BMC medical informatics and decision making, Jan 5, 2006
Comprehensive knowledge about the level of healthcare information technology (HIT) adoption in th... more Comprehensive knowledge about the level of healthcare information technology (HIT) adoption in the United States remains limited. We therefore performed a baseline assessment to address this knowledge gap. We segmented HIT into eight major stakeholder groups and identified major functionalities that should ideally exist for each, focusing on applications most likely to improve patient safety, quality of care and organizational efficiency. We then conducted a multi-site qualitative study in Boston and Denver by interviewing key informants from each stakeholder group. Interview transcripts were analyzed to assess the level of adoption and to document the major barriers to further adoption. Findings for Boston and Denver were then presented to an expert panel, which was then asked to estimate the national level of adoption using the modified Delphi approach. We measured adoption level in Boston and Denver was graded on Rogers' technology adoption curve by co-investigators. National...
BACKGROUND. Electronic results management may improve the reliability and efficiency of test resu... more BACKGROUND. Electronic results management may improve the reliability and efficiency of test results management, but few studies have investigated this topic in pediatrics. METHODS. We conducted semi-structured, key informant interviews before and after implementation of electronic results management at 8 pediatric ambulatory care practices. We also surveyed all pediatricians at 18 practices (10 additional practices). All practices were members of Partners Healthcare and had been using an electronic health record when they were offered electronic results management. We assessed baseline processes for results management, barriers to electronic results management adoption, and the perceived impact of electronic results management on quality, efficiency, and provider satisfaction. RESULTS. From interviews, we found a range of processes in place to manage test results, but all practices reported losing some results and no practice tracked all test results from the time of ordering to pa...
Journal of the American Medical Informatics Association : JAMIA
Patients, policymakers, providers, payers, employers, and others have increasing interest in usin... more Patients, policymakers, providers, payers, employers, and others have increasing interest in using personal health records (PHRs) to improve healthcare costs, quality, and efficiency. While organizations now invest millions of dollars in PHRs, the best PHR architectures, value propositions, and descriptions are not universally agreed upon. Despite widespread interest and activity, little PHR research has been done to date, and targeted research investment in PHRs appears inadequate. The authors reviewed the existing PHR specific literature (100 articles) and divided the articles into seven categories, of which four in particular--evaluation of PHR functions, adoption and attitudes of healthcare providers and patients towards PHRs, PHR related privacy and security, and PHR architecture--present important research opportunities. We also briefly discuss other research related to PHRs, PHR research funding sources, and PHR business models. We believe that additional PHR research can inc...
There is little known about how academic medical centers (AMCs) in the US develop, implement, and... more There is little known about how academic medical centers (AMCs) in the US develop, implement, and maintain predictive modeling and machine learning (PM and ML) models. We conducted semi-structured interviews with leaders from AMCs to assess their use of PM and ML in clinical care, understand associated challenges, and determine recommended best practices. Each transcribed interview was iteratively coded and reconciled by a minimum of 2 investigators to identify key barriers to and facilitators of PM and ML adoption and implementation in clinical care. Interviews were conducted with 33 individuals from 19 AMCs nationally. AMCs varied greatly in the use of PM and ML within clinical care, from some just beginning to explore their utility to others with multiple models integrated into clinical care. Informants identified 5 key barriers to the adoption and implementation of PM and ML in clinical care: (1) culture and personnel, (2) clinical utility of the PM and ML tool, (3) financing, (...
Unplanned hospital readmissions represent a significant health care value problem with high costs... more Unplanned hospital readmissions represent a significant health care value problem with high costs and poor quality of care. A significant percentage of readmissions could be prevented if clinical inpatient teams were better able to predict which patients were at higher risk for readmission. Many of the current clinical decision support models that predict readmissions are not configured to integrate closely with the electronic health record or alert providers in real-time prior to discharge about a patient’s risk for readmission. We report on the implementation and monitoring of the Epic electronic health record—“Unplanned readmission model version 1”—over 2 years from 1/1/2018–12/31/2019. For patients discharged during this time, the predictive capability to discern high risk discharges was reflected in an AUC/C-statistic at our three hospitals of 0.716–0.760 for all patients and 0.676–0.695 for general medicine patients. The model had a positive predictive value ranging from 0.217...
