Pub Date : 2022-07-01DOI: 10.1109/ICDH55609.2022.00016
Jenna M. Reinen, C. Agurto, G. Cecchi, Jeffrey L. Rogers, Navitas Envision Studies Physician Author Group, Boston Scientific Research Scientists Consortium
The technical capacity to monitor patients with a mobile device has drastically expanded, but data produced from this approach are often difficult to interpret. We present a solution to produce a meaningful representation of patient status from large, complex data streams, leveraging both a data-driven approach, and use clinical knowledge to validate results. Data were collected from a clinical trial enrolling chronic pain patients, and included questionnaires, voice recordings, actigraphy, and standard health assessments. The data were reduced using a clustering analysis. In an initial exploratory analysis with only questionnaire data, we found up to 3 stable cluster solutions that grouped symptoms on a positive to negative spectrum. Objective features (actigraphy, speech) expanded the cluster solution granularity. Using a 5 state solution with questionnaire and actigraphy data, we found significant correlations between cluster properties and assessments of disability and quality- of-life. The correlation coefficient values showed an ordinal distinction, confirming the cluster ranking on a negative to positive spectrum. This suggests we captured novel, distinct Pain Patient States with this approach, even when multiple clusters were equated on pain magnitude. Relative to using complex time courses of many variables, Pain Patient States holds promise as an interpretable, useful, and actionable metric for a clinician or caregiver to simplify and provide timely delivery of care.
{"title":"Definition and clinical validation of Pain Patient States from high-dimensional mobile data: application to a chronic pain cohort","authors":"Jenna M. Reinen, C. Agurto, G. Cecchi, Jeffrey L. Rogers, Navitas Envision Studies Physician Author Group, Boston Scientific Research Scientists Consortium","doi":"10.1109/ICDH55609.2022.00016","DOIUrl":"https://doi.org/10.1109/ICDH55609.2022.00016","url":null,"abstract":"The technical capacity to monitor patients with a mobile device has drastically expanded, but data produced from this approach are often difficult to interpret. We present a solution to produce a meaningful representation of patient status from large, complex data streams, leveraging both a data-driven approach, and use clinical knowledge to validate results. Data were collected from a clinical trial enrolling chronic pain patients, and included questionnaires, voice recordings, actigraphy, and standard health assessments. The data were reduced using a clustering analysis. In an initial exploratory analysis with only questionnaire data, we found up to 3 stable cluster solutions that grouped symptoms on a positive to negative spectrum. Objective features (actigraphy, speech) expanded the cluster solution granularity. Using a 5 state solution with questionnaire and actigraphy data, we found significant correlations between cluster properties and assessments of disability and quality- of-life. The correlation coefficient values showed an ordinal distinction, confirming the cluster ranking on a negative to positive spectrum. This suggests we captured novel, distinct Pain Patient States with this approach, even when multiple clusters were equated on pain magnitude. Relative to using complex time courses of many variables, Pain Patient States holds promise as an interpretable, useful, and actionable metric for a clinician or caregiver to simplify and provide timely delivery of care.","PeriodicalId":120923,"journal":{"name":"2022 IEEE International Conference on Digital Health (ICDH)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132081167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/ICDH55609.2022.00018
Flor Nino Sevilla Palma
This case study explores the challenges in the interoperability of cross-border digital medical prescriptions between Finland and Estonia as pioneer countries in cross-border ePrescription which means Finnish patients' prescriptions can be dispensed with medicines in Estonian pharmacies and vice versa. This delves into the critical factors that contributed to the success of this government e-service as well as the different deployment constraints that happened at every stage of the six levels of the refined eHealth European Interoperability Framework. The reported challenges and implemented solutions are further mapped out at which environments they typically occur whether in micro, meso, and macro levels. The data collection was done in multi-method by which semi-structured interviews were conducted to eight (8) key government experts from Finland and Estonia. Results revealed that common challenges include different health care systems, different national legislations on the policy of consent, constraints in the semantic level as new prescriptions emerge in the pharmaceutical markets and the need for assessment to measure actual benefits and impact. On one side, the drivers of successful implementation consist of organizational and country resources, long-standing cross-border cooperation, trust, and political commitment, and pan-European support.
