A Quantitative Framework for Measuring Personalized Medicine Integration into US Healthcare Delivery Organizations

Agarwal, Arushi; Pritchard, Daryl; Gullett, Laura; Amanti, Kristen Garner; Gustavsen, Gary

doi:10.3390/jpm11030196

Cited by 9 publications

(3 citation statements)

References 10 publications

(11 reference statements)

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“…For these, we conducted abstract screening based on the predefined inclusion criteria, leading to 55 potentially relevant articles being retained. Next, we performed a full-text review to determine eligibility, resulting in 21 papers [3,[13][14][15][16][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56] being included after the screening. A detailed overview of the screening process is shown in Figure 2.…”

Section: Characterizing Digital Maturity In General Practices (Litera...mentioning

confidence: 99%

Big 5 Personality Traits and Individual- and Practice-Related Characteristics as Influencing Factors of Digital Maturity in General Practices: Quantitative Web-Based Survey Study

Weik,

Fehring,

Mortsiefer

et al. 2024

J Med Internet Res

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Background Various studies propose the significance of digital maturity in ensuring effective patient care and enabling improved health outcomes, a successful digital transformation, and optimized service delivery. Although previous research has centered around inpatient health care settings, research on digital maturity in general practices is still in its infancy. Objective As general practitioners (GPs) are the first point of contact for most patients, we aimed to shed light on the pivotal role of GPs’ inherent characteristics, especially their personality, in the digital maturity of general practices. Methods In the first step, we applied a sequential mixed methods approach involving a literature review and expert interviews with GPs to construct the digital maturity scale used in this study. Next, we designed a web-based survey to assess digital maturity on a 5-point Likert-type scale and analyze the relationship with relevant inherent characteristics using ANOVAs and regression analysis. Results Our web-based survey with 219 GPs revealed that digital maturity was overall moderate (mean 3.31, SD 0.64) and substantially associated with several characteristics inherent to the GP. We found differences in overall digital maturity based on GPs’ gender, the expected future use of digital health solutions, the perceived digital affinity of medical assistants, GPs’ level of digital affinity, and GPs’ level of extraversion and neuroticism. In a regression model, a higher expected future use, a higher perceived digital affinity of medical assistants, a higher digital affinity of GPs, and lower neuroticism were substantial predictors of overall digital maturity. Conclusions Our study highlights the impact of GPs’ inherent characteristics, especially their personality, on the digital maturity of general practices. By identifying these inherent influencing factors, our findings support targeted approaches to drive digital maturity in general practice settings.

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Section: Characterizing Digital Maturity In General Practices (Litera...mentioning

confidence: 99%

Big 5 Personality Traits and Individual- and Practice-Related Characteristics as Influencing Factors of Digital Maturity in General Practices: Quantitative Web-Based Survey Study

Weik,

Fehring,

Mortsiefer

et al. 2024

J Med Internet Res

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“…Being such an important problem, the medication is not uniform for all. There are several levels of Diabetes [10][11][12], which are classified by standard organizations like the American Association of Diabetes [13,14].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Evolutionary Schema on Precision Medicine for Diabetes Using Big Data Analytics

Sobhanbabu,

Bharati

2023

RIA

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Precision medicine is the future of the Healthcare system, which is going to change the generalized medicine prescription. To establish precision medicine in the place of generalized medicine, new frameworks have to be designed that will utilize the existing enormous volumes of data. Big data is the term that represents massive volumes of data being generated through various means, including in Healthcare. In this work, a new framework for precision medicine is proposed beginning from big data, applying Classification techniques to avail reduced data and there on to achieve precision medicine with an intelligent medicine advisor. In the first phase, we evaluated the noisy big data with innovative hierarchical decision attention networks, Multi class classification using Map Reduce mechanism. This phase is experimented with a diabetes dataset. This output can be utilized by the next phase component called Intelligent Medicine advisor to get system recommended precision medicine, which can help the medical practitioner to join the Artificial Intelligence in their treatment.

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“…Анализ, проведенный в 153 организациях США, свидетельствует о значительной неоднородности степени внедрения ПМ [7]. Относительно немного организаций, предоставляющих медицинские услуги в Соединенных Штатах, предлагают ПМ как часть клинического рабочего процесса [8].…”

Section: Introductionunclassified

Personalized Critical Care Medicine (Review)

Голубев

2022

Obŝaâ reanimatologiâ

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Personalized medicine (PM) is a major trend in health care development in the 21st century. This area includes studying risk factors for disease development (prediction), interventions for preventing diseases (prophylaxis), individualization of diagnosis and treatment (personalization), informing the patient on disease prevention and treatment (participation). In the recent years, an intense research to introduce the personalized medicine principles into the management of critically ill patients, has been under way. This includes identification of patient groups based on genomic research, development of diagnostic tests using molecular markers, creation of novel classes of drugs based on individual patient characteristics.The aim of the review is to summarize the available data on the implementation of the principles of PM in the routine practice of critical care institutions.We analyzed more than 300 sources of literature from the Pubmed and Scopus databases, as well as the RSCI database. Eighty five most relevant sources were selected for the review. The paper reports data on the organization and results of implementation of PM principles and advanced technologies, such as Emergency Medicine Sample Bank (EMSB), in the daily activity of clinics providing emergency critical care. The formation of the novel PM concept focused on the treatment of critically ill patients has been discussed. The review contains detailed data on the patterns of development of specific critical illnesses such as acute cerebrovascular events, acute respiratory distress syndrome, traumatic brain injury, shock, myocardial infarction, cardiac rhythm and conduction disturbances. Medication eﬃcacy in view of individual genetic patient characteristics has also been highlighted. No research limitations on the subject were identified.Conclusion. The analysis of literature has demonstrated positive results of implementing PM principles in prevention, diagnosis and treatment of critically ill patients. Creation of Biobanks, development of training programs and regulatory documentation, advancing the scientific research, introduction of new methods of diagnosis and treatment will contribute to the implementation of PM principles in practical healthcare.

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A Quantitative Framework for Measuring Personalized Medicine Integration into US Healthcare Delivery Organizations

Cited by 9 publications

References 10 publications

Big 5 Personality Traits and Individual- and Practice-Related Characteristics as Influencing Factors of Digital Maturity in General Practices: Quantitative Web-Based Survey Study

Big 5 Personality Traits and Individual- and Practice-Related Characteristics as Influencing Factors of Digital Maturity in General Practices: Quantitative Web-Based Survey Study

An Intelligent Evolutionary Schema on Precision Medicine for Diabetes Using Big Data Analytics

Personalized Critical Care Medicine (Review)

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