The Promise of Information and Communication Technology in Healthcare: Extracting Value From the Chaos

Mamlin, Burke W.; Tierney, William M.

doi:10.1016/j.amjms.2015.10.015

Cited by 86 publications

(43 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…12,13 However, implementation of such interventions faces many challenges, such as the need for resources to develop and implement algorithms to extract EHR data and administrative or clinical personnel to deliver it to the point of care. 6 Additional research is needed to ensure such interventions are trans- lated successfully into practice.…”

Section: Discussionmentioning

confidence: 99%

Communicating Findings of Delayed Diagnostic Evaluation to Primary Care Providers

Meyer

Murphy

Singh

2016

The Journal of the American Board of Family Medicine

View full text Add to dashboard Cite

Background:We previously found that an intervention involving electronic algorithms to detect delays in follow-up of cancer-related abnormal or "red-flag" findings and communicating this information to primary care providers (PCPs) led to more timely diagnostic evaluation. In this study, we examined the effectiveness of various communication strategies to inform PCPs about the delayed follow-up.Methods: After identifying follow-up delays through electronic health record-based algorithms and record reviews, we communicated this information to PCPs using 3 escalating steps. First, we sent secure E-mails. If no evidence of follow-up was found in a medical record review after 1 week, we made up to 3 attempts to reach the PCPs or their nurses via telephone. If they could not be reached, we informed clinic directors as the third and final step. In this analysis, we evaluate PCPs' follow-up in response to these methods of communication.Results: A total of 733 patients with follow-up delays were identified (369 patients in the intervention group and 364 patients in the control group). Communicating information to PCPs about possible follow-up delays led to decreased times to diagnostic evaluation, but communication related to delays did not always lead to follow-up for the patients in the intervention group. Specifically, secure E-mails led to follow-up in 11.1% of cases (41

show abstract

Section: Discussionmentioning

confidence: 99%

Communicating Findings of Delayed Diagnostic Evaluation to Primary Care Providers

Meyer

Murphy

Singh

2016

The Journal of the American Board of Family Medicine

View full text Add to dashboard Cite

show abstract

“…Additionally, methods for collecting and presenting contextual information, such as whether a blood pressure cuff was applied correctly, need to be developed. As such, mHealth apps are creating new silos of data which can be a challenge to integrate into electronic health record and health information exchange ecosystems [94].…”

Section: Themes Data Transmission and Storagementioning

confidence: 99%

Developments in Privacy and Data Ownership in Mobile Health Technologies, 2016-2019

Galvin

DeMuro²

2020

Yearb Med Inform

View full text Add to dashboard Cite

Objectives: To survey international regulatory frameworks that serve to protect privacy of personal data as a human right as well as to review the literature regarding privacy protections and data ownership in mobile health (mHealth) technologies between January 1, 2016 and June 1, 2019 in order to identify common themes. Methods: We performed a review of relevant literature available in English published between January 1, 2016 and June 1, 2019 from databases including PubMed, Google Scholar, and Web of Science, as well as relevant legislative background material. Articles out of scope (as detailed below) were eliminated. We categorized the remaining pool of articles and discrete themes were identified, specifically: concerns around data transmission and storage, including data ownership and the ability to re-identify previously de-identified data; issues with user consent (including the availability of appropriate privacy policies) and access control; and the changing culture and variable global attitudes toward privacy of health data. Results: Recent literature demonstrates that the security of mHealth data storage and transmission remains of wide concern, and aggregated data that were previously considered “de-identified” have now been demonstrated to be re-identifiable. Consumer-informed consent may be lacking with regard to mHealth applications due to the absence of a privacy policy and/or to text that is too complex and lengthy for most users to comprehend. The literature surveyed emphasizes improved access control strategies. This survey also illustrates a wide variety of global user perceptions regarding health data privacy. Conclusion: The international regulatory framework that serves to protect privacy of personal data as a human right is diverse. Given the challenges legislators face to keep up with rapidly advancing technology, we introduce the concept of a “healthcare fiduciary” to serve the best interest of data subjects in the current environment.

show abstract

“…The acceptance of new mobile personal technologies and wearable sensors have increased the quantity of data related to human behaviors [10,11]. According to CISCO Visual Networking Index Prediction [12], the number of connected things on the Internet will rise to 26.3 billion by 2020 [13], which stands as an excellent opportunity towards a new generation of new applications and services at a population level [14,15]. A large amount of data available provides a new challenge for creating new models capable of extracting information, insights, visualizations, and to support in daily decisions for improving accuracy in the diagnostics [16].…”

Section: Introductionmentioning

confidence: 99%

Individual Behavior Modeling with Sensors Using Process Mining

et al. 2019

View full text Add to dashboard Cite

Understanding human behavior can assist in the adoption of satisfactory health interventions and improved care. One of the main problems relies on the definition of human behaviors, as human activities depend on multiple variables and are of dynamic nature. Although smart homes have advanced in the latest years and contributed to unobtrusive human behavior tracking, artificial intelligence has not coped yet with the problem of variability and dynamism of these behaviors. Process mining is an emerging discipline capable of adapting to the nature of high-variate data and extract knowledge to define behavior patterns. In this study, we analyze data from 25 in-house residents acquired with indoor location sensors by means of process mining clustering techniques, which allows obtaining workflows of the human behavior inside the house. Data are clustered by adjusting two variables: the similarity index and the Euclidean distance between workflows. Thereafter, two main models are created: (1) a workflow view to analyze the characteristics of the discovered clusters and the information they reveal about human behavior and (2) a calendar view, in which common behaviors are rendered in the way of a calendar allowing to detect relevant patterns depending on the day of the week and the season of the year. Three representative patients who performed three different behaviors: stable, unstable, and complex behaviors according to the proposed approach are investigated. This approach provides human behavior details in the manner of a workflow model, discovering user paths, frequent transitions between rooms, and the time the user was in each room, in addition to showing the results into the calendar view increases readability and visual attraction of human behaviors, allowing to us detect patterns happening on special days.

show abstract

The Promise of Information and Communication Technology in Healthcare: Extracting Value From the Chaos

Cited by 86 publications

References 41 publications

Communicating Findings of Delayed Diagnostic Evaluation to Primary Care Providers

Communicating Findings of Delayed Diagnostic Evaluation to Primary Care Providers

Developments in Privacy and Data Ownership in Mobile Health Technologies, 2016-2019

Individual Behavior Modeling with Sensors Using Process Mining

Contact Info

Product

Resources

About