{"title":"Interoperability Challenges and Critical Success Factors in the Deployment of Cross-border Digital Medical Prescriptions in Finland and Estonia","authors":"Flor Nino Sevilla Palma","doi":"10.1109/ICDH55609.2022.00018","DOIUrl":"https://doi.org/10.1109/ICDH55609.2022.00018","url":null,"abstract":"This case study explores the challenges in the interoperability of cross-border digital medical prescriptions between Finland and Estonia as pioneer countries in cross-border ePrescription which means Finnish patients' prescriptions can be dispensed with medicines in Estonian pharmacies and vice versa. This delves into the critical factors that contributed to the success of this government e-service as well as the different deployment constraints that happened at every stage of the six levels of the refined eHealth European Interoperability Framework. The reported challenges and implemented solutions are further mapped out at which environments they typically occur whether in micro, meso, and macro levels. The data collection was done in multi-method by which semi-structured interviews were conducted to eight (8) key government experts from Finland and Estonia. Results revealed that common challenges include different health care systems, different national legislations on the policy of consent, constraints in the semantic level as new prescriptions emerge in the pharmaceutical markets and the need for assessment to measure actual benefits and impact. On one side, the drivers of successful implementation consist of organizational and country resources, long-standing cross-border cooperation, trust, and political commitment, and pan-European support.","PeriodicalId":120923,"journal":{"name":"2022 IEEE International Conference on Digital Health (ICDH)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131202953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/ICDH55609.2022.00030
Jared Leitner, Po-Han Chiang, Brian Khan, Sujit Dey
In this paper, we present an AI-driven lifestyle intervention service for patients with hypertension. The automated intervention platform consists of a remote monitoring system that ingests lifestyle and blood pressure (BP) data and builds a personalized machine learning (ML) model to generate tailored lifestyle recommendations most relevant to each patient's BP. Lifestyle data is collected from a wearable device and questionnaire mobile app which includes activity, sleep, stress and diet information. BP data is remotely collected using at-home BP monitors. With this data, the system trains random forest models that predict BP from lifestyle features and uses Shapley Value analysis to estimate the impact of features on BP. Precise lifestyle recommendations are generated based on the top lifestyle factors for each patient. To test the system's ability to improve BP, we enrolled hypertensive patients into a three-armed clinical trial. During the 6-month trial period, our system provided weekly recommendations to patients in the experimental group. We evaluate the system's effectiveness based on multiple BP improvement metrics and comparison with a control group. Patients in the experimental group experienced an average BP change of −4.0 and −4.7 mmHg for systolic and diastolic BP, respectively, compared to −0.3 and −0.9 mmHg for the control group. Our results demonstrate that the platform can effectively help patients improve their BP through precise lifestyle recommendations.
{"title":"An mHealth Lifestyle Intervention Service for Improving Blood Pressure using Machine Learning and IoMTs","authors":"Jared Leitner, Po-Han Chiang, Brian Khan, Sujit Dey","doi":"10.1109/ICDH55609.2022.00030","DOIUrl":"https://doi.org/10.1109/ICDH55609.2022.00030","url":null,"abstract":"In this paper, we present an AI-driven lifestyle intervention service for patients with hypertension. The automated intervention platform consists of a remote monitoring system that ingests lifestyle and blood pressure (BP) data and builds a personalized machine learning (ML) model to generate tailored lifestyle recommendations most relevant to each patient's BP. Lifestyle data is collected from a wearable device and questionnaire mobile app which includes activity, sleep, stress and diet information. BP data is remotely collected using at-home BP monitors. With this data, the system trains random forest models that predict BP from lifestyle features and uses Shapley Value analysis to estimate the impact of features on BP. Precise lifestyle recommendations are generated based on the top lifestyle factors for each patient. To test the system's ability to improve BP, we enrolled hypertensive patients into a three-armed clinical trial. During the 6-month trial period, our system provided weekly recommendations to patients in the experimental group. We evaluate the system's effectiveness based on multiple BP improvement metrics and comparison with a control group. Patients in the experimental group experienced an average BP change of −4.0 and −4.7 mmHg for systolic and diastolic BP, respectively, compared to −0.3 and −0.9 mmHg for the control group. Our results demonstrate that the platform can effectively help patients improve their BP through precise lifestyle recommendations.","PeriodicalId":120923,"journal":{"name":"2022 IEEE International Conference on Digital Health (ICDH)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129073303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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