The document outlines statistical analyses and performance dashboards developed by Robert Sutter using patient data to identify opportunities for performance improvement. Key points include:
- Dashboards were created to monitor quality performance metrics and catalyze annual improvement planning across multiple health systems.
- Additional analyses of SCIP, STS, and ACC data revealed factors associated with outcomes and complications. This stimulated numerous system-wide performance projects.
- Physician and hospital comparisons using the data identified best practices and areas for benchmarking, focusing improvement efforts.
1) The article discusses concerns with tying individual physician performance to scores from the Clinician and Group Consumer Assessment of Healthcare Providers and Systems (CG-CAHPS) surveys as directed by the Affordable Care Act.
2) The concerns center around the survey's use of an extrinsic rather than intrinsic approach, measurement issues around attributing scores to individual physicians, and potential unintended consequences such as focus on scores over quality.
3) The authors suggest allowing an opt-out pathway for organizations to develop their own internal patient experience measures as an alternative to the CG-CAHPS program.
Value of Medication Adherence in Chronic Vascular Disease: Fixed Effects Mode...M. Christopher Roebuck
This document summarizes a study that examined the impact of medication adherence on health services utilization and costs for patients with chronic vascular conditions. The study used claims data from over 135,000 patients to measure adherence rates and model the relationship between adherence and outcomes. The results showed that optimal adherence was associated with higher pharmacy costs but lower medical costs, leading to overall savings. Adherence had a greater impact on reducing utilization and costs for elderly patients compared to non-seniors.
Atlantic Health System Case Study for McKessonLori Gilchrist
Atlantic Health System implemented McKesson Analytics Explorer and McKesson Performance Analytics to improve data analysis capabilities for quality improvement initiatives. The new tools allowed them to combine data from multiple sources, visualize relationships within the data, and provide customizable dashboards to key stakeholders. This empowered users to identify root causes of quality gaps and directly influence patient care. Access to integrated, high-quality data helped reduce medical errors and length of hospital stays.
Recent reports indicate that physicians are stressed and overburdened by several administrative challenges, leaving them with less time for patient care.
The document describes the implementation of a clinical decision support system (CDSS) for glucose control on an intensive cardiac care unit. [1] Adherence to the existing paper glucose control protocol was low. [2] The CDSS automated the paper protocol and displayed recommendations at nurses' workstations, improving adherence and glucose measurement timeliness. [3] Future work includes incorporating a third-party guideline authoring tool and expanding the CDSS to other devices and organizations.
The Top 7 Outcomes Measures and 3 Measurement EssentialsHealth Catalyst
Outcomes improvement can’t happen without effective outcomes measurement. Given the healthcare industry’s administrative and regulatory complexities, and the fact that health systems measure and report on hundreds of outcomes annually, this blog adds much-needed clarity by reviewing the top seven outcome measures, including definitions, important nuances, and real-life examples:
Mortality
Readmissions
Safety of care
Effectiveness of care
Patient experience
Timeliness of care
Efficient use of medical imaging
CMS used these exact seven outcome measures to calculate overall hospital quality and arrive at its 2016 hospital star ratings. This blog also reiterates the importance of outcomes measurement, clarifies how outcome measures are defined and prioritized, and recommends three essentials for successful outcomes measurement:
Transparency
Integrated care
Interoperability
The Changing Role of Healthcare Data AnalystsHealth Catalyst
The healthcare industry is undergoing a sea change, and healthcare data analysts will play a central role in this transformation. This report explores how the evolution to value-based care is changing the role of healthcare data analysts, how data analysts’ skills can best be applied to achieve value-based objectives and, finally, how Health Catalyst’s most successful health system clients are making this cultural transformation happen in the real world.
1) The article discusses concerns with tying individual physician performance to scores from the Clinician and Group Consumer Assessment of Healthcare Providers and Systems (CG-CAHPS) surveys as directed by the Affordable Care Act.
2) The concerns center around the survey's use of an extrinsic rather than intrinsic approach, measurement issues around attributing scores to individual physicians, and potential unintended consequences such as focus on scores over quality.
3) The authors suggest allowing an opt-out pathway for organizations to develop their own internal patient experience measures as an alternative to the CG-CAHPS program.
Value of Medication Adherence in Chronic Vascular Disease: Fixed Effects Mode...M. Christopher Roebuck
This document summarizes a study that examined the impact of medication adherence on health services utilization and costs for patients with chronic vascular conditions. The study used claims data from over 135,000 patients to measure adherence rates and model the relationship between adherence and outcomes. The results showed that optimal adherence was associated with higher pharmacy costs but lower medical costs, leading to overall savings. Adherence had a greater impact on reducing utilization and costs for elderly patients compared to non-seniors.
Atlantic Health System Case Study for McKessonLori Gilchrist
Atlantic Health System implemented McKesson Analytics Explorer and McKesson Performance Analytics to improve data analysis capabilities for quality improvement initiatives. The new tools allowed them to combine data from multiple sources, visualize relationships within the data, and provide customizable dashboards to key stakeholders. This empowered users to identify root causes of quality gaps and directly influence patient care. Access to integrated, high-quality data helped reduce medical errors and length of hospital stays.
Recent reports indicate that physicians are stressed and overburdened by several administrative challenges, leaving them with less time for patient care.
The document describes the implementation of a clinical decision support system (CDSS) for glucose control on an intensive cardiac care unit. [1] Adherence to the existing paper glucose control protocol was low. [2] The CDSS automated the paper protocol and displayed recommendations at nurses' workstations, improving adherence and glucose measurement timeliness. [3] Future work includes incorporating a third-party guideline authoring tool and expanding the CDSS to other devices and organizations.
