Uninterruptible power systems and other power protection equipment for electronic health care systems

1979 ◽  
Vol 3 (3-4) ◽  
pp. 199-212
Author(s):  
James K. Massey
Keyword(s):  
Health Care ◽  
Power Systems ◽  
Health Care Systems ◽  
Care Systems ◽  
Electronic Health ◽  
Uninterruptible Power

Machines and Medicines: A Projection of Analogies between Electric Power Systems and Health Care Systems

1986 ◽  
Vol 2 (2) ◽  
pp. 285-295
Author(s):  
Thomas P. Hughes
Keyword(s):  
Health Care ◽  
Power Systems ◽  
Policy Making ◽  
Health Care Systems ◽  
Electric Power Systems ◽  
Policy Makers ◽  
Care Systems ◽  
History Of ◽  
Large Systems ◽  
Capital Intensive

If medicine is becoming mechanized, as many indications suggest, then those interested in policy making for medical matters have much to learn from the history of technology. The mechanization of medicine, as in the case of the mechanization of production, will accelerate the transfer of skill and knowledge from people to machines and the transition of health care to a capital intensive industry (19, 196–226). Furthermore, mechanization and increasing capital intensification may bring the increased systematization of health care. If the development of mechanized medicine follows the precedent of the mechanization of production, then our society must deal with the evolution of another set of extremely large systems, systems that will become virtually impervious to social control. Historians of technology are currently providing a better understanding of the evolution of large systems of production (3;9;10); there are lessons to be learned from this history by policy makers in health care.

scholarly journals An efficient approach for surveillance of childhood diabetes by type derived from electronic health record data: the SEARCH for Diabetes in Youth Study

2016 ◽  
Vol 23 (6) ◽  
pp. 1060-1067 ◽  
Author(s):  
Victor W Zhong ◽  
Jihad S Obeid ◽  
Jean B Craig ◽  
Emily R Pfaff ◽  
Joan Thomas ◽  
...  
Keyword(s):  
Health Care ◽  
Electronic Health Record ◽  
Health Care Systems ◽  
Diabetes Type ◽  
Health Record ◽  
Childhood Diabetes ◽  
Electronic Health Record Data ◽  
Efficient Approach ◽  
Care Systems ◽  
Electronic Health

Abstract Objective To develop an efficient surveillance approach for childhood diabetes by type across 2 large US health care systems, using phenotyping algorithms derived from electronic health record (EHR) data. Materials and Methods Presumptive diabetes cases <20 years of age from 2 large independent health care systems were identified as those having ≥1 of the 5 indicators in the past 3.5 years, including elevated HbA1c, elevated blood glucose, diabetes-related billing codes, patient problem list, and outpatient anti-diabetic medications. EHRs of all the presumptive cases were manually reviewed, and true diabetes status and diabetes type were determined. Algorithms for identifying diabetes cases overall and classifying diabetes type were either prespecified or derived from classification and regression tree analysis. Surveillance approach was developed based on the best algorithms identified. Results We developed a stepwise surveillance approach using billing code–based prespecified algorithms and targeted manual EHR review, which efficiently and accurately ascertained and classified diabetes cases by type, in both health care systems. The sensitivity and positive predictive values in both systems were approximately ≥90% for ascertaining diabetes cases overall and classifying cases with type 1 or type 2 diabetes. About 80% of the cases with “other” type were also correctly classified. This stepwise surveillance approach resulted in a >70% reduction in the number of cases requiring manual validation compared to traditional surveillance methods. Conclusion EHR data may be used to establish an efficient approach for large-scale surveillance for childhood diabetes by type, although some manual effort is still needed.

scholarly journals Predicting Dropouts From an Electronic Health Platform for Lifestyle Interventions: Analysis of Methods and Predictors (Preprint)

2019 ◽  
Author(s):  
Daniel Hansen Pedersen ◽  
Marjan Mansourvar ◽  
Camilla Sortsø ◽  
Thomas Schmidt
Keyword(s):  
Health Care ◽  
Random Forest ◽  
Health Care Systems ◽  
Activity Patterns ◽  
Predictor Variables ◽  
Lifestyle Interventions ◽  
Health Coaches ◽  
Care Systems ◽  
Electronic Health ◽  
Ehealth Intervention

