Healthcare organizations are transitioning from basic to comprehensive electronic health records (EHRs) to meet Meaningful Use requirements and improve patient safety. Yet, full adoption of EHRs is lagging and may be linked to clinician dissatisfaction. In depth assessment of satisfaction before, during, and after EHR transition is rarely done. Using an adapted published tool to assess adoption and satisfaction with EHRs, we surveyed clinicians at a large, non-profit academic medical center before (baseline) and 6-12 months (short-term follow-up) and 12-24 months (long-term follow-up) after transition from a basic, locally-developed to a comprehensive, commercial EHR. Satisfaction with the EHR (overall and by component) was captured at each interval. Overall satisfaction was highest at baseline (85%), lowest at short-term follow-up (66%), and increasing at long-term follow-up (79%). This trend was similar for satisfaction with EHR components designed to improve patient safety including clinical decision support, patient communication, health information exchange, and system reliability. Conversely, at baseline, short-term and long-term follow-up, perceptions of productivity, ability to provide better care with the EHR, and satisfaction with available resources, were lower at both short- and long-term follow-up compared to baseline. Persistent dissatisfaction with productivity and resources was identified. Addressing determinants of dissatisfaction may increase full adoption of EHRs. Further investigation in larger populations is warranted.
Clinicians Satisfaction Before and After Transition from a Basic to a Compreh...Allison McCoy
Healthcare organizations are transitioning from basic to comprehensive electronic health records (EHRs) to meet Meaningful Use requirements and improve patient safety. Yet, full adoption of EHRs is lagging and may be linked to clinician dissatisfaction. In depth assessment of satisfaction before, during, and after EHR transition is rarely done. Using an adapted published tool to assess adoption and satisfaction with EHRs, we surveyed clinicians at a large, non-profit academic medical center before (baseline) and 6-12 months (short-term follow-up) and 12-24 months (long-term follow-up) after transition from a basic, locally-developed to a comprehensive, commercial EHR. Satisfaction with the EHR (overall and by component) was captured at each interval. Overall satisfaction was highest at baseline (85%), lowest at short-term follow-up (66%), and increasing at long-term follow-up (79%). This trend was similar for satisfaction with EHR components designed to improve patient safety including clinical decision support, patient communication, health information exchange, and system reliability. Conversely, at baseline, short-term and long-term follow-up, perceptions of productivity, ability to provide better care with the EHR, and satisfaction with available resources, were lower at both short- and long-term follow-up compared to baseline. Persistent dissatisfaction with productivity and resources was identified. Addressing determinants of dissatisfaction may increase full adoption of EHRs. Further investigation in larger populations is warranted.
The Diabetes Discovery Project at Austin Health aimed to use their Cerner EMR system to routinely test HbA1c levels on inpatients over 54 to identify undiagnosed and poorly controlled diabetes. Testing of over 5,000 patients found 5% had undiagnosed diabetes and 29% had known diabetes. Higher HbA1c levels were associated with increased hospital admissions and longer lengths of stay for surgical patients. The project demonstrated using health IT to identify diabetes management opportunities. Ongoing work includes refining protocols and expanding to other patient populations.
This document describes the development of an electronic workflow system called scope to improve surgical practice at a District Health Board (DHB) hospital. The goals were to seamlessly map the patient journey, accurately collect coded data, and leverage trusted data to inform clinicians. The system streamlines waiting lists, captures accurate operating notes, and facilitates morbidity and mortality meetings. Implementation across surgical specialties has achieved good compliance and uptake. Preliminary results found increased quality of notes, discussion of complications, and potential to change practice through advanced data analysis. In conclusion, scope has replaced a disconnected paper system with a seamless electronic solution that fully captures standardized data to improve surgical outcomes.
Evaluating new models of care: Improvement Analytics UnitNuffield Trust
Martin Caunt, Improvement Analytics Unit Project Director and NHS England and Adam Steventon, Director of Data Analytics at The Health Foundation share insights into how they have approached evaluating new models of care.
Digital medicine technologies like ingestible sensors and connected digital platforms have the potential to revolutionize healthcare by improving medication adherence and returning patients to the center of their own care. Preliminary clinical studies show these digital medicines can achieve near-perfect adherence rates even in high-risk patient groups and lead to significantly improved health outcomes, such as getting 98% of hypertension patients to their blood pressure goal after 12 weeks. The data from these connected digital platforms also enables more effective care by healthcare teams through targeted interventions and recommendations informed by real-time medication ingestion and response insights. As the technologies continue to miniaturize and costs reduce per Moore's Law, digital medicines may help address the worldwide problem of poor medication adherence and its huge impacts on patient
This document discusses predicting patient risk of acquiring Klebsiella pneumoniae carbapenemase producing organisms (KCPO) and linking environmental exposure to patient acquisition. It describes developing a patient risk model using a case-control approach and clinical and demographic data. A naïve Bayesian model was created and validated, showing an AUC of 0.746. It then analyzes the impact of positive room environments on patient infection using a treatment effects model, controlling for patient risk and length of stay. The results show room positivity is significantly associated with acquisition of infection, with an odds ratio of 22.25. Ultimately, interventions like hopper covers and heater traps reduced environmental transmission.
This document summarizes a presentation on using data and informatics to improve allied health services. It discusses the history of allied health and challenges with data collection. Examples are provided of projects in New Zealand that used data to enhance patient and clinician experiences, reduce hospital-acquired infections, and inform staffing needs. The presentation emphasizes standardizing data to facilitate benchmarking and applying knowledge gained from data analysis to drive improvements in allied health.
- Real-time monitoring of healthcare services requires defining both a reporting window and data window to accurately capture demand, activity, and wait times.
- Using only a reporting window (e.g. a single month) to request data can result in invalid or misleading performance metrics, as it does not account for patients with long wait times.
- Defining a larger data window that includes all patients requested before the end of the reporting window and reported after the start avoids this problem, but requires a counterintuitive data request.
- Without properly defining both windows, real-time monitoring can provide an inaccurate picture of service performance and falsely suggest the need for more resources.