The Top 7 Outcomes Measures and 3 Measurement EssentialsHealth Catalyst
Outcomes improvement can’t happen without effective outcomes measurement. Given the healthcare industry’s administrative and regulatory complexities, and the fact that health systems measure and report on hundreds of outcomes annually, this blog adds much-needed clarity by reviewing the top seven outcome measures, including definitions, important nuances, and real-life examples:
Mortality
Readmissions
Safety of care
Effectiveness of care
Patient experience
Timeliness of care
Efficient use of medical imaging
CMS used these exact seven outcome measures to calculate overall hospital quality and arrive at its 2016 hospital star ratings. This blog also reiterates the importance of outcomes measurement, clarifies how outcome measures are defined and prioritized, and recommends three essentials for successful outcomes measurement:
Transparency
Integrated care
Interoperability
The Changing Role of Healthcare Data AnalystsHealth Catalyst
The healthcare industry is undergoing a sea change, and healthcare data analysts will play a central role in this transformation. This report explores how the evolution to value-based care is changing the role of healthcare data analysts, how data analysts’ skills can best be applied to achieve value-based objectives and, finally, how Health Catalyst’s most successful health system clients are making this cultural transformation happen in the real world.
From Installed to Stalled: Why Sustaining Outcomes Improvement Requires More ...Health Catalyst
The big first step toward building an outcomes improvement program is installing the analytics platform. But it’s certainly not the only step. Sustaining healthcare outcomes improvement is a triathlon, and the three legs are:
Installing an analytics platform
Gaining adoption
Implementing best practices
The program requires buy-in, enthusiasm, even evangelizing of analytics and its tools throughout the organization. It also requires that learnings from analysis translate into best practices, otherwise the program fails to produce results and will eventually fade away. Equally important is that top-level leadership across the organization, not just IT, supports and promotes the program ongoing. We explore each of the elements and how they come together to create successful and sustainable outcomes improvement that defines leading healthcare organizations.
This document discusses several topics related to improving healthcare quality including data transparency, evidence-based medicine, pay for performance programs, and using these strategies together as a new engine for healthcare quality improvement. It provides examples of how public reporting of outcomes data has driven quality improvement. It also outlines 10 challenges healthcare organizations must address to be ready for ongoing quality and payment reform.
This document discusses how data transparency, evidence-based medicine, and pay for performance can turbo-charge quality improvement in healthcare. Public reporting of quality data has incentivized hospitals and physicians to improve care through reducing mortality rates and adhering to guidelines. While perfect quality may not be achievable, guidelines standardized from evidence-based practices can reduce errors when properly implemented.
Five Strategies for Easing the Burden of Clinical Quality MeasuresHealth Catalyst
Healthcare systems need to view regulatory measures in a different light. Rather than approaching them as required processes that burden the system, they should be viewed as quality improvement opportunities that lead to best practices. It helps to have a strategy to get there:
Prioritize measures that truly impact patient care
Have a line-of-sight to reimbursement
Understand measure alignment across programs
Involve the right people
Get involved in measure development upstream
The right tools also help, but a plan for success is advised for healthcare system administrators and clinicians who need to ease the reporting burden and take advantage of every measure in a positive way.
This document discusses the challenges of testing electronic health record (EHR) software and provides recommendations for improving the testing process. It notes that EHR software involves complex workflows across many systems, making testing difficult. It recommends developing a testing strategy that includes risk analysis to prioritize what to test. This strategy should define testing scope, coverage and responsibilities. It also suggests using manual test cases, exploratory testing, or checklists as testing methods depending on resources and needs. The overall goal is to establish an efficient yet effective testing process to ensure software quality and patient safety.
TRI was founded as a subsidiary of Triumph Consultancy Services in 2013, following 12 years of consulting to the clinical trial industry. TRI has been evaluating the specific challenges facing the industry when implementing a risk-based monitoring strategy and the various approaches and products being utilized by organizations as they move into the RBM arena. This paper aims to summarize our findings and provide guidance as to how the main challenges can be overcome.
Looking Back on Clinical Decision Support and Data WarehousingHealth Catalyst
Dale will take a slide deck previously prepared in 2006, from a lecture entitled, "The Power of an Enterprise Data Warehouse in Clinical Decision Support", presented to several informatics masters classes at Northwestern University and the University of Victoria. He won’t change anything about the slide deck, including the content and the old school graphics. The concept with this webinar is to give a “time capsule” perspective on past thinking and contrast that against current thoughts and trends in the market. Some of the information will be laughably wrong and naive, and some of the information will still be relevant. The hope is, by regularly reviewing our past, we will better inform our future.
This study analyzed healthcare provider attrition rates at a major academic medical center before and after the implementation of a new electronic medical record (EMR) system. The study found 208 provider departures from July 2011 to June 2014, with most (66.4%) occurring before the EMR go-live date in July 2013. The peak in attrition occurred in June 2013, just before implementation. While the cause is unclear, the authors hypothesize EMR implementation may have influenced some providers to retire rather than adapt to the new system.
WHEN AND HOW DOES VALUE BASED PURCHASING IMPACT HOSPITAL PERFORMANCE?Kirsty Macauldy, MBA
To improve the overall quality of healthcare, The National Quality Strategy of the U.S. Department of Health and Human Services broadly defines the outcomes that the Centers for Medicare and Medicaid Services (CMS) wants to achieve through the care it purchases for its beneficiaries. The strategies; aims of better health, better care, and lower costs.
This document provides an overview of Medicaid claims data and how it can be used for program evaluation and research. It describes the key components of Medicaid claims data including eligibility data, diagnostic codes, procedure codes, and expenditures. It outlines some of the strengths of claims data for population monitoring, benchmarking, and expenditure analysis, as well as limitations related to clinical validity and completeness. Accessing Medicaid claims data requires working with state Medicaid agencies or research groups that have obtained the data.
Genomic Medicine: Personalized Care for Just PenniesHealth Catalyst
The document discusses the progress and future of genomic medicine. The cost of sequencing a human genome has declined drastically from $100 million to an expected cost of just pennies by 2020. This will enable more personalized care based on a patient's genomic profile. Genomic analysis is already improving diagnosis and treatment for various diseases like rare genetic disorders and cancer. In the future, genomic data combined with sensor data will generate huge amounts of healthcare data and further advance personalized medicine.