BACKGROUND The increasing prevalence and economic impact of chronic diseases challenge health care systems globally. Digital solutions can potentially improve efficiency and quality of care, but these initiatives struggle with nonusage attrition. Machine learning methods have been proven to predict dropouts in other settings but lack implementation in health care. OBJECTIVE This study aimed to gain insight into the causes of attrition for patients in an electronic health (eHealth) intervention for chronic lifestyle diseases and evaluate if attrition can be predicted and consequently prevented. We aimed to build predictive models that can identify patients in a digital lifestyle intervention at high risk of dropout by analyzing several predictor variables applied in different models and to further assess the possibilities and impact of implementing such models into an eHealth platform. METHODS Data from 2684 patients using an eHealth platform were iteratively analyzed using logistic regression, decision trees, and random forest models. The dataset was split into a 79.99% (2147/2684) training and cross-validation set and a 20.0% (537/2684) holdout test set. Trends in activity patterns were analyzed to assess engagement over time. Development and implementation were performed iteratively with health coaches. RESULTS Patients in the test dataset were classified as dropouts with an 89% precision using a random forest model and 11 predictor variables. The most significant predictors were the provider of the intervention, 2 weeks inactivity, and the number of advices received from the health coach. Engagement in the platform dropped significantly leading up to the time of dropout. CONCLUSIONS Dropouts from eHealth lifestyle interventions can be predicted using various data mining methods. This can support health coaches in preventing attrition by receiving proactive warnings. The best performing predictive model was found to be the random forest.

scholarly journals Pragmatic (trial) informatics: a perspective from the NIH Health Care Systems Research Collaboratory

2017 ◽  
Vol 24 (5) ◽  
pp. 996-1001 ◽  
Author(s):  
Rachel L Richesson ◽  
Beverly B Green ◽  
Reesa Laws ◽  
Jon Puro ◽  
Michael G Kahn ◽  
...  
Keyword(s):  
Health Care ◽  
Health System ◽  
Health Care Systems ◽  
Pragmatic Trial ◽  
Heterogeneous Systems ◽  
Full Potential ◽  
Systems Research ◽  
Pragmatic Clinical Trials ◽  
Care Systems ◽  
Electronic Health

Abstract Pragmatic clinical trials (PCTs) are research investigations embedded in health care settings designed to increase the efficiency of research and its relevance to clinical practice. The Health Care Systems Research Collaboratory, initiated by the National Institutes of Health Common Fund in 2010, is a pioneering cooperative aimed at identifying and overcoming operational challenges to pragmatic research. Drawing from our experience, we present 4 broad categories of informatics-related challenges: (1) using clinical data for research, (2) integrating data from heterogeneous systems, (3) using electronic health records to support intervention delivery or health system change, and (4) assessing and improving data capture to define study populations and outcomes. These challenges impact the validity, reliability, and integrity of PCTs. Achieving the full potential of PCTs and a learning health system will require meaningful partnerships between health system leadership and operations, and federally driven standards and policies to ensure that future electronic health record systems have the flexibility to support research.

scholarly journals Predicting Dropouts From an Electronic Health Platform for Lifestyle Interventions: Analysis of Methods and Predictors

10.2196/13617 ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. e13617 ◽  
Author(s):  
Daniel Hansen Pedersen ◽  
Marjan Mansourvar ◽  
Camilla Sortsø ◽  
Thomas Schmidt
Keyword(s):  
Health Care ◽  
Random Forest ◽  
Health Care Systems ◽  
Activity Patterns ◽  
Predictor Variables ◽  
Lifestyle Interventions ◽  
Health Coaches ◽  
Care Systems ◽  
Electronic Health ◽  
Ehealth Intervention

Background The increasing prevalence and economic impact of chronic diseases challenge health care systems globally. Digital solutions can potentially improve efficiency and quality of care, but these initiatives struggle with nonusage attrition. Machine learning methods have been proven to predict dropouts in other settings but lack implementation in health care. Objective This study aimed to gain insight into the causes of attrition for patients in an electronic health (eHealth) intervention for chronic lifestyle diseases and evaluate if attrition can be predicted and consequently prevented. We aimed to build predictive models that can identify patients in a digital lifestyle intervention at high risk of dropout by analyzing several predictor variables applied in different models and to further assess the possibilities and impact of implementing such models into an eHealth platform. Methods Data from 2684 patients using an eHealth platform were iteratively analyzed using logistic regression, decision trees, and random forest models. The dataset was split into a 79.99% (2147/2684) training and cross-validation set and a 20.0% (537/2684) holdout test set. Trends in activity patterns were analyzed to assess engagement over time. Development and implementation were performed iteratively with health coaches. Results Patients in the test dataset were classified as dropouts with an 89% precision using a random forest model and 11 predictor variables. The most significant predictors were the provider of the intervention, 2 weeks inactivity, and the number of advices received from the health coach. Engagement in the platform dropped significantly leading up to the time of dropout. Conclusions Dropouts from eHealth lifestyle interventions can be predicted using various data mining methods. This can support health coaches in preventing attrition by receiving proactive warnings. The best performing predictive model was found to be the random forest.

Sign in / Sign up

Export Citation Format

Share Document