Clinicians Satisfaction Before and After Transition from a Basic to a Compreh...Allison McCoy
Healthcare organizations are transitioning from basic to comprehensive electronic health records (EHRs) to meet Meaningful Use requirements and improve patient safety. Yet, full adoption of EHRs is lagging and may be linked to clinician dissatisfaction. In depth assessment of satisfaction before, during, and after EHR transition is rarely done. Using an adapted published tool to assess adoption and satisfaction with EHRs, we surveyed clinicians at a large, non-profit academic medical center before (baseline) and 6-12 months (short-term follow-up) and 12-24 months (long-term follow-up) after transition from a basic, locally-developed to a comprehensive, commercial EHR. Satisfaction with the EHR (overall and by component) was captured at each interval. Overall satisfaction was highest at baseline (85%), lowest at short-term follow-up (66%), and increasing at long-term follow-up (79%). This trend was similar for satisfaction with EHR components designed to improve patient safety including clinical decision support, patient communication, health information exchange, and system reliability. Conversely, at baseline, short-term and long-term follow-up, perceptions of productivity, ability to provide better care with the EHR, and satisfaction with available resources, were lower at both short- and long-term follow-up compared to baseline. Persistent dissatisfaction with productivity and resources was identified. Addressing determinants of dissatisfaction may increase full adoption of EHRs. Further investigation in larger populations is warranted.
The Diabetes Discovery Project at Austin Health aimed to use their Cerner EMR system to routinely test HbA1c levels on inpatients over 54 to identify undiagnosed and poorly controlled diabetes. Testing of over 5,000 patients found 5% had undiagnosed diabetes and 29% had known diabetes. Higher HbA1c levels were associated with increased hospital admissions and longer lengths of stay for surgical patients. The project demonstrated using health IT to identify diabetes management opportunities. Ongoing work includes refining protocols and expanding to other patient populations.
This document describes the development of an electronic workflow system called scope to improve surgical practice at a District Health Board (DHB) hospital. The goals were to seamlessly map the patient journey, accurately collect coded data, and leverage trusted data to inform clinicians. The system streamlines waiting lists, captures accurate operating notes, and facilitates morbidity and mortality meetings. Implementation across surgical specialties has achieved good compliance and uptake. Preliminary results found increased quality of notes, discussion of complications, and potential to change practice through advanced data analysis. In conclusion, scope has replaced a disconnected paper system with a seamless electronic solution that fully captures standardized data to improve surgical outcomes.
Evaluating new models of care: Improvement Analytics UnitNuffield Trust
Martin Caunt, Improvement Analytics Unit Project Director and NHS England and Adam Steventon, Director of Data Analytics at The Health Foundation share insights into how they have approached evaluating new models of care.
Digital medicine technologies like ingestible sensors and connected digital platforms have the potential to revolutionize healthcare by improving medication adherence and returning patients to the center of their own care. Preliminary clinical studies show these digital medicines can achieve near-perfect adherence rates even in high-risk patient groups and lead to significantly improved health outcomes, such as getting 98% of hypertension patients to their blood pressure goal after 12 weeks. The data from these connected digital platforms also enables more effective care by healthcare teams through targeted interventions and recommendations informed by real-time medication ingestion and response insights. As the technologies continue to miniaturize and costs reduce per Moore's Law, digital medicines may help address the worldwide problem of poor medication adherence and its huge impacts on patient
This document discusses predicting patient risk of acquiring Klebsiella pneumoniae carbapenemase producing organisms (KCPO) and linking environmental exposure to patient acquisition. It describes developing a patient risk model using a case-control approach and clinical and demographic data. A naïve Bayesian model was created and validated, showing an AUC of 0.746. It then analyzes the impact of positive room environments on patient infection using a treatment effects model, controlling for patient risk and length of stay. The results show room positivity is significantly associated with acquisition of infection, with an odds ratio of 22.25. Ultimately, interventions like hopper covers and heater traps reduced environmental transmission.
This document summarizes a presentation on using data and informatics to improve allied health services. It discusses the history of allied health and challenges with data collection. Examples are provided of projects in New Zealand that used data to enhance patient and clinician experiences, reduce hospital-acquired infections, and inform staffing needs. The presentation emphasizes standardizing data to facilitate benchmarking and applying knowledge gained from data analysis to drive improvements in allied health.
- Real-time monitoring of healthcare services requires defining both a reporting window and data window to accurately capture demand, activity, and wait times.
- Using only a reporting window (e.g. a single month) to request data can result in invalid or misleading performance metrics, as it does not account for patients with long wait times.
- Defining a larger data window that includes all patients requested before the end of the reporting window and reported after the start avoids this problem, but requires a counterintuitive data request.
- Without properly defining both windows, real-time monitoring can provide an inaccurate picture of service performance and falsely suggest the need for more resources.
The emerging healthcare environment requires expanded patient access while delivering optimal outcomes and cost. As healthcare moves form a fee for service model to alternative delivery and payment models, there are opportunities for physical therapy to revolutionize the delivery of musculoskeletal medicine. Physical therapists are uniquely qualified to spearhead musculoskeletal care through direct access with the potential to improve patient satisfaction and outcomes while limiting unneeded medical care. While this model has been described in the military, there are few descriptions of this PT First approach in the private payer arena. This session will provide the attendee with a multifaceted perspective on the impact of physical therapy in emerging, collaborative healthcare models. Approaches to payers and employers with the business implications will be presented that influence these new models. Key strategies to implement a scalable, best practice model will be discussed including the logistical challenges and corollary solutions in the private arena. We will discus our experience implementing novel delivery models for management of neck, back, shoulder and knee pain. The session will deliver practical solutions to the challenges of implementing, assessing, and adapting a theoretical construct to a working viable program. Finally, the session will discuss how the use of a a large Patient Outcomes Registry and analysis of “big data” can drive best practice and inform development of the program.
How to Define Effective and Efficient Real World TrialsTodd Berner MD
This document discusses strategies for designing effective and efficient real-world clinical trials. It covers topics such as using real-world evidence to inform clinical trial design, the differences between efficacy and effectiveness, challenges around representativeness in trial populations, and the value of pragmatic clinical trials. It also discusses leveraging electronic health records for condition-specific prompts and clinical decision support to improve performance and quality of care.