The document analyzes the relationship between clinical computing systems used by family practices in the UK and their performance under the Quality and Outcomes Framework (QOF) pay-for-performance scheme between 2007-2011. Statistical models found that practices' choice of clinical computing system was a significant predictor of their QOF achievement scores, with some systems associated with better performance than others. Practices using the Vision 3 or Synergy systems tended to score highest overall, while those using the PCS system tended to score the lowest. Performance varied by the type of clinical activities as well.
Innovative Strategies For Successful Trial Design - Webinar SlidesnQuery
Full webinar available here: https://www.statsols.com/webinar/innovative-strategies-for-successful-trial-design
[Webinar] Innovative Strategies For Successful Trial Design- In this free webinar, you will learn about:
- The challenges facing your trials
- How to calculate the correct sample size
- Worked examples including Mixed/Hierarchical Models
- Posterior Error
- Adaptive Designs For Survival
www.statsols.com
This document describes the development of a Computerized Physician Order Entry (CPOE) system with a Decision Support System (DSS) and an assessment of physician attitudes towards the system. Key points:
- Researchers developed a comprehensive drug database for the CPOE/DSS using a commercial drug information resource and Microsoft SQL Server.
- They administered a questionnaire to 25 physicians at a 1600-bed teaching hospital to assess preconceived attitudes towards CPOE/DSS and measure end-user satisfaction.
- Results showed most physicians agreed the system could improve patient safety, reduce medication errors, and were easy to use, though some had doubts about data reliability and completeness. Overall, 88% agreed
A webinar hosted by CHIME. It shared thoughts on one of my areas of interest – harnessing both business intelligence and health IT, for more effective measurement of healthcare performance.
Demystifying Text Analytics and NLP in HealthcareHealth Catalyst
Leading the discussion, we have two exceptional thinkers in this space, Mike Dow, a former CIO and current Health Catalyst product manager and software developer, and Dr. Carolyn Simpkins, Health Catalyst’s Chief Medical Informatics Officer.
They will share thoughts on the challenges of text in clinical analytics as well as demonstrate:
Why text is an important part of clinical analytics
Why a text search is not enough
How clinical text search can be refined with NLP techniques
Healthcare Interoperability: New Tactics and TechnologyHealth Catalyst
Every provider agrees on the need for healthcare interoperability to achieve clinical data insights at the point of care. The question is how to get there from the myriad technologies and the volumes of data that comprise electronic medical records. It’s been difficult to organize among participants that have had little incentive to cooperate. And standards for sending and receiving data have been slow to develop. This is changing, but the key components that are still vital to realizing insights are closed-loop analytics and its accompanying tools, an enterprise data warehouse and analytics applications. This article defines the problems and explores the solutions to optimizing clinical decision making where it’s needed most.
What Veterinarians Can Learn From Physician Practice Modelsmjmcgaunn
Veterinarians can learn from physician practice models that aim to gain market share through innovation and niche services. Concierge medicine offers patients enhanced services for an annual retainer fee averaging $10,000. Compensation for veterinarians should balance incentives for individual and team performance with base salaries that increase with experience and responsibilities. Electronic medical records can reduce medical errors and some hospitals have seen a 7.2% lower mortality rate when using health IT.
Drug Utilization in a regulated EnviormentAlok Anand
Tracking drugs across the supply chain in a regulated environment. This white paper brief on would be drug utilization approach of Life Science Industry. This white paper is just a step forward to show future life science industry process automation
This document discusses using chi-square tests to analyze categorical healthcare data and determine if there are significant differences between groups. Chi-square tests can be used to analyze factors like physician prescribing patterns, order set adherence, and their effects on appropriate treatment. Quality improvement implications may include targeting education or interventions at physicians or factors found to have non-equivalent outcomes. The document provides examples of chi-square test questions, assumptions, and interpreting results to identify opportunities to improve care quality.
The document discusses hypothesis testing and its use in quality improvement to determine if potential factors significantly affect process performance. Hypothesis testing involves determining if a null hypothesis (that outcomes are produced by similar processes) or alternative hypothesis (that outcomes are produced by dissimilar processes) is correct. A statistically significant result, where the p-value is below a threshold like 0.05, means the null hypothesis can be rejected, indicating the factor affects performance and is a target for process improvement. The example compares weekday and weekend antibiotic timing for pneumonia patients, finding a significant difference, suggesting the administration process differs between weekdays and weekends.
From Installed to Stalled: Why Sustaining Outcomes Improvement Requires More ...Health Catalyst
The big first step toward building an outcomes improvement program is installing the analytics platform. But it’s certainly not the only step. Sustaining healthcare outcomes improvement is a triathlon, and the three legs are:
Installing an analytics platform
Gaining adoption
Implementing best practices
The program requires buy-in, enthusiasm, even evangelizing of analytics and its tools throughout the organization. It also requires that learnings from analysis translate into best practices, otherwise the program fails to produce results and will eventually fade away. Equally important is that top-level leadership across the organization, not just IT, supports and promotes the program ongoing. We explore each of the elements and how they come together to create successful and sustainable outcomes improvement that defines leading healthcare organizations.
This document discusses several topics related to improving healthcare quality including data transparency, evidence-based medicine, pay for performance programs, and using these strategies together as a new engine for healthcare quality improvement. It provides examples of how public reporting of outcomes data has driven quality improvement. It also outlines 10 challenges healthcare organizations must address to be ready for ongoing quality and payment reform.
This document discusses how data transparency, evidence-based medicine, and pay for performance can turbo-charge quality improvement in healthcare. Public reporting of quality data has incentivized hospitals and physicians to improve care through reducing mortality rates and adhering to guidelines. While perfect quality may not be achievable, guidelines standardized from evidence-based practices can reduce errors when properly implemented.
