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Applying machine learning to predict future adherence to physical activity programs.
BackgroundIdentifying individuals who are unlikely to adhere to a physical exercise regime has potential to improve physical activity interventions. The aim of this paper is to develop and test adherence prediction models using objectively measured physical activity data in the Mobile Phone-Based Physical Activity Education program (mPED) trial. To the best of our knowledge, this is the first to apply Machine Learning methods to predict exercise relapse using accelerometer-recorded physical activity data.MethodsWe use logistic regression and support vector machine methods to design two versions of a Discontinuation Prediction Score (DiPS), which uses objectively measured past data (e.g., steps and goal achievement) to provide a numerical quantity indicating the likelihood of exercise relapse in the upcoming week. The respective prediction accuracy of these two versions of DiPS are compared, and then numerical simulation is performed to explore the potential of using DiPS to selectively allocate financial incentives to participants to encourage them to increase physical activity.Resultswe had access to a physical activity trial data that were continuously collected every 60 sec every day for 9 months in 210 participants. By using the first 15 weeks of data as training and test on weeks 16-30, we show that both versions of DiPS have a test AUC of 0.9 with high sensitivity and specificity in predicting the probability of exercise adherence. Simulation results assuming different intervention regimes suggest the potential benefit of using DiPS as a score to allocate resources in physical activity intervention programs in reducing costs over other allocation schemes.ConclusionsDiPS is capable of making accurate and robust predictions for future weeks. The most predictive features are steps and physical activity intensity. Furthermore, the use of DiPS scores can be a promising approach to determine when or if to provide just-in-time messages and step goal adjustments to improve compliance. Further studies on the use of DiPS in the design of physical activity promotion programs are warranted.Trial registrationClinicalTrials.gov NCT01280812 Registered on January 21, 2011
Regional data exchange to improve care for veterans after non-VA hospitalization: a randomized controlled trial
BACKGROUND:
Coordination of care, especially after a patient experiences an acute care event, is a challenge for many health systems. Event notification is a form of health information exchange (HIE) which has the potential to support care coordination by alerting primary care providers when a patient experiences an acute care event. While promising, there exists little evidence on the impact of event notification in support of reengagement into primary care. The objectives of this study are to 1) examine the effectiveness of event notification on health outcomes for older adults who experience acute care events, and 2) compare approaches to how providers respond to event notifications.
METHODS:
In a cluster randomized trial conducted across two medical centers within the U.S. Veterans Health Administration (VHA) system, we plan to enroll older patients (≥ 65 years of age) who utilize both VHA and non-VHA providers. Patients will be enrolled into one of three arms: 1) usual care; 2) event notifications only; or 3) event notifications plus a care transitions intervention. In the event notification arms, following a non-VHA acute care encounter, an HIE-based intervention will send an event notification to VHA providers. Patients in the event notification plus care transitions arm will also receive 30 days of care transition support from a social worker. The primary outcome measure is 90-day readmission rate. Secondary outcomes will be high risk medication discrepancies as well as care transitions processes within the VHA health system. Qualitative assessments of the intervention will inform VHA system-wide implementation.
DISCUSSION:
While HIE has been evaluated in other contexts, little evidence exists on HIE-enabled event notification interventions. Furthermore, this trial offers the opportunity to examine the use of event notifications that trigger a care transitions intervention to further support coordination of care.
TRIAL REGISTRATION:
ClinicalTrials.gov NCT02689076. "Regional Data Exchange to Improve Care for Veterans After Non-VA Hospitalization." Registered 23 February 2016
Outlook Magazine, Summer 2018
https://digitalcommons.wustl.edu/outlook/1204/thumbnail.jp
Electronic health records to facilitate clinical research
Electronic health records (EHRs) provide opportunities to enhance patient care, embed performance measures in clinical practice, and facilitate clinical research. Concerns have been raised about the increasing recruitment challenges in trials, burdensome and obtrusive data collection, and uncertain generalizability of the results. Leveraging electronic health records to counterbalance these trends is an area of intense interest. The initial applications of electronic health records, as the primary data source is envisioned for observational studies, embedded pragmatic or post-marketing registry-based randomized studies, or comparative effectiveness studies. Advancing this approach to randomized clinical trials, electronic health records may potentially be used to assess study feasibility, to facilitate patient recruitment, and streamline data collection at baseline and follow-up. Ensuring data security and privacy, overcoming the challenges associated with linking diverse systems and maintaining infrastructure for repeat use of high quality data, are some of the challenges associated with using electronic health records in clinical research. Collaboration between academia, industry, regulatory bodies, policy makers, patients, and electronic health record vendors is critical for the greater use of electronic health records in clinical research. This manuscript identifies the key steps required to advance the role of electronic health records in cardiovascular clinical research
Addendum to Informatics for Health 2017: Advancing both science and practice
This article presents presentation and poster abstracts that were mistakenly omitted from the original publication
Softer perspectives on enhancing the patient experience using IS/IT
Purpose – This paper aims to argue that the implementation of the Choose and Book system has failed due to the inability of project sponsors to appreciate the complex and far-reaching softer implications of the implementation, especially in a complex organisation such as the NHS, which has multifarious stakeholders.
Design/methodology/approach – The authors use practice-oriented research to try and isolate key parameters. These parameters are compared with existing conventional thinking in a number of focused areas.
Findings – Like many previous NHS initiatives, the focus of this system is in its obvious link to patients. However we find that although this project has cultural, social and organisational implications, programme managers and champions of the Connecting for Health programme emphasised the technical domains to IS/IT adoption.
Research limitations/implications – This paper has been written in advance of a fully implemented Choose and Book system.
Practical implications – The paper requests that more attention be paid to the softer side of IS/IT delivery, implementation, introduction and adoption.
Originality/value – The paper shows that patient experience within the UK healthcare sector is still well below what is desired
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Marshfield Clinic: Health Information Technology Paves the Way for Population Health Management
Highlights Fund-defined attributes of an ideal care delivery system and best practices, including an internal electronic health record, primary care teams, physician quality metrics and mentors, and standardized care processes for chronic care management
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