Prof Devlin discusses the rationale for the PROMs programme and provides an overview of the various uses of the EQ-5D in England—for example by NICE in health technology assessment, in population surveys and in the English NHS PROMS program. The presentation also reviews how EQ-5D data are collected, analysed and used in the UK to inform decisions by health care providers, payers and patients.
An outpatient practice performance program was shown to significantly improve delivery of guideline-recommended care for heart failure patients. The IMPROVE HF study found underuse of heart failure guidelines in many cardiology practices. The practice improvement program measured adherence to quality measures and provided feedback to help increase adherence. Participating practices saw improvements in delivery of heart failure therapies like ACE inhibitors, beta-blockers, and aldosterone antagonists.
Dr Nic Woods discusses tools for early recognition and management of sepsis using the electronic medical record (EMR). Sepsis poses a major global health challenge and burden. Tools discussed include a sepsis predictive model built into the EMR that can detect signs of sepsis with sensitivities of 68-91% and specificities of 91-97.6%. Clinical decision support and workflows in the EMR are also used to alert clinicians and guide treatment. Evaluations found these tools helped reduce mortality from sepsis by 4.2-17% and lower length of hospital stays. Key points emphasized that predictive models integrated into clinical workflows can positively impact outcomes, but more progress is still needed.
Martin Utley, Director of the Clinical Operational Research Unit at University College London, reflects upon his involvement in the launch of specific tools to monitor care quality for paediatric cardiac surgery.
Understand what healthcare analytics is.
Identify the 5-stage Analytics Program Lifecycle (APL).
Understand how data analytics can be used in healthcare.
Check it on Experfy: https://www.experfy.com/training/courses/introduction-to-healthcare-analytics.
The Inferscience introduce Infera, a clinical decision support engine that improves decision making, assisting clinicians to work more quick-witted. In this presentation, you can get the detailed information about this Advanced Clinical Decision Support System.
This document discusses implementing electronic medication reconciliation (eMR) to improve patient safety during transitions of care. eMR embeds the medication reconciliation process within electronic systems used at admission, transfer, and discharge. Pilot programs at two District Health Boards saw improvements like fewer medication errors and increased accuracy and completeness of medication information. Challenges included engaging doctors and changes to workflows. Ongoing efforts are needed for regional clinical information sharing and electronic prescribing across care settings.
Healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve quality of life. It can improve processes, enhance patient care, and save lives by using analytics to better predict patient needs and staff accordingly. Electronic health records store a patient's comprehensive medical history digitally, allowing doctors to track changes over time with no risk of lost data or duplication. Analyzing demographic health data allows for strategic planning to identify factors that discourage treatment uptake. Analytics also helps prevent security threats, fraud, and inaccurate insurance claims while streamlining the claims process. The patient experience, overall population health, and operational costs can all be improved through healthcare analytics.
This document discusses using statistical process control (CUSUM) charts to monitor mortality rates at the level of individual general practitioners and health authorities. It describes how CUSUM charts could potentially have detected Harold Shipman, a GP who murdered over 200 patients, by spotting outliers in the routine mortality data. The document also discusses challenges in risk adjusting outcomes to account for differences in patient characteristics and casemix between providers. Accurately adjusting for factors like age, comorbidities, and emergency status is important for fair comparisons but difficult using only administrative data.
This document discusses telemedicine and reducing healthcare costs through innovative programs. It provides data on the breakdown of healthcare costs by category for a pilot population, showing that hospital care accounts for the largest share. The document also summarizes results from several telemedicine programs that led to reductions in ICU and hospital days, readmission rates, and overall healthcare costs compared to national averages. It concludes that preliminary data suggests healthcare costs were reduced by around 50% through telemedicine interventions but that more investigation is still needed.
The document describes a simulation project called SIMTEGR8 that was conducted to evaluate the impact of interventions from the Better Care Fund on emergency admissions in Leicestershire, UK. The project used simulation modeling to assess four integrated care pathways and provide recommendations. Workshops were held with stakeholders and patients to discuss the pathways and identify issues. The findings from the project informed local commissioning of integrated care under the Better Care Fund.
This document outlines an agenda and case studies for a healthcare analytics bootcamp. The bootcamp will use healthcare data to develop machine learning solutions to predict heart disease and identify high-risk patients. Case Study 1 will involve exploratory data analysis of tuberculosis data to analyze global trends, hotspots, and mortality rates. Case Study 2 will use a heart disease screening dataset and logistic regression to build a model to predict heart disease risk and develop treatment plans for high-risk patients. The document discusses the types of structured and unstructured healthcare data, sources of data, and applications of machine learning in healthcare analytics.
This document provides guidance on conducting audits to assess appropriate use of venous thromboembolism (VTE) prophylaxis in hospitals. It describes snap-shot and detailed audits, resources needed, steps to conduct audits, and how to report and disseminate results to drive quality improvement. The goal is to help close any gaps between evidence-based guidelines and actual clinical practice of VTE prophylaxis prescription and use.
This document summarizes the results of a patient experience survey conducted at over 400 hospitals with 20,000 survey respondents. Some key findings include that 70% would recommend the hospital to others, medication provision and discharge planning could be improved, and 14% reported unexpected medical events during or after hospitalization. The survey aims to monitor patient experiences and outcomes to help hospitals improve quality of care.
Is healthcare getting safer? Professor Charles Vincent - Patient safety lead, Oxford AHSN
Presentation from the Patient Safety Collaborative launch event held in London on 14 October 2014
More information at http://www.nhsiq.nhs.uk/improvement-programmes/patient-safety/patient-safety-collaboratives.aspx
IV Congresso Internacional CBA2017
Emerging Technologies and the Quality of Care
David W. Bates, MD, MSc, Chief, Division of General Internal Medicine, Brigham and Women’s Hospital, Past President, ISQua
The document discusses methods for measuring performance and clinical outcomes in healthcare. It describes the major domains of patient safety measurement as harm, mortality, infections, readmissions, patient satisfaction, and safety culture. It then focuses on defining medical errors and adverse events, and explaining why measurement is important for evaluating current systems and improving outcomes. Different methods of data collection are outlined, including direct observation, cohort studies, record review, and incident reporting systems. The Global Trigger Tool for assessing harm using chart review is also summarized.