Five Strategies for Easing the Burden of Clinical Quality MeasuresHealth Catalyst
Healthcare systems need to view regulatory measures in a different light. Rather than approaching them as required processes that burden the system, they should be viewed as quality improvement opportunities that lead to best practices. It helps to have a strategy to get there:
Prioritize measures that truly impact patient care
Have a line-of-sight to reimbursement
Understand measure alignment across programs
Involve the right people
Get involved in measure development upstream
The right tools also help, but a plan for success is advised for healthcare system administrators and clinicians who need to ease the reporting burden and take advantage of every measure in a positive way.
This document discusses the challenges of testing electronic health record (EHR) software and provides recommendations for improving the testing process. It notes that EHR software involves complex workflows across many systems, making testing difficult. It recommends developing a testing strategy that includes risk analysis to prioritize what to test. This strategy should define testing scope, coverage and responsibilities. It also suggests using manual test cases, exploratory testing, or checklists as testing methods depending on resources and needs. The overall goal is to establish an efficient yet effective testing process to ensure software quality and patient safety.
TRI was founded as a subsidiary of Triumph Consultancy Services in 2013, following 12 years of consulting to the clinical trial industry. TRI has been evaluating the specific challenges facing the industry when implementing a risk-based monitoring strategy and the various approaches and products being utilized by organizations as they move into the RBM arena. This paper aims to summarize our findings and provide guidance as to how the main challenges can be overcome.
Looking Back on Clinical Decision Support and Data WarehousingHealth Catalyst
Dale will take a slide deck previously prepared in 2006, from a lecture entitled, "The Power of an Enterprise Data Warehouse in Clinical Decision Support", presented to several informatics masters classes at Northwestern University and the University of Victoria. He won’t change anything about the slide deck, including the content and the old school graphics. The concept with this webinar is to give a “time capsule” perspective on past thinking and contrast that against current thoughts and trends in the market. Some of the information will be laughably wrong and naive, and some of the information will still be relevant. The hope is, by regularly reviewing our past, we will better inform our future.
This study analyzed healthcare provider attrition rates at a major academic medical center before and after the implementation of a new electronic medical record (EMR) system. The study found 208 provider departures from July 2011 to June 2014, with most (66.4%) occurring before the EMR go-live date in July 2013. The peak in attrition occurred in June 2013, just before implementation. While the cause is unclear, the authors hypothesize EMR implementation may have influenced some providers to retire rather than adapt to the new system.
WHEN AND HOW DOES VALUE BASED PURCHASING IMPACT HOSPITAL PERFORMANCE?Kirsty Macauldy, MBA
To improve the overall quality of healthcare, The National Quality Strategy of the U.S. Department of Health and Human Services broadly defines the outcomes that the Centers for Medicare and Medicaid Services (CMS) wants to achieve through the care it purchases for its beneficiaries. The strategies; aims of better health, better care, and lower costs.
This document provides an overview of Medicaid claims data and how it can be used for program evaluation and research. It describes the key components of Medicaid claims data including eligibility data, diagnostic codes, procedure codes, and expenditures. It outlines some of the strengths of claims data for population monitoring, benchmarking, and expenditure analysis, as well as limitations related to clinical validity and completeness. Accessing Medicaid claims data requires working with state Medicaid agencies or research groups that have obtained the data.
Genomic Medicine: Personalized Care for Just PenniesHealth Catalyst
The document discusses the progress and future of genomic medicine. The cost of sequencing a human genome has declined drastically from $100 million to an expected cost of just pennies by 2020. This will enable more personalized care based on a patient's genomic profile. Genomic analysis is already improving diagnosis and treatment for various diseases like rare genetic disorders and cancer. In the future, genomic data combined with sensor data will generate huge amounts of healthcare data and further advance personalized medicine.
The document analyzes the relationship between clinical computing systems used by family practices in the UK and their performance under the Quality and Outcomes Framework (QOF) pay-for-performance scheme between 2007-2011. Statistical models found that practices' choice of clinical computing system was a significant predictor of their QOF achievement scores, with some systems associated with better performance than others. Practices using the Vision 3 or Synergy systems tended to score highest overall, while those using the PCS system tended to score the lowest. Performance varied by the type of clinical activities as well.
Innovative Strategies For Successful Trial Design - Webinar SlidesnQuery
Full webinar available here: https://www.statsols.com/webinar/innovative-strategies-for-successful-trial-design
[Webinar] Innovative Strategies For Successful Trial Design- In this free webinar, you will learn about:
- The challenges facing your trials
- How to calculate the correct sample size
- Worked examples including Mixed/Hierarchical Models
- Posterior Error
- Adaptive Designs For Survival
www.statsols.com
This document describes the development of a Computerized Physician Order Entry (CPOE) system with a Decision Support System (DSS) and an assessment of physician attitudes towards the system. Key points:
- Researchers developed a comprehensive drug database for the CPOE/DSS using a commercial drug information resource and Microsoft SQL Server.
- They administered a questionnaire to 25 physicians at a 1600-bed teaching hospital to assess preconceived attitudes towards CPOE/DSS and measure end-user satisfaction.
- Results showed most physicians agreed the system could improve patient safety, reduce medication errors, and were easy to use, though some had doubts about data reliability and completeness. Overall, 88% agreed
A webinar hosted by CHIME. It shared thoughts on one of my areas of interest – harnessing both business intelligence and health IT, for more effective measurement of healthcare performance.
Demystifying Text Analytics and NLP in HealthcareHealth Catalyst
Leading the discussion, we have two exceptional thinkers in this space, Mike Dow, a former CIO and current Health Catalyst product manager and software developer, and Dr. Carolyn Simpkins, Health Catalyst’s Chief Medical Informatics Officer.
