Transitions of Care: A Patient-Centered Perspective of Health Information Systems That Support Post-Acute Care

Clark, Scott; Elswick, Shannon; Gabriel, Meghan Hufstader; Gurupur, Varadraj P.; Wiśniewski, Pamela

doi:10.3233/jid-2016-0008

Cited by 12 publications

(9 citation statements)

References 9 publications

(14 reference statements)

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“…Here the author believes that the research conducted in this area of science is still very embryonic in nature and much more needs to be achieved. Additionally, the discussion presented in this article will motivate researchers and developers of technology (45) to seriously consider working on some of the critical problem areas (46) that need to be targeted mentioned in this article.…”

Section: Discussionmentioning

confidence: 99%

Key observations in terms of management of electronic health records from a mHealth perspective

Gurupur¹

2022

mHealth

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The article is a narrative review that briefly describes some of the recent advances in healthcare data management that will have positive effect on mHealth. The advances described in this article are in fact innovation introduced by the author to the field of data management with respect to electronic health records. The research delineated is transdisciplinary in nature and will potentially have positive impact on healthcare outcomes. Also, the article illustrates the necessity for an out of the box thinking approach to improve mHealth while discussing the current impending issues related to data incompleteness of electronic health records and the much-needed decision support systems for mHealth. It is to be noted that most of the electronic health records are now accessed by patients through mobile devices. These mobile devices will run as clients while much of the heavy computing is performed using servers. Here it is important to discuss some of the important technologies and methods used for decision making. The article attempts to present a discussion on how this myriad of intertwining technologies support this decision making with respect to electronic health records. More importantly it is these processes that assist in decision making and efficiency for both mHealth users and providers. In this respect, the article first provides insights on the complexities of decision making involved with electronic health records. This is followed by a discussion on the problem of data incompleteness of electronic health records. Finally, the author provides some insights into the gravity of the problem of data incompleteness in terms of revenue loss/gain for healthcare providers.

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Section: Discussionmentioning

confidence: 99%

Key observations in terms of management of electronic health records from a mHealth perspective

Gurupur¹

2022

mHealth

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“…Furthermore, multivariate modeling techniques, such as multi-level analysis, could help delineate the relative effects of personal and contextual predictors on the rehospitalization of HF patients. By identifying a high-risk patient group and applying relevant human-factor principles as care management enhancers for heart failure or other targeted chronic conditions, the population health management and research could generate systematic information and knowledge about important determinants of health [23] and healthcare differentials [24]. The shared decision-making strategies [25] coupled with the use of artificial intelligence research [26] under the population health management approach could then be developed and implemented to enhance the quality of care and reduce costly readmissions of heart failure.…”

Section: Discussionmentioning

confidence: 99%

A Patient-Centric Care Approach to Facilitate the Design of an Artificial Intelligence Application in Geriatric Care Management of Heart Failure Readmissions

Wan¹

2021

BRCR

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The examination of human factors’ role in moderating medical interventions and hospitalizations and/or rehospitalization of heart failure (HF) patients. Objectives: The primary purpose of this study is two-fold: 1) to show relevant human factors influencing the rehospitalization of persons with heart failure by developing a systematic algorithm generated from the cited randomized trials; and 2) to examine how the self-care principles, such as choice/efficacy, restfulness, healing environment, activity, trust, interpersonal relationships, outlook, and nutrition, may reduce heart failure readmissions. Methods: The meta-analytic approach generated a theoretically relevant and empirically validated self-care management decision support protocol for HF. Statistical modeling of the effects of eight human factors for the reduction of HF readmissions was presented. Findings: The systematic review and meta-analysis approach documents the results of randomized clinical trials that affect heart failure hospitalization by selected human factors. A patient-centered decision support system was developed to facilitate the self-care management of heart failure. Discussion: Our research generates systematic knowledge about the importance of human-factor principles in the provision of geriatric care for heart failure. Using shared decision-making strategies under the population health management approach could enhance the quality of care and reduce costly readmissions of heart failure, particularly for elderly patients.

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“…In addition, healthcare providers can target this group with educational interventions by employing the Knowledge, Motivation, Attitude, Preventive Practice, and Outcomes (KMAP-O) framework to improve their health status, prevent severe adverse outcomes, and reduce their level of healthcare utilization [ 58 , 59 ]. Last, collaborations among different levels of providers (e.g., clinics, hospitals, long-term, and post-acute care organizations) might also facilitate monitoring wearable devices among high-risk DM patients to prevent HG episodes [ 60 ].…”

Section: Discussionmentioning

confidence: 99%

Factors Influencing Variations in Hospitalization for Diabetes with Hypoglycemia

Kattan

Wan

2018

JCM

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Many studies have explored risk factors associated with Hypoglycemia (HG) and examined the variation in healthcare utilization among HG patients. However, most of these studies failed to integrate a comprehensive list of personal risk factors in their investigations. This empirical study employed the Behavioral Model (BM) of health care utilization as a framework to investigate diabetes’ hospitalizations with HG. The national inpatient sample with all non-pregnant adult patients admitted to hospitals’ emergency departments and diagnosed with HG from 2012 to 2014 was used. Personal factors were grouped as predictors of the length of stay and the total charges incurred for hospitalization. High-risk profiles of hospitalized HG patients were identified. The analysis shows the need for care factors are the most influential predictors for lengthy hospitalization. The predisposing factors have a limited influence, while enabling factors influence the variation in hospital total charges. The presence of renal disease and diabetes mellitus (DM) complications played a key role in predicting hospital utilization. Furthermore, age, socio-economic status (SES), and the geographical location of the patients were also found to be vital factors in determining the variability in utilization among HG patients. Findings provide practical applications for targeting the high-risk HG patients for interventions.

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Transitions of Care: A Patient-Centered Perspective of Health Information Systems That Support Post-Acute Care

Cited by 12 publications

References 9 publications

Key observations in terms of management of electronic health records from a mHealth perspective

Key observations in terms of management of electronic health records from a mHealth perspective

A Patient-Centric Care Approach to Facilitate the Design of an Artificial Intelligence Application in Geriatric Care Management of Heart Failure Readmissions

Factors Influencing Variations in Hospitalization for Diabetes with Hypoglycemia

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