How to design effective and efficient real world trials TB Evidence 2014 10.2...Todd Berner MD
This document discusses strategies for designing effective and efficient real-world clinical trials. It covers topics such as using real-world evidence to inform clinical trial design, the differences between efficacy and effectiveness, challenges in defining quality metrics, and strategies for improving performance within healthcare systems. The document provides information on pragmatic clinical trials and how real-world evidence could reduce costs compared to traditional clinical trials.
The emerging healthcare environment requires expanded patient access while delivering optimal outcomes and cost. As healthcare moves form a fee for service model to alternative delivery and payment models, there are opportunities for physical therapy to revolutionize the delivery of musculoskeletal medicine. Physical therapists are uniquely qualified to spearhead musculoskeletal care through direct access with the potential to improve patient satisfaction and outcomes while limiting unneeded medical care. While this model has been described in the military, there are few descriptions of this PT First approach in the private payer arena. This session will provide the attendee with a multifaceted perspective on the impact of physical therapy in emerging, collaborative healthcare models. Approaches to payers and employers with the business implications will be presented that influence these new models. Key strategies to implement a scalable, best practice model will be discussed including the logistical challenges and corollary solutions in the private arena. We will discus our experience implementing novel delivery models for management of neck, back, shoulder and knee pain. The session will deliver practical solutions to the challenges of implementing, assessing, and adapting a theoretical construct to a working viable program. Finally, the session will discuss how the use of a a large Patient Outcomes Registry and analysis of “big data” can drive best practice and inform development of the program.
How to Define Effective and Efficient Real World TrialsTodd Berner MD
This document discusses strategies for designing effective and efficient real-world clinical trials. It covers topics such as using real-world evidence to inform clinical trial design, the differences between efficacy and effectiveness, challenges around representativeness in trial populations, and the value of pragmatic clinical trials. It also discusses leveraging electronic health records for condition-specific prompts and clinical decision support to improve performance and quality of care.
Prof Devlin discusses the rationale for the PROMs programme and provides an overview of the various uses of the EQ-5D in England—for example by NICE in health technology assessment, in population surveys and in the English NHS PROMS program. The presentation also reviews how EQ-5D data are collected, analysed and used in the UK to inform decisions by health care providers, payers and patients.
An outpatient practice performance program was shown to significantly improve delivery of guideline-recommended care for heart failure patients. The IMPROVE HF study found underuse of heart failure guidelines in many cardiology practices. The practice improvement program measured adherence to quality measures and provided feedback to help increase adherence. Participating practices saw improvements in delivery of heart failure therapies like ACE inhibitors, beta-blockers, and aldosterone antagonists.
Dr Nic Woods discusses tools for early recognition and management of sepsis using the electronic medical record (EMR). Sepsis poses a major global health challenge and burden. Tools discussed include a sepsis predictive model built into the EMR that can detect signs of sepsis with sensitivities of 68-91% and specificities of 91-97.6%. Clinical decision support and workflows in the EMR are also used to alert clinicians and guide treatment. Evaluations found these tools helped reduce mortality from sepsis by 4.2-17% and lower length of hospital stays. Key points emphasized that predictive models integrated into clinical workflows can positively impact outcomes, but more progress is still needed.
Martin Utley, Director of the Clinical Operational Research Unit at University College London, reflects upon his involvement in the launch of specific tools to monitor care quality for paediatric cardiac surgery.
Understand what healthcare analytics is.
Identify the 5-stage Analytics Program Lifecycle (APL).
Understand how data analytics can be used in healthcare.
Check it on Experfy: https://www.experfy.com/training/courses/introduction-to-healthcare-analytics.
The Inferscience introduce Infera, a clinical decision support engine that improves decision making, assisting clinicians to work more quick-witted. In this presentation, you can get the detailed information about this Advanced Clinical Decision Support System.
This document discusses implementing electronic medication reconciliation (eMR) to improve patient safety during transitions of care. eMR embeds the medication reconciliation process within electronic systems used at admission, transfer, and discharge. Pilot programs at two District Health Boards saw improvements like fewer medication errors and increased accuracy and completeness of medication information. Challenges included engaging doctors and changes to workflows. Ongoing efforts are needed for regional clinical information sharing and electronic prescribing across care settings.
Healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve quality of life. It can improve processes, enhance patient care, and save lives by using analytics to better predict patient needs and staff accordingly. Electronic health records store a patient's comprehensive medical history digitally, allowing doctors to track changes over time with no risk of lost data or duplication. Analyzing demographic health data allows for strategic planning to identify factors that discourage treatment uptake. Analytics also helps prevent security threats, fraud, and inaccurate insurance claims while streamlining the claims process. The patient experience, overall population health, and operational costs can all be improved through healthcare analytics.
This document discusses using statistical process control (CUSUM) charts to monitor mortality rates at the level of individual general practitioners and health authorities. It describes how CUSUM charts could potentially have detected Harold Shipman, a GP who murdered over 200 patients, by spotting outliers in the routine mortality data. The document also discusses challenges in risk adjusting outcomes to account for differences in patient characteristics and casemix between providers. Accurately adjusting for factors like age, comorbidities, and emergency status is important for fair comparisons but difficult using only administrative data.
This document discusses telemedicine and reducing healthcare costs through innovative programs. It provides data on the breakdown of healthcare costs by category for a pilot population, showing that hospital care accounts for the largest share. The document also summarizes results from several telemedicine programs that led to reductions in ICU and hospital days, readmission rates, and overall healthcare costs compared to national averages. It concludes that preliminary data suggests healthcare costs were reduced by around 50% through telemedicine interventions but that more investigation is still needed.
The document describes a simulation project called SIMTEGR8 that was conducted to evaluate the impact of interventions from the Better Care Fund on emergency admissions in Leicestershire, UK. The project used simulation modeling to assess four integrated care pathways and provide recommendations. Workshops were held with stakeholders and patients to discuss the pathways and identify issues. The findings from the project informed local commissioning of integrated care under the Better Care Fund.