They will share thoughts on the challenges of text in clinical analytics as well as demonstrate:
Why text is an important part of clinical analytics
Why a text search is not enough
How clinical text search can be refined with NLP techniques
Healthcare Interoperability: New Tactics and TechnologyHealth Catalyst
Every provider agrees on the need for healthcare interoperability to achieve clinical data insights at the point of care. The question is how to get there from the myriad technologies and the volumes of data that comprise electronic medical records. It’s been difficult to organize among participants that have had little incentive to cooperate. And standards for sending and receiving data have been slow to develop. This is changing, but the key components that are still vital to realizing insights are closed-loop analytics and its accompanying tools, an enterprise data warehouse and analytics applications. This article defines the problems and explores the solutions to optimizing clinical decision making where it’s needed most.
What Veterinarians Can Learn From Physician Practice Modelsmjmcgaunn
Veterinarians can learn from physician practice models that aim to gain market share through innovation and niche services. Concierge medicine offers patients enhanced services for an annual retainer fee averaging $10,000. Compensation for veterinarians should balance incentives for individual and team performance with base salaries that increase with experience and responsibilities. Electronic medical records can reduce medical errors and some hospitals have seen a 7.2% lower mortality rate when using health IT.
Drug Utilization in a regulated EnviormentAlok Anand
Tracking drugs across the supply chain in a regulated environment. This white paper brief on would be drug utilization approach of Life Science Industry. This white paper is just a step forward to show future life science industry process automation
This document discusses using chi-square tests to analyze categorical healthcare data and determine if there are significant differences between groups. Chi-square tests can be used to analyze factors like physician prescribing patterns, order set adherence, and their effects on appropriate treatment. Quality improvement implications may include targeting education or interventions at physicians or factors found to have non-equivalent outcomes. The document provides examples of chi-square test questions, assumptions, and interpreting results to identify opportunities to improve care quality.
The document discusses hypothesis testing and its use in quality improvement to determine if potential factors significantly affect process performance. Hypothesis testing involves determining if a null hypothesis (that outcomes are produced by similar processes) or alternative hypothesis (that outcomes are produced by dissimilar processes) is correct. A statistically significant result, where the p-value is below a threshold like 0.05, means the null hypothesis can be rejected, indicating the factor affects performance and is a target for process improvement. The example compares weekday and weekend antibiotic timing for pneumonia patients, finding a significant difference, suggesting the administration process differs between weekdays and weekends.
Quantitative research involves examining hypotheses and numbers in a systematic way. It includes defining the research problem, organizing hypotheses to answer the problem, designing the research with options like experimental, quasi-experimental or ex-post facto, developing instruments to collect data, analyzing the data, and drawing conclusions based on probability. The quantitative research process aims to objectively test hypotheses and relationships between variables through collecting and analyzing numerical data.
This document provides an overview of quantitative data analysis techniques for hypothesis testing, including types of errors, statistical power, and tests for single and multiple sample means. It also discusses regression analysis, issues of multicollinearity, and other multivariate tests such as discriminant analysis, logistic regression, and canonical correlation.
The document discusses research hypotheses and defining variables. It explains that a research hypothesis is an educated guess that can be tested and measured. There are two types of hypotheses: the null hypothesis, which denies a relationship, and the alternative hypothesis, which affirms a relationship. Good hypotheses are testable, logical, related to the problem, and represent a single concept. Research variables are qualities that can change or vary, and must be measurable. Variables can be independent or dependent, quantitative or qualitative, continuous or categorical. The definitions of variables should include both a conceptual definition and an operational definition for the study.
This document discusses research hypotheses. It defines a hypothesis as a tentative, testable statement about the relationship between two or more variables. A hypothesis helps translate research problems into clear predictions about expected outcomes. Hypotheses are derived from literature reviews and conceptual frameworks. The main types discussed are research hypotheses, null hypotheses, and testable hypotheses. Research hypotheses make predictions, while null hypotheses predict no relationship. Testable hypotheses involve measurable variables. Variables are also discussed, including independent, dependent, extraneous, and demographic variables. Assumptions and limitations of research are briefly covered.
The document discusses methodology sections in research papers. It provides examples of methodology sections and discusses what they should include. It lists things like when and where the research was conducted, the data collection procedures, criteria for including subjects, a description of surveys used to collect data, and how results will be presented. It also includes multiple links to methodology sections from published research papers that could be used as examples.
Physician performance improvement part oneRobert Sutter
This white paper discusses using analytics to improve physician performance. It addresses quantifying the proportion of performance variability attributable to physicians and determining if statistically significant differences exist between physicians. The paper provides an example showing physician practice patterns account for over 90% of variability in length of stay for back procedures, suggesting focusing improvement there. It also gives an example where physician performance for vascular procedures is statistically significantly different, indicating reducing variability could meaningfully improve outcomes. The overall goal is to use data-driven analytics to inform effective physician performance improvement strategies.
This document summarizes a seminar on health technology assessment and economic evaluation. It discusses key concepts like incremental cost-effectiveness ratio (ICER) and quality-adjusted life years (QALYs). It also summarizes a past study on the cost-effectiveness of treating hypertension at worksites versus regular care. The study found that the worksite program had an ICER of $5.63/mmHg reduction in blood pressure, which was more cost-effective than the regular care ICER of $32.51/mmHg. Sensitivity analysis supported these findings.
White Paper_Achieving Sustainable Medicare Breakeven Performance_Care Logisti...Ben Sawyer
This document provides an overview of a hospital leadership guidebook for operational efficiency. It discusses the challenges hospitals face in adapting to a value-based healthcare system and the need to improve both quality and efficiency. It introduces two key performance measures - the CMI-Adjusted Combined LOS Ratio and Observed to Expected Ratio - that can help hospitals identify opportunities to streamline operations and eliminate waste. The document then presents a sample hospital's data using these measures and outlines a model for hospitals to implement a logistical control system to achieve sustainable performance at Medicare reimbursement levels through reliable, predictable care processes and continuous performance monitoring.
This document provides an overview of electronic clinical quality measures (eCQMs) and the transition from manual chart abstraction to electronic reporting of quality measures. It discusses upcoming requirements for eCQM reporting to CMS programs like IQR and the vision for a unified set of electronically specified measures. The document reviews the eCQM reporting process including planning, testing, validation and submission. Challenges and opportunities of eCQM reporting are also addressed.
