Soraya Ghebleh - Unwarranted Variation in HealthcareSoraya Ghebleh
This is a short paper by Soraya Ghebleh that discusses the causes of unwarranted variation in healthcare delivery and potential strategies to reduce these unwarranted variations.
In first of two-part series, Pamela Greenhouse explores the differences and similarities of the Patient and Family Centered Care Methodology and Practice (PFCC M/P) and leean process improvement approachs, such as Lean, Six Sigma and Toyota. She believes that the PFCC M/P can be the unifying theme for health care, incorporating both process improvement and performance improvement.
How can hospitalist programs manage the ongoing shift to value-based care, along with operating costs and the challenges of managing, recruiting and retaining high-quality physicians? Read the report to find out.
Soraya Ghebleh - Unwarranted Variation in HealthcareSoraya Ghebleh
This is a short paper by Soraya Ghebleh that discusses the causes of unwarranted variation in healthcare delivery and potential strategies to reduce these unwarranted variations.
In first of two-part series, Pamela Greenhouse explores the differences and similarities of the Patient and Family Centered Care Methodology and Practice (PFCC M/P) and leean process improvement approachs, such as Lean, Six Sigma and Toyota. She believes that the PFCC M/P can be the unifying theme for health care, incorporating both process improvement and performance improvement.
How can hospitalist programs manage the ongoing shift to value-based care, along with operating costs and the challenges of managing, recruiting and retaining high-quality physicians? Read the report to find out.
Patient Blood Management: Impact of Quality Data on Patient OutcomesViewics
Patient blood management (PBM) has been proven to improve patient outcomes and save hospitals millions of dollars. Ensuring the quality of your data is central to decision making and critical to having a strong PBM program.
Would you like to learn how your organization can improve patient outcomes by implementing a PBM program based on accurate data?
If so, view this presentation by blood management expert Lance Trewhella. Lance presents how to develop a successful, evidence-based, multidisciplinary PBM program aimed at optimizing the care of patients who might need transfusion.
You’ll learn:
• Current recommendations for blood transfusion utilization
• The impact of quality data on PBM programs
• Best data practices in PBM
Advanced Laboratory Analytics — A Disruptive Solution for Health SystemsViewics
As US healthcare systems grapple with the recent upheavals in care payment and delivery, they are turning to advanced analytics as their “central nervous systems” for driving care and financial performance.
Laboratory information — spanning chemistry, pathology, microbiology and molecular testing, for example — is among the best sources of data for these advanced analytics, including clinician decision support, predictive analytics, population health management, and personalized medicine. When strategically harnessed and integrated to create a patient-centric lab data lake, laboratory information can form an affordable yet competitively powerful advanced analytics solution well suited for many health systems — i.e., a disruptive option.
L. Eleanor J. Herriman, MD, MBA, Chief Medical Informatics Officer of Viewics, explains why laboratory data should be a core strategic component for achieving success in value-based healthcare.
Advanced Lab Analytics for Patient Blood Management ProgramsViewics
Reports indicate that 30 – 70% of blood transfusions are inappropriate. Inappropriate blood transfusions put patients at increased risk of post-surgical infections, multi-system organ failure, longer hospital stays, and higher mortality rates. The transfusion guidelines most clinicians learned in their training are now outdated. As such, blood transfusion practices vary widely, and overutilization remains a major quality and cost problem.
Patient Blood Management (PBM) programs are designed to optimize the use of transfusions through a team-based approach, evidence-based guidelines, and algorithms that together guide decisions regarding specifically which patients and clinical procedures warrant blood products, and how much to transfuse. PBM programs have been quite successful in improving patient morbidity and mortality outcomes and generating millions of dollars in savings for hospitals.
Laboratory analytics can be an effective means of instituting restrictive transfusion programs, and advanced lab analytics can be critical in implementing PBM programs, as lab testing and tracking blood usage is central to decision making, changing behavior, and improving performance.
Watch a presentation by Dr. Eleanor Herriman, Chief Medical Informatics Officer at Viewics. She unveils a new suite of advanced analytics tools that support PBS and other restrictive blood management programs, enabling health systems to better leverage their valuable lab medicine assets and fully integrate this key service line into these programs.
You’ll learn:
• How inappropriate blood transfusions are burdening our healthcare system, and the need for better utilization management tools
• New guidelines restricting red blood cell transfusions
• The role of advanced lab analytics in PBM programs
• How Viewics is leveraging advanced lab analytics to help health systems more easily and cost-effectively implement PBM programs
Tackling the U.S. Healthcare System’s Infectious Disease Management ProblemViewics
The United States healthcare system has a serious infectious disease management problem. The antibiotic resistance crisis is widespread, serious, costly, and deadly. Delays in pathogen identification lead to poor clinical outcomes, including increased mortality risk. And, optimally managing outbreaks is critical to health systems whose reimbursement is tied to the health of a population, such as ACOs.
Eleanor Herriman, MD, MBA, Chief Medical Informatics Officer at Viewics led an informative panel discussion with industry leaders on the issues surrounding the infectious disease management crisis. Margret Oethinger, MD, Ph.D., Medical Director of Providence Health & Services, and Susan E. Sharp, Ph.D., DABMM, FAAM, Regional Director of Microbiology and the Molecular Infectious Disease Laboratories, Department of Pathology, Kaiser Permanente and President-Elect, American Society for Microbiology cover the current state of infectious disease management in the U.S., and what can be done to improve it.