This document outlines an agenda and case studies for a healthcare analytics bootcamp. The bootcamp will use healthcare data to develop machine learning solutions to predict heart disease and identify high-risk patients. Case Study 1 will involve exploratory data analysis of tuberculosis data to analyze global trends, hotspots, and mortality rates. Case Study 2 will use a heart disease screening dataset and logistic regression to build a model to predict heart disease risk and develop treatment plans for high-risk patients. The document discusses the types of structured and unstructured healthcare data, sources of data, and applications of machine learning in healthcare analytics.
This document provides guidance on conducting audits to assess appropriate use of venous thromboembolism (VTE) prophylaxis in hospitals. It describes snap-shot and detailed audits, resources needed, steps to conduct audits, and how to report and disseminate results to drive quality improvement. The goal is to help close any gaps between evidence-based guidelines and actual clinical practice of VTE prophylaxis prescription and use.
This document summarizes the results of a patient experience survey conducted at over 400 hospitals with 20,000 survey respondents. Some key findings include that 70% would recommend the hospital to others, medication provision and discharge planning could be improved, and 14% reported unexpected medical events during or after hospitalization. The survey aims to monitor patient experiences and outcomes to help hospitals improve quality of care.
Is healthcare getting safer? Professor Charles Vincent - Patient safety lead, Oxford AHSN
Presentation from the Patient Safety Collaborative launch event held in London on 14 October 2014
More information at http://www.nhsiq.nhs.uk/improvement-programmes/patient-safety/patient-safety-collaboratives.aspx
IV Congresso Internacional CBA2017
Emerging Technologies and the Quality of Care
David W. Bates, MD, MSc, Chief, Division of General Internal Medicine, Brigham and Women’s Hospital, Past President, ISQua
The document discusses methods for measuring performance and clinical outcomes in healthcare. It describes the major domains of patient safety measurement as harm, mortality, infections, readmissions, patient satisfaction, and safety culture. It then focuses on defining medical errors and adverse events, and explaining why measurement is important for evaluating current systems and improving outcomes. Different methods of data collection are outlined, including direct observation, cohort studies, record review, and incident reporting systems. The Global Trigger Tool for assessing harm using chart review is also summarized.
How to design effective and efficient real world trials TB Evidence 2014 10.2...Todd Berner MD
This document discusses strategies for designing effective and efficient real-world clinical trials. It covers topics such as using real-world evidence to inform clinical trial design, the differences between efficacy and effectiveness, challenges in defining quality metrics, and strategies for improving performance within healthcare systems. The document provides information on pragmatic clinical trials and how real-world evidence could reduce costs compared to traditional clinical trials.
Making Healthcare Waste Reduction and Patient Safety Actionable - HAS Session 6Health Catalyst
Multiple studies have estimated that at least 30% of US healthcare expenditures are wasteful. But how do you identify and reduce that waste? In this session, we will share with you a three-part framework for understanding, measuring and addressing waste reduction. In particular, we will highlight the importance patient safety and injury prevention, framing the importance of shifting from a system of incident reporting (which creates a culture of blame and guilt) to a system in which patient injury is regarded as a process failure rather than a person failure. To make that transition, health systems will need to 1) define process flows and metrics for each major type of patient injury; and 2) create a learning environment in which team members are engaged in process redesign to prevent process failure and injury. A leading health system in patient safety and quality will also share their best practices in how they have created a culture of patient safety and quality.
This document discusses big data in healthcare and physical therapy. It provides an overview of ATI's use of big data through its large patient outcomes registry, which includes over 800 variables and has been accepted into federal registries. ATI leverages data on patient demographics, referrals, outcomes, satisfaction surveys, and costs to enhance care and outcomes. The challenges of evidence-based medicine in an era of big data are also examined, highlighting the need to reconcile evidence-based and precision approaches through standardized sharing of data.
iHT2 Health IT Summit Boston – Larry Garber, Medical Director, Reliant Medical Group Case Study: "Maximizing the Value of an EHR: Beyond Meaningful Use Stage 1"
This session will provide the opportunity to explore how Reliant Medical Group began their journey into EHR and now, after receiving the 2011 HIMSS Ambulatory Davies Award, what it is they have done to capitalize on the EHR. Medical Director for Informatics, Larry Garber, MD stands behind belief that “The EHR enables patients to be more engaged in their health through improved communication with the provider team. The EHR also triggers alerts and automates processes to maintain consistent testing, education and follow up with the providers and patients to ensure higher quality, safer and more efficient care with better outcomes.” This presentation will share with the audience what Reliant Medical Group has done, and is continuing to do, that allows them to maximize the value of the EHR
Learning Objectives:
∙ Understand how Reliant Medical Group effectively implemented the EHR
∙ Develop a deeper understanding of the various ways to best utilize EHR services
∙ Analyze both the pros and cons of implementing and using EHR
Electronic Health Records: Implications for IMO State's Healthcare SystemMichael Loechel
Very high level overview and benefits of Electronic Health Records systems and a multi-phased approach to implementation. By Michael Loechel & Joy Gupta.
The document summarizes key findings from a study on physician retention in rural Michigan communities. It discusses the importance of professional satisfaction, competent medical support staff, and open communication with hospital administration as retention factors. For personal/family retention, safety of the community, comfortable lifestyle, and adequate leisure time were most important. The document also provides a sample rural physician retention plan and tool with steps for onboarding and supporting new physicians.
This document discusses productivity tools in healthcare IT systems and their relationship to patient care. It begins by outlining the concept of using electronic medical record (EMR) and laboratory information management system (LIMS) data to develop more objective measures of clinical management. The present scenario section notes that EMR implementation can initially lower but later increase physician productivity. It also stresses the need to continually adapt processes. Several challenges of EMR are presented, including difficulties with longitudinal patient tracking across multiple providers and issues with system usability and financial impacts. The solution involves using healthcare IT systems to integrate and analyze longitudinal patient data from various sources to facilitate more objective clinical decision-making and monitoring of metrics like productivity and efficiency.