Keynote Presentation "Meaningful Use Stage 2 and Meaningful Use Audit Insight"
Think far beyond just threshold increases. The differences between Meaningful Use (MU) Stage 1 and Stage 2, including the 2014 Clinical Quality Measures, are technically and clinically challenging. And just when you thought you could safely look at Stage 1 in the rearview mirror, here come the audits! I will highlight the Stage 1 and Stage 2 differences and talk about the challenges they have initiated at Tenet. I will touch on the impact of Quality measures and will also provide you with insight into the basics of MU Audits and will take you through the actual audit experience at Tenet.
Learning Objectives:
∙ Review the program and measure changes from Stage 1 to Stage 2 and how the changes are being managed at Tenet
∙ Provide insight into the 2014 Clinical Quality Measures chosen by Tenet, the challenges posed, solutions that work and a little about the overall
impact of Quality measures
∙ Discuss Meaningful Use Audits, covering the basics as well as providing the benefit of the Tenet experience
This document summarizes a proposed Patient Blood Management (PBM) program at St. Elsewhere Hospital. It identifies issues with current transfusion practices and policies across three pilot areas: post-partum haemorrhage, pre-operative optimization of haemoglobin levels, and deployment of intraoperative cell salvage. The program proposes applying ISO 9001 quality management standards to establish a PBM quality system and governance structure to standardize practices, reduce unnecessary transfusions, and improve outcomes. Key elements include developing a PBM committee, validating all clinical policies, establishing responsibilities and trainings, and deploying alternatives to transfusion like intraoperative cell salvage. The flexibility of ISO 9001 makes it suitable to implement across complex healthcare organizations
Annual Results and Impact Evaluation Workshop for RBF - Day One - Using Oper...RBFHealth
This document discusses using operational and health management information system (HMIS) data for monitoring programs and evaluating impact in Zambia. It provides the following key points:
1) Zambia uses a results-based financing model where health centers are paid for achieving targets on maternal and child health indicators. Operational and HMIS data are used for verification and payments.
2) Trend data from 2012-2013 show increases in several incentivized indicators like skilled deliveries and antenatal visits based on operational data.
3) While operational and HMIS data come from the same sources, operational data is verified monthly whereas HMIS is self-reported and occasionally verified, so trends must be consistent.
Through a mixed methods analysis of issue-tracking data from a project to implement nine heart failure clinical quality measures, the researchers identified seven categories of work involved in building and validating automated EHR-based quality measures. They found the process to be non-linear, with iteration between developing the measures, debugging systems, and exploring data. Data quality issues from the EHR led to complex measure logic. Accurately attributing patients and results to providers also required significant effort.
IRJET- Disease Prediction and Doctor Recommendation SystemIRJET Journal
This document proposes a disease prediction and doctor recommendation system that uses machine learning and natural language processing. It uses a Naive Bayes classifier to predict the likelihood of diseases based on patient symptoms and medical data. It then recommends doctors for the predicted disease by analyzing reviews with CoreNLP and filtering by location, cost, experience or reviews. The system aims to help users receive accurate diagnoses and treatment recommendations efficiently. Future work could involve expanding the types of diseases and doctors covered as well as collecting more real-world patient and review data to improve accuracy.
The document discusses using data and analytics to drive improvements in healthcare. It outlines the components of a data-driven organization, including an enterprise data warehouse, metrics, predictive models, protocols, and governance. It also discusses how analytics can help healthcare providers transition to value-based payments by measuring quality, reducing variation, and eliminating waste. Specific examples are provided on how one healthcare system used data to reduce variation in spine care, lower bleeding complications after PCI procedures, identify drug cost opportunities in knee replacements, and lower supply costs for lumbar fusion procedures.
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Three Steps to Prioritize Clinical Quality Improvement in HealthcareHealth Catalyst
Healthcare organizations today have access to so much data from across their systems that they may struggle to know where to focus quality improvement efforts. An analytic framework and a stepwise process ensures organizations have broad data access and can identify the most significant opportunities for impact. With a strategic, data-informed approach to clinical quality improvement, health systems can consume fewer resources, discover cost savings, and improve ROI and the quality of care.
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2. Harnessing Data For Performance Improvement
The following slides depict statistical analyses I conducted on patient level data and
performance dashboards I developed that revealed performance improvement
opportunities and catalyzed performance improvement projects.
Robert Sutter, RN MBA MHA
3. Quality Performance Dashboard
Developed this quality performance
dashboard for a health system to assess
and monitor the quality of care provided,
as well as guide annual quality
improvement planning.
The dashboard has several unique
features:
Each category is comprised of sub-
categories and associated metrics.
Category and sub-category
performance is summarized by robust
composite indicators.
Every metric is compared to an external
benchmark.
The dashboard provides relevant
information to all levels of the organization
from the Board of Directors to middle
managers and medical staff.
Dissemination of this information initiated
the development of annual quality
improvement planning and project reviews
throughout the health system and
stimulated the incorporation of quality
improvement into the strategic planning
process.
3 Robert Sutter, RN MBA MHA
4. Quality Performance Dashboard
This figure depicts the additional
information within each sub-category of
the Quality Performance Dashboard.
On the prior slide, hospital H had a one
star – less than the benchmark –
performance in Core Measures.
Additional information available reveals
that Pneumonia has a less than the
benchmark performance and the
following metrics are less than the
benchmark:
Pneumococcal screening
Smoking cessation advice
Antibiotic selection
Antibiotic within 6 hours
Influenza vaccination
Subsequently hospital H launched
performance improvement projects to
close the performance gap.
4 Robert Sutter, RN MBA MHA
5. Cardiothoracic Performance Dashboard
Harnessing the data collected for the Society of Thoracic Surgeons Adult Cardiac Database, this dashboard is updated
monthly in order to provide feedback to the hospitals more frequently than the quarterly report from STS.
