not yet well established among surgeon practice groups beyond a few surgical subspecialties. To help identify the facilitators and barriers to their adoption, this belief-elicitation study contextualized and supplemented constructs of the unified theory of acceptance and use of technology (UTAUT) in robotic-assisted surgery. Semi-structured individual interviews were conducted with 21 surgeons comprising two groups: users and nonusers. The main facilitators to adoption were Perceived Usefulness and Facilitating Conditions among both users and nonusers, followed by Attitude Toward Using Technology among users and Extrinsic Motivation among nonusers. The three main barriers to adoption for both users and nonusers were Perceived Ease of Use and Complexity, Perceived Usefulness, and Perceived Behavioral Control. This study's findings can assist surgeons, hospital and medical school administrators, and other policy makers on the proper adoption of robotic-assisted surgery and can guide future research on the development of theories and framing of hypotheses

What’s Past Is Prologue: A Scoping Review of Recent Public Health and Global Health Informatics Literature

Objective: To categorize and describe the public health informatics (PHI) and global health informatics (GHI) literature between 2012 and 2014. Methods: We conducted a semi-systematic review of articles published between January 2012 and September 2014 where information and communications technologies (ICT) was a primary subject of the study or a main component of the study methodology. Additional inclusion and exclusion criteria were used to filter PHI and GHI articles from the larger biomedical informatics domain. Articles were identified using MEDLINE as well as personal bibliographies from members of the American Medical Informatics Association PHI and GHI working groups. Results: A total of 85 PHI articles and 282 GHI articles were identified. While systems in PHI continue to support surveillance activities, we identified a shift towards support for prevention, environmental health, and public health care services. Furthermore, articles from the U.S. reveal a shift towards PHI applications at state and local levels. GHI articles focused on telemedicine, mHealth and eHealth applications. The development of adequate infrastructure to support ICT remains a challenge, although we observed a small but growing set of articles that measure the impact of ICT on clinical outcomes. Discussion: There is evidence of growth with respect to both implementation of information systems within the public health enterprise as well as a widening of scope within each informatics discipline. Yet the articles also illuminate the need for more primary research studies on what works and what does not as both searches yielded small numbers of primary, empirical articles. Conclusion: While the body of knowledge around PHI and GHI continues to mature, additional studies of higher quality are needed to generate the robust evidence base needed to support continued investment in eHealth by governmental health agencies

A Fuzzy Approach Model for Uncovering Hidden Latent Semantic Structure in Medical Text Collections

One of the challenges for text analysis in the medical domain including the clinical notes and research papers is analyzing large-scale medical documents. As a consequence, finding relevant documents has become more difficult and previous work has also shown unique problems of medical documents. The themes in documents help to retrieve documents on the same topic with and without a query. One of the popular methods to retrieve information based on discovering the themes in the documents is topic modeling. In this paper we describe a novel approach in topic modeling, FATM, using fuzzy clustering. To assess the value of FATM, we experiment with two text datasets of medical documents. The quantitative evaluation carried out through log-likelihood on held-out data shows that FATM produces superior performance to LDA. This research contributes to the emerging field of understanding the characteristics of the medical documents and how to account for them in text mining

Identifying Predictors of Nursing Home Admission by Using Electronic Health Records and Administrative Data: Scoping Review

Abstract BackgroundAmong older adults, nursing home admissions (NHAs) are considered a significant adverse outcome and have been extensively studied. Although the volume and significance of electronic data sources are expanding, it is unclear what predictors of NHA have been systematically identified in the literature via electronic health records (EHRs) and administrative data. ObjectiveThis study synthesizes findings of recent literature on identifying predictors of NHA that are collected from administrative data or EHRs. MethodsThe PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines were used for study selection. The PubMed and CINAHL databases were used to retrieve the studies. Articles published between January 1, 2012, and March 31, 2023, were included. ResultsA total of 34 papers were selected for final inclusion in this review. In addition to NHA, all-cause mortality, hospitalization, and rehospitalization were frequently used as outcome measures. The most frequently used models for predicting NHAs were Cox proportional hazards models (studies: n=12, 35%), logistic regression models (studies: n=9, 26%), and a combination of both (studies: n=6, 18%). Several predictors were used in the NHA prediction models, which were further categorized into sociodemographic, caregiver support, health status, health use, and social service use factors. Only 5 (15%) studies used a validated frailty measure in their NHA prediction models. ConclusionsNHA prediction tools based on EHRs or administrative data may assist clinicians, patients, and policy makers in making informed decisions and allocating public health resources. More research is needed to assess the value of various predictors and data sources in predicting NHAs and validating NHA prediction models externally