Opening Keynote"From Patient to Population: Providing Optimal Care - The Role for Technology"
Ronald Paulus, MD, MBA
President & CEO
Mission Health System
Patient Engagement is growing in importance as consumer expectations of healthcare providers change and as portals and other technologies improve. Early studies show affects on outcomes for patient engagement technologies
Remote Monitoring of Vital signs of Elderly in the Community: a Feasibility S...Dr. Wilfred Lin (Ph.D.)
This document summarizes a feasibility study on remotely monitoring vital signs of elderly patients in the community. The study found that a simple telemonitoring system using Bluetooth-enabled devices to automatically upload health data and send alerts was easy for elderly patients to use independently. Over a 3-month trial with 30 patients, the system achieved a high 90% adoption rate and 9.2% alert rate, with 0.44% of alerts leading to medical interventions. Both patients and doctors found the system useful and it increased patients' healthcare awareness. The study concluded remote monitoring is a feasible approach that could be incorporated into electronic health records to facilitate earlier diagnosis and improved chronic disease management.
The document summarizes research from the Robert Graham Center on telehealth projects and surveys of family physicians and residency directors. It finds that while family physicians see benefits to telehealth, adoption has been limited due to barriers like reimbursement and technology issues. A survey of family physicians found 15% had used telehealth in the past year, with higher rates in rural areas. Barriers to more use included reimbursement, costs, and preference for in-person visits.
The presentation is about Electronic Health Records. The topic discusses the EHR implementation in organizations and their ongoing maintenance. The following topics are discussed: EHR functionalities, Benefits of EHR, EHR Implementation, After EHR Implementation, Policy in EHR
CoArtha Technolsolutions IT for Meaningful UseMapRecruit.com
CoArtha Technosolutions provides various healthcare IT services including electronic health record systems, healthcare portals, product development, and infrastructure management. The document discusses CoArtha's experience in healthcare domains, technologies used, and services offered to help healthcare providers achieve Meaningful Use of EHRs and qualify for related incentive payments under the HITECH Act.
Acute hospitals end of life care best practiceNHSRobBenson
Delivering reliable best practice in an acute hospital setting for patients whose recovery is uncertain. Including details of the AMBER care bundle. Presentation from Anita Hayes and colleagues from England's National End of Life Care Programme as part of the Department of Health's QIPP end of life care workstream seminar series at Healthcare Innovation Expo 2011
The document recommends continuing to use the Post-Discharge Questionnaire (PDQ) on Unit P7 to assess patient satisfaction and gather data in real-time. Results from the PDQ administered in June 2011 to 101 discharged patients on Unit P7 showed high patient satisfaction scores above the 80th percentile for most discharge measures. Scores were below the 80th percentile for information provided in the discharge packet. The PDQ provided a larger sample size than other surveys and captured feedback that can be used to improve the discharge process.
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Clinician Satisfaction Before and After Transition from a Basic to a Comprehensive Electronic Health Record
1. Clinician Satisfaction Before and
After Transition from a Basic to a
Comprehensive Electronic Health
Record
Allison B. McCoy, PhD
Richard V. Milani, MD
Elizabeth Holt, PhD
Marie Krousel-Wood, MD, MSPH
2. Disclosure Statement
Southern Regional Meeting
February 26-28, 2015
Speaker: Allison B. McCoy, PhD
Dr. McCoy has documented that she has nothing
to disclose.
3. Introduction
• Electronic health records (EHRs)
– Patient safety
– Provider efficiency
• Meaningful use incentive program
• Barriers to EHR adoption
– Costs, return on investment
– Loss of productivity, clinician dissatisfaction
1http://healthit.gov
4. Introduction
• EHR adoption vs. possession
– Adoption – a specified set of EHR functions are
implemented in at least one clinical unit
– Possession – the hospital has a legal agreement
with the EHR vendor, but is not equivalent to
adoption
– 59% vs. 93% (hospitals)1
– 48% vs. 78% (office-based physicians)2
1Charles D, Gabriel M, Furukawa MF. ONC Data Brief, no. 16. 2014. 2Hsiao C-J, Hing E. NCHS data brief, no 143. 2014.
5. Objective
• Assess clinician satisfaction before, during,
and after transition from a basic, locally-
developed EHR to a comprehensive, vendor
EHR
6. Study Setting
• Not-for-profit academic medical center
consisting of 8 hospitals and over 38 clinics in
urban and rural settings
– Preliminary evaluation included one site
• EHR use for more than a decade
7. Survey Methods
• Established survey methods1 based on
published tool2
– Online via e-mail
– Hard copy via standard mail
• Incentives provided
– iPad raffle
– Flash drive or pen with hard copy
1 Dillman, DA, et al. Mail and Internet Surveys: The Tailored Design Method. 2000. 2 DesRoches CM, et al. N Engl J Med 2008.
8. Survey Components
EHR System Use
• The EHR decreases time in scheduling of consults.
• …decreases the time in getting results of consults.
• …allows me to access, store and retrieve patient information
without difficulties.
• …provides easy access to relevant clinical information when
patients are transitioning between hospital to clinic or clinic to
hospital.
• …provides timely and accurate information to me.
• …is a valuable aid to me in tracking and/or monitoring patients.
9. Survey Components
EHR System Use
• The EHR allows me to spend more time on other aspects of patient care.
• …increases coordination between departments.
• …facilitates the process of scheduling patients.
• …improves the safety of patients.
• …improves my productivity on the job.
• …allows me to provide better care for my patients.
• I have sufficient access to computers with the EHR.
• There are adequate resources (staff, training, help lines) available to turn
to for help in solving problems with the EHR.
10. Survey Components
Assessment of the EHR
• The EHR positively affects the quality of clinical decisions.
• …facilitates communication with other providers.
• …facilitates communication with my patients.
• …assists with prescription refills.
• …provides me with timely access to medical records.
• …helps providers to avoid medication errors.
• …facilitates the delivery of preventive care that meets guidelines.
11. Survey Components
Patient Care
• In providing patient care, have you avoided a drug allergy because
of the EHR?
• …avoided a potentially dangerous medication interaction because
of the EHR?
• …been alerted to a critical lab value because of the EHR?