The comparative nature of the dashboard catalyzed benchmarking and initiated performance improvement projects
throughout the health system. The data was also used in several data analysis projects to answer questions posed by the
cardiothoracic surgeons (see slides 9-12).
5 Robert Sutter, RN MBA MHA
6. Physician Performance Measurement
A physician performance measurement
system was developed to answer three
questions:
What proportion of variability is
attributable to physicians?
Is there a statistically significant
difference in physician performance?
Is there a distribution in outcome
categories among physicians?
The answers to these questions provide
the necessary information to develop
an effective physician performance
improvement strategy.
This analysis has notably enhanced
physician engagement.
6 Robert Sutter, RN MBA MHA
3.9
37.2
99.2
x
DIABETES
HERNIORRHAPHY
CHEST PAIN
Physician Variability Percent
2.09
1.26
-0.07
-0.13
-0.34
-0.38
-0.43
-0.53
-0.54
-0.58
Median
10
1
6
3
8
5
2
4
9
7
Risk-Adjusted LOS Excess
P<0.05
Attending Physician
Chest Pain
43
12
7
40
56
59
46
11
2
19
AttendingPhysician
-2 -1 0 1 2 3 4 5 6
Risk-Adjusted Median Excess LOS Confidence Interval
Better Than Expected
As Expected
Worse Than Expected
Length of Stay Outcome Categories
Confidence Level = 0.95
Attending Physician
Chest Pain
7. SCIP Core Measures Data Analysis & Improvement
A multilevel logistic regression analysis
of the SCIP core measures patient level
data, comprising all hospitals, revealed
the following factors significantly
associated with administering an
antibiotic within one hour prior to
incision:
Surgical Procedure
Surgical Day of Week
Shift
This analysis catalyzed a system-wide
performance improvement project that
resulted in significant improvement.
.94 .93 .9 .91 .86
.95 .91
0
.2.4.6.8
1
Porportion
C
ABGO
therC
ardiac
H
ip
Knee
C
olonH
ysterectom
y
Vascular
P=0.0445
Surgical Procedure
Antibiotic Within 1 Hr Prior to Incision
.83
.93 .93 .94 .94 .92 .96
0
.2.4.6.8
1
Porportion
Sun Mon Tue Wed Thu Fri Sat
P=0.0222
Surgery Day of Week
Antibiotic Within 1 Hr Prior to Incision
.9 .94
0
.2.4.6.8
1
Porportion
Evening Day
P=0.0186
Shift
Antibiotic Within 1 Hr Prior to Incision
.93 .96
0
.2.4.6.8
1
Proportion
Baseline Improvement
P<0.000
System Performance
Antibiotic Within 1 Hour Prior to Incision
7 Robert Sutter, RN MBA MHA
8. SCIP Core Measures Data Analysis & Improvement
A multilevel logistic regression analysis
of the SCIP core measures patient level
data revealed that timely antibiotic
discontinuation is significantly
associated with patient’s acquiring an
infection.
Further analysis revealed the following
factors significantly associated with
timely discontinuation of antibiotics
post-operatively:
Hospitals
Surgical Procedure
These analyses catalyzed a system-
wide performance improvement project
that resulted in statistically significant
improvement.
.013
.002
0
.005
.01
.015
InfectionRate
No Yes
P=0.037
Infection
Timely Antibiotic Discontinuation
.86 .85 .9
.99
.91
1
.93
.79
1
0
.2.4.6.8
1
Proportion
1 2 3 4 5 6 7 8 9
P<0.000
Hospital Comparison
Timely Antibiotic Discontinuation
.95 .88 .87 .91
.67
.96
.78
0
.2.4.6.8
1
Proportion
C
ABGO
therC
ardiac
H
ip
Knee
C
olon
H
ysterectom
y
Vascular
P<0.0000
Surgical Procedure
Timely Antibiotic Discontinuation
.91 .93
0
.2.4.6.8
1
Proportion
Baseline Improvement
P=0.003
System Performance
Timely Antibiotic Discontinuation
8 Robert Sutter, RN MBA MHA
9. SCIP Core Measures Data Analysis & Improvement
Using the SCIP Core Measures patient
level data, statistically significant
differences in the proportion of cardiac
surgery patients with appropriate post-
operative glucose control among
hospitals was revealed.
This resulted in launching a system-
wide performance improvement project
that yielded a significant system-wide
improvement.
9
.48
.87
.95
.8
.94 .94
.91
.73
.81
0
.2.4.6.8
1
Proportion
1 2 3 4 5 6 7 8 9
P<0.000
Hospital Comparison
Cardiac Surgery Glucose Control
.82
.94
0
.2.4.6.8
1
Proportion
Baseline Improvement
P<0.000
System Performance
Cardiac Surgery Glucose Control
Robert Sutter, RN MBA MHA
10. Society of Thoracic Surgeons Data Analysis
The following analyses of the STS
patient level data catalyzed numerous
performance improvement projects
throughout the hospitals that are
currently underway.
In addition, a monthly STS report was
developed and disseminated via
SharePoint to provide hospitals with
more frequent and timely information
to assist in their improvement projects.
A propensity score analysis revealed
that pre-operative beta-blocker use in
isolated CABG patients was significantly
associated with a lower mortality rate.
Further analysis exposed significant
differences among hospitals in pre-
operative beta-blocker use as well as
composite medication performance in
isolated CABG patients.
10
.029
.013
0
.01.02.03
MortalityRate
No Yes
Odds Ratio 0.360: P<0.000
Pre-Operative Beta Blocker
Isolated CABG
.59
.66
.78
.7 .72
.57
0
.2.4.6.8
Proportion
1 2 3 4 5 6
P<0.000
Hospital Comparison
Isolated CABG Pre-OP Beta-Blocker
.39
.62
.68
.55
.49
.71
.45
0
.2.4.6.8
Proportion
1 2 3 4 5 6 7
P<0.000
Hospital Comparison
Isolated CABG Composite Medication
Robert Sutter, RN MBA MHA
11. Society of Thoracic Surgeons Data Analysis
A multilevel logistic regression analysis
uncovered highly significant
relationships between the occurrence of
isolated CABG post-operative
complications and mortality.