BMC medical informatics and decision making, Jan 5, 2006
Comprehensive knowledge about the level of healthcare information technology (HIT) adoption in th... more Comprehensive knowledge about the level of healthcare information technology (HIT) adoption in the United States remains limited. We therefore performed a baseline assessment to address this knowledge gap. We segmented HIT into eight major stakeholder groups and identified major functionalities that should ideally exist for each, focusing on applications most likely to improve patient safety, quality of care and organizational efficiency. We then conducted a multi-site qualitative study in Boston and Denver by interviewing key informants from each stakeholder group. Interview transcripts were analyzed to assess the level of adoption and to document the major barriers to further adoption. Findings for Boston and Denver were then presented to an expert panel, which was then asked to estimate the national level of adoption using the modified Delphi approach. We measured adoption level in Boston and Denver was graded on Rogers' technology adoption curve by co-investigators. National...
BACKGROUND. Electronic results management may improve the reliability and efficiency of test resu... more BACKGROUND. Electronic results management may improve the reliability and efficiency of test results management, but few studies have investigated this topic in pediatrics. METHODS. We conducted semi-structured, key informant interviews before and after implementation of electronic results management at 8 pediatric ambulatory care practices. We also surveyed all pediatricians at 18 practices (10 additional practices). All practices were members of Partners Healthcare and had been using an electronic health record when they were offered electronic results management. We assessed baseline processes for results management, barriers to electronic results management adoption, and the perceived impact of electronic results management on quality, efficiency, and provider satisfaction. RESULTS. From interviews, we found a range of processes in place to manage test results, but all practices reported losing some results and no practice tracked all test results from the time of ordering to pa...
Journal of the American Medical Informatics Association : JAMIA
Patients, policymakers, providers, payers, employers, and others have increasing interest in usin... more Patients, policymakers, providers, payers, employers, and others have increasing interest in using personal health records (PHRs) to improve healthcare costs, quality, and efficiency. While organizations now invest millions of dollars in PHRs, the best PHR architectures, value propositions, and descriptions are not universally agreed upon. Despite widespread interest and activity, little PHR research has been done to date, and targeted research investment in PHRs appears inadequate. The authors reviewed the existing PHR specific literature (100 articles) and divided the articles into seven categories, of which four in particular--evaluation of PHR functions, adoption and attitudes of healthcare providers and patients towards PHRs, PHR related privacy and security, and PHR architecture--present important research opportunities. We also briefly discuss other research related to PHRs, PHR research funding sources, and PHR business models. We believe that additional PHR research can inc...
There is little known about how academic medical centers (AMCs) in the US develop, implement, and... more There is little known about how academic medical centers (AMCs) in the US develop, implement, and maintain predictive modeling and machine learning (PM and ML) models. We conducted semi-structured interviews with leaders from AMCs to assess their use of PM and ML in clinical care, understand associated challenges, and determine recommended best practices. Each transcribed interview was iteratively coded and reconciled by a minimum of 2 investigators to identify key barriers to and facilitators of PM and ML adoption and implementation in clinical care. Interviews were conducted with 33 individuals from 19 AMCs nationally. AMCs varied greatly in the use of PM and ML within clinical care, from some just beginning to explore their utility to others with multiple models integrated into clinical care. Informants identified 5 key barriers to the adoption and implementation of PM and ML in clinical care: (1) culture and personnel, (2) clinical utility of the PM and ML tool, (3) financing, (...
Unplanned hospital readmissions represent a significant health care value problem with high costs... more Unplanned hospital readmissions represent a significant health care value problem with high costs and poor quality of care. A significant percentage of readmissions could be prevented if clinical inpatient teams were better able to predict which patients were at higher risk for readmission. Many of the current clinical decision support models that predict readmissions are not configured to integrate closely with the electronic health record or alert providers in real-time prior to discharge about a patient’s risk for readmission. We report on the implementation and monitoring of the Epic electronic health record—“Unplanned readmission model version 1”—over 2 years from 1/1/2018–12/31/2019. For patients discharged during this time, the predictive capability to discern high risk discharges was reflected in an AUC/C-statistic at our three hospitals of 0.716–0.760 for all patients and 0.676–0.695 for general medicine patients. The model had a positive predictive value ranging from 0.217...
Uploads
Papers by Eric Poon