• …provided preventive care (e.g. vaccine, colonoscopy,
mammogram) because you were prompted by the EHR?
• …ordered an indicated lab test (such as A1c or LDL) as a result of an
electronic prompt from the EHR?
12. Survey Components
Satisfaction
• Overall, how satisfied are you with the EHR system?
• How satisfied are you with the ease of use when providing direct
care to a patient.
• …the reliability of the system (i.e. frequency of system failures,
system speed).
• …the sharing of medical information with system hospitals and
health-care providers.
• …obtaining medical information from outside hospitals and
providers
14. Analysis
• Unadjusted – McNemar’s chi-squared test
• Adjusted – random effect logistic regression
– Age
– Gender
– Setting (outpatient vs. inpatient vs. both)
– Practice (primary care vs. specialty care)
– Time worked at study setting
15. Study Eligibility and Response
Contact information for active providers obtained
Ineligible
CRNAs, Residents, Fellows, PRN, Worked < 6
months, Resign/Retire over study period, etc.
Eligible
Active MDs, DOs, NP, PA, Optometry, Mental
Health Professionals
Baseline Respondents (N=83)
First Follow-up Respondents (N=51)
Second Follow-up Respondents (N=47) * Recapture Rate: 47/83 (57%)
16. Respondents
Gender Male
Female
29 (62%)
18 (38%)
Age 26-35 years old
36-45 years old
46-55 years old
56-65 years old
> 65 years old
4 (9%)
16 (34%)
13 (28%)
12 (26%)
2 (4%)
Training Staff Physician - MD
Staff Physician - DO
Mid Level Provider
Optometrist
Mental Health Professional
35 (76%)
1 (2%)
7 (15%)
2 (4%)
1 (2%)
17. Respondents
Worked at
Study
Setting
< 1 year
1 to < 5 years
5 to < 10 years
10 to < 20 years
20 years or more
3 (6%)
13 (28%)
18 (38%)
9 (19%)
4 (9%)
Setting Outpatient only
Inpatient only
Outpatient and inpatient
24 (51%)
5 (11%)
18 (38%)
Practice Primary Care
Medical Specialty
Surgical Specialty
Hospital Medicine
Anesthesia
Laboratory/Radiology Services
19 (42%)
6 (13%)
12 (27%)
4 (9%)
2 (4%)
2 (4%)
19. Patient Safety
19%
83%
36%
72%
60%
9%
68%
13%
51%
57%
11%
66%
13%
40%
47%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Ordered an indicated lab test (such as A1c or LDL)
as a result of an electronic prompt from the EHR.
Been alerted to a critical lab value because of the
EHR.
Provided preventive care because you were
prompted by the EHR?
I feel the EHR improves the safety of patients.
Using the EHR facilitates the delivery of preventive
care that meets guidelines.
Baseline 6 Months 1 Year
Note: Satisfaction = very satisfied/somewhat satisfied, strongly agree/agree, in the last 6 months/ever
*Unadjusted p < 0.05, †Adjusted p < 0.05
*
*
*
*
*
20. Health Information Exchange
38%
91%
85%
68%
64%
32%
96%
79%
64%
53%
26%
74%
87%
57%
38%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Obtaining medical information from outside hospitals and
providers.
Sharing of medical information with system hospitals and
health-care providers.
The EHR facilitates communication with other providers.
The EHR facilitates communication with my patients.
When patients are transitioning between hospital to clinic
or clinic to hospital, the EHR provides easy access to
relevant clinical information.
Baseline 6 Months 1 Year
Note: Satisfaction = very satisfied/somewhat satisfied, strongly agree/agree, in the last 6 months/ever
*Unadjusted p < 0.05, †Adjusted p < 0.05
*
* *
21. Productivity and Patient Care
40%
15%
68%
49%
28%
34%
17%
68%
43%
26%
62%
21%
83%
81%
45%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
There are adequate resources available to turn to for help
in solving problems with the EHR.
The EHR allows me to spend more time on other aspects of
patient care.
Ease of use when providing direct care to a patient.
Using the EHR allows me to provide better care for my
patients.
Using the EHR improves my productivity on the job.
Baseline 6 Months 1 Year
Note: Satisfaction = very satisfied/somewhat satisfied, strongly agree/agree, in the last 6 months/ever
*Unadjusted p < 0.05, †Adjusted p < 0.05
* *
* *
* *
*
†
22. Strengths and Limitations
• Longitudinal data over
three time periods
• Majority adopters in
real world setting vs.
innovators and early
adopters
• Single study site
• Modest response rate
23. Conclusions
• Overall and after adjustment for age, gender,
time in practice, and specialty, non-significant
trends of initial lower satisfaction and subsequent
improvement in satisfaction over time were
identified.
• Increasing trends were identified in several items
related to patient safety and health information
exchange.
24. Conclusions
• Assessment of these trends in a larger sample is
underway.
• Longer follow up is necessary to determine if
EHRs demonstrate improvements over time in
patient care and safety in real-world settings.
• Further research includes opportunities to
identify components predictive of safety, quality,
and EHR use.
Thank you for the introduction. I’m excited to present this work that my colleagues and I have been working on to study the satisfaction and adoption of electronic health records.
Before I get started, I have no disclosures to report.
Implementations of electronic health records, or EHRs, have been increasing in recent years for many reasons. Most importantly, research has indicated that they have the potential to improve patient safety through clinical decision support and other features. They can also improve provider efficiency, for example by making patient information more readily available and improving communication.
Meaningful Use has been another reason for increased adoption, and this is an incentive program from CMS. Its goal is to promote so-called meaningful use of EHRs, to increase data capturing, advance clinical processes, and improved outcomes.
There are some known barriers to EHR adoption, and these primarily include concerns about cost and return on investment, along with the potential for loss of productivity and resulting clinician dissatisfaction.
Some recent reports have been released describing EHR adoption across the country. One thing to note is that there is a distinction between having actually adopted an EHR compared to being in possession of an EHR. For these reports, they defined adoption as having implemented a specified set of EHR functions in at least one unit, such as having electronic patient data and computerized provider order entry. Hospitals and physicians can be in possession of an EHR or have implemented some of the software functionalities without having actually adopted the EHR.