Numerous performance improvement
projects were launched to reduce the
incidence of post-operative
complications.
11
.053
.18
0
.05
.1
.15
.2
MortalityRate
No Yes
Odds Ratio 3.0: P=0.010
Post-Operative Stroke
Isolated CABG
.029
.27
0
.1.2.3
MortalityRate
No Yes
Odds Ratio 12.4: P<0.000
Renal Failure
Isolated CABG
.026
.23
0
.05
.1
.15
.2
.25
MortalityRate
No Yes
Odds Ratio 12.2: P<0.000
Prolonged Ventilation
Isolated CABG
.043
.2
0
.05
.1
.15
.2
MortalityRate
No Yes
Odds Ratio 5.4: P<0.000
Reoperation
Isolated CABG
.016
.17
0
.05
.1
.15
.2
MortalityRate
No Yes
Odds Ratio 13.0: P<0.000
Prolonged Post-Operative LOS
Isolated CABG
Robert Sutter, RN MBA MHA
12. Society of Thoracic Surgeons Data Analysis
A multilevel logistic regression analysis
uncovered highly significant
relationships between the occurrence of
isolated CABG post-operative
complications and prolonged post-
operative length of stay.
Numerous performance improvement
projects were launched to reduce the
incidence of post-operative
complications.
12
.064
.25
0
.05
.1
.15
.2
.25
ProlongedPost-OPLosRate
No Yes
Odds Ratio 4.9: P=0.001
Post-Operative Stroke
Isolated CABG
.05
.23
0
.05
.1
.15
.2
.25
ProlongedPost-OPLosRate
No Yes
Odds Ratio 5.7: P<0.000
Renal Failure
Isolated CABG
.032
.28
0
.1.2.3
ProlongedPost-OPLosRate
No Yes
Odds Ratio 13.7: P<0.000
Prolonged Ventilation
Isolated CABG
.062
.16
0
.05
.1
.15
.2
ProlongedPost-OPLosRate
No Yes
Odds Ratio 3.0: P<0.005
Reoperation
Isolated CABG
Robert Sutter, RN MBA MHA
13. Society of Thoracic Surgeons Data Analysis
Surgeon specific risk-adjusted mortality
and reoperation performance was
derived for hospitals to facilitate
focusing improvement efforts.
13
3.5
2.8
0
5.5
1.7
9.8
0
02468
10
Observed/ExpectedMortaliltyRatio
1 2 4 6 7 8 9
Surgeon
Isolated CABG Observed/Expected Mortality
.9
1.1
0
2.1
1
1.9
0
0
.5
1
1.5
2
Observed/ExpectedReoperationRatio
1 2 4 6 7 8 9
Surgeon
Isolated CABG Observed/Expected Reoperation
Robert Sutter, RN MBA MHA
14. American College of Cardiology Data Analysis
The American College of Cardiology
patient level data was analyzed to
determine if there were significant
differences in hospital utilization of
contraindicated antithrombotics in
dialysis patients undergoing PCI.
The results revealed highly significant
differences in hospital utilization of
contraindicated antithrombotics.
This information was presented to the
medical staff at each hospital and
subsequent changes in practice
patterns were initiated.
14
.25
.29
.06
.43
.29
.087
0
.1.2.3.4
Proportion
1 2 3 4 5 6
P<0.000
Hospital Comparison
PCI Dialysis Contraindicated Antithrombotics
.88
.38
.25
.85
1
.33
.67
.42
.57
.43
0
1
0
.2.4.6.8
1
Proportion
1 2 3 4 5 6
Hospital Comparison
PCI Dialysis Contraindicated Antithrombotics
mean of enoxaparin
mean of eptifibatide
Robert Sutter, RN MBA MHA
15. American College of Cardiology Data Analysis
The American College of Cardiology
patient level data was analyzed to
determine if there were significant
differences in the incidence of vascular
complications among hospitals.
The results revealed highly significant
differences.
This stimulated benchmarking and
process improvement at various
hospitals.
15
.0041
.012 .014
.041
.011
.02
0
.01.02.03.04
Proportion
1 2 3 4 5 6
P<0.000
Hospital Comparison
Cardiac Catheterization Vascular Complications
.0084
.037
.015
0
.022
.048
0
.01.02.03.04.05
Proportion
1 2 3 4 5 6
P=0.014
Hospital Comparison
Percutaneous Coronary Intervention Vascular Complications
.0012 0
.012
.053
.0078
.0039
0
.01.02.03.04.05
Proportion
1 2 3 4 5 6
P<0.000
Hospital Comparison
Diagnostic Catheterization Vascular Complications
Robert Sutter, RN MBA MHA
16. American College of Cardiology Data Analysis
Based on the previous analysis one of
the hospitals wanted to answer the
following questions regarding diagnostic
catheterization:
Is there a significant difference
among physicians?
Are certain patient characteristics
associated with vascular
complications?
The results revealed highly significant
differences among physicians.
Multilevel logistic regression analysis
indicated that patient characteristics are
not significantly associated with
vascular complications.
This information stimulated evaluating
physician practice patterns.
16
.8
0 0 0
.034
.067
0
0
.2.4.6.8
Proportion
1 2 3 4 5 6 9
P<0.000
Physician Comparison
Diagnostic Catheterization Vascular Complications
Variable P Value
Gender 0.265
Hypertension 0.508
Prior MI 0.273
Prior Heart Failure 0.494
Diabetes 0.867
Dyslipidemia 0.636
Peripheral Arterial Disease 0.337
Prior PCI 0.372
Age_spline1 0.444
Age_spline2 0.673
Robert Sutter, RN MBA MHA