What these reports found is that despite all of the incentives for having EHRs, actual adoption is low. For hospitals, 59% met the criteria for having fully adopted an EHR, compared to 93% of hospitals who have are in possession. Similarly, only 48% of office-based physicians have fully adopted an EHR, compared to 78% who are in possession of one. While these numbers are significantly increased from previous reports over the last decade, there is still a lot of room for improvement.
Given the potential for dissatisfaction to affect EHR adoption, our objective in this study was to assess clinician satisfaction before, during, and after transition from a basic, locally-developed EHR to a comprehensive, vendor EHR – in our case, Epic.
We performed our study in a not-for-profit academic medical center that consists of 8 hospitals and over 38 clinics in urban and rural settings. What I’m presenting today is just a preliminary evaluation of the data, which includes satisfaction at one of these sites. One of the great things about this medical center is that they have been using an EHR for more than a decade, although the switch to the vendor system is more recent.
We used established survey methods and repurposed an existing survey, published in the New England Journal, to assess EHR satisfaction and adoption. We first sent out e-mails to the clinicians with a link to the survey, and after about 10 e-mails, if they hadn’t yet responded, we sent out hard copies about 3 different times via standard mail with an enclosed self-addressed stamped envelope. We did provide incentives for completing the survey, so all respondents were entered into a raffle to win an iPad, and hard copy surveys that were mailed out included either a flash drive or a pen.
There were 4 main components to the survey, each with a set of corresponding questions that we asked. The first component addressed EHR system use, so we asked questions about whether the EHR helped with consults and accessing patient information.
Other questions about EHR system use asked about patient care, processes, safety, productivity, and access to resources.
We asked them about their assessment of the EHR, including how it affects the quality of clinical decisions, communication, access to records, avoiding medication errors, and delivery of preventive care.
Questions about patient care asked whether the EHR resulted in the avoiding interactions, whether they had been alerted to critical labs, provided preventive care, and ordered an indicated tests.
Finally, we asked about their satisfaction, including how satisfied they were overall, and with different specific components.
We went out 3 surveys to the clinicians. The baseline survey was sent out while the clinicians were using the older, locally-developed, basic EHR. We sent out the first followup survey approximately 6 months after we implemented and made the transition to the new, comprehensive EHR, and we sent the second followup survey out about 6 months after that.
We first compared the responses for the three surveys using McNemar’s chi-squared tests, and then we used a random effect logistic regression model to adjust for age, gender, setting, practice, and time worked at the study setting.
Eligible providers included full time active physicians, nurse practitioners, physician assistants, and optometrists. We excluded CRNAs, trainees, those who had worked at the study setting for less than 6 months, and those who resigned or retired during the study period. As I mentioned, these results are preliminary and do not include all sites at the institution, so I only have the numbers starting from those who completed the baseline survey. I do know that in the whole population, we had about 1300 active providers, 1130 eligible (86%), and 580 baseline respondents (52%), so I suspect that the response rates for this setting similar. We had 83 clinicians respond to the baseline survey, 51 respond to the first follow up, and 47 respond to the second follow up, which is a 57% recapture rate from baseline.
Among those who responded, 62% were male, the majority were between 26 and 65 years old, and most were staff physicians.
The respondents had mostly worked at the institution for 1 to 20 years, half were in an outpatient only practice, and a little bit less than half were in a primary care practice.
Overall satisfaction was highest at baseline (85% satisfied), lowest at 6 months (66% satisfied), and increasing at 1 year (79% satisfied). Unadjusted differences in satisfaction between baseline and 6 months and between 6 months and 1 year were statistically significant, but the difference between baseline and 1 year was not. However, in the adjusted model, none of the differences were significant, likely due to the smaller sample size.
This trend was similar across most of the questions we asked, where satisfaction dropped at 6 months and came back up close to baseline at a year, but there were some responses with a different trend, so I’ll go through those selected responses in more detail.
Some of the questions about patient safety actually had an increasing trend at both 6 months and 1 year. Some of these were significant increases initially, but none of them were significant after adjustments.
The increasing trends continued for some questions about information exchange with the EHR. Again, some of these were significant increases before adjustments, but none of them were significant afterward.
Some responses actually went in the opposite direction, for example some of the questions about perceived productivity and ability to provide patient care. Only one of these was statistically significant in our adjusted analysis, and this was the difference between baseline and 6 months for whether clinicians felt like they could provide better care while using the EHR. This is one area where we can really spend some time on future research identifying problems and making improvements to satisfaction and adoption.
Our study has some important strengths and limitations. One of the strengths is that we have longitudinal data over three time periods about clinician satisfaction instead of data at just a single point in time. Our study is also really unique in that we did our survey in a real world, typical setting, compared to innovators and early adopters that were assessed in previous studies.
The limitations, however, are that in this analysis we were only looking at a single study site, although some early analyses of the larger population confirm that the trends we saw here persist across all study sites, so I expect we’ll have some more significant findings when we finish that evaluation. We also had a modest response rate for the surveys at about 50%, but with a large population of busy clinicians with 3 surveys, I think this is pretty acceptable.
The primary conclusions from this study are that while overall satisfaction trended downward initially, we did see an increasing trend over time, so it’s important to continually assess satisfaction and make improvements wherever possible when implementing new technology. There are a lot of opportunities for further research, including opportunities to identify components predictive of safety, quality, and EHR use. In particular, we should explore in detail some of the responses, especially the perceptions on the ability to provide better care, to see if we can make improvements.
The primary conclusions from this study are that while overall satisfaction trended downward initially, we did see an increasing trend over time, so it’s important to continually assess satisfaction and make improvements wherever possible when implementing new technology. There are a lot of opportunities for further research, including opportunities to identify components predictive of safety, quality, and EHR use. In particular, we should explore in detail some of the responses, especially the perceptions on the ability to provide better care, to see if we can make improvements.
The implications of this research are huge, so hopefully with these results we can work in overcoming barriers that are preventing clinicains from fully adopting EHRs, we can improve the training and rollout of these EHRs, and ultimately we can improve patient safety and quality.