You’ll learn about:
• The magnitude of the U.S. health system’s infectious disease management problem
• The most serious concerns and trends for healthcare institutions and communities across the nation
• The most promising solutions to health systems’ most urgent infectious disease management challenges
A Dartmouth Microsystem Assessment was conducted to examine a hospital unit\\’s functionality and to highlight opportunities for improvement. To enhance the gathering of data, a statistical tool was created to measure a wider sample population. The CNL student implemented a more reliable and valid data gathering system. The nurse educator asked to use the graduate student’s tool on the unit and throughout the hospital.
Presented at the 2015 IHI International Forum byThe Royal Melbourne Hospital of Victoria,Australia, this poster,speaks to the power of Shadowing to engage patients and families in decisions of care, specifically the post-discharge planning process.
Predicting Patient Adherence: Why and HowCognizant
To contain costs and improve healthcare outcomes, players across the value chain must apply advanced analytics to measure and understand patients’ failure to follow treatment therapies, and to then determine effective remedial action. This white paper lays out a framework for enabling patient adherence management and some general prescriptions on how to convert lofty concepts to meaningful action.
EDM ForumEDM Forum CommunityeGEMs (Generating Evidence & M.docxgreg1eden90113
EDM Forum
EDM Forum Community
eGEMs (Generating Evidence & Methods to
improve patient outcomes) Publish
4-20-2017
Reducing Healthcare Costs Through Patient
Targeting: Risk Adjustment Modeling to Predict
Patients Remaining High-Cost
Jonathan A. Wrathall
Intermountain Healthcare, [email protected]
Tom Belnap
Intermountain Healthcare, [email protected]
Follow this and additional works at: http://repository.edm-forum.org/egems
Part of the Other Medicine and Health Sciences Commons, and the Social Statistics Commons
This Methods Case Study is brought to you for free and open access by the the Publish at EDM Forum Community. It has been peer-reviewed and
accepted for publication in eGEMs (Generating Evidence & Methods to improve patient outcomes).
The Electronic Data Methods (EDM) Forum is supported by the Agency for Healthcare Research and Quality (AHRQ), Grant 1U18HS022789-01.
eGEMs publications do not reflect the official views of AHRQ or the United States Department of Health and Human Services.
Recommended Citation
Wrathall, Jonathan A. and Belnap, Tom (2017) "Reducing Healthcare Costs Through Patient Targeting: Risk Adjustment Modeling to
Predict Patients Remaining High-Cost," eGEMs (Generating Evidence & Methods to improve patient outcomes): Vol. 5: Iss. 2, Article 4.
DOI: https://doi.org/10.13063/2327-9214.1279
Available at: http://repository.edm-forum.org/egems/vol5/iss2/4
Reducing Healthcare Costs Through Patient Targeting: Risk Adjustment
Modeling to Predict Patients Remaining High-Cost
Abstract
Context: The transition to population health management has changed the healthcare landscape to identify
high risk, high cost patients. Various measures of patient risk have attempted to identify likely candidates for
care management programs. Pre-screening patients for outreach has often required several years of data.
Intermountain Healthcare relied on cost-ranking algorithms which had limited predictive ability. A new risk-
adjusted algorithm shows improvements in predicting patients’ future cost status to facilitate identifying
patient eligibility for care management.
Case Description: A retrospective cohort study design was used to evaluate high-cost patient status for two
of the next three years. Modeling was developed using logistic regression and tested against other decision tree
methods. Key variables included those readily available in electronic health records supplemented by
additional clinical data and estimates of socio-economic status.
Findings: The risk-adjusted modeling correctly identified 79.0% of patients ranking among the top 15% of
costs in one of the next three years. In addition, it correctly estimated 48.1% of the patients in the top 15% cost
group in two of the next three years. This method identified patients with higher medical costs and more
comorbid conditions than previous cost-ranking methods.
Major Themes: This approach improves the predictive accuracy of identifying high cost patients in the future
.
Patient Blood Management: Impact of Quality Data on Patient OutcomesViewics
Patient blood management (PBM) has been proven to improve patient outcomes and save hospitals millions of dollars. Ensuring the quality of your data is central to decision making and critical to having a strong PBM program.
Would you like to learn how your organization can improve patient outcomes by implementing a PBM program based on accurate data?
If so, view this presentation by blood management expert Lance Trewhella. Lance presents how to develop a successful, evidence-based, multidisciplinary PBM program aimed at optimizing the care of patients who might need transfusion.
You’ll learn:
• Current recommendations for blood transfusion utilization
• The impact of quality data on PBM programs
• Best data practices in PBM
Advanced Laboratory Analytics — A Disruptive Solution for Health SystemsViewics
As US healthcare systems grapple with the recent upheavals in care payment and delivery, they are turning to advanced analytics as their “central nervous systems” for driving care and financial performance.
Laboratory information — spanning chemistry, pathology, microbiology and molecular testing, for example — is among the best sources of data for these advanced analytics, including clinician decision support, predictive analytics, population health management, and personalized medicine. When strategically harnessed and integrated to create a patient-centric lab data lake, laboratory information can form an affordable yet competitively powerful advanced analytics solution well suited for many health systems — i.e., a disruptive option.
L. Eleanor J. Herriman, MD, MBA, Chief Medical Informatics Officer of Viewics, explains why laboratory data should be a core strategic component for achieving success in value-based healthcare.
Advanced Lab Analytics for Patient Blood Management ProgramsViewics
Reports indicate that 30 – 70% of blood transfusions are inappropriate. Inappropriate blood transfusions put patients at increased risk of post-surgical infections, multi-system organ failure, longer hospital stays, and higher mortality rates. The transfusion guidelines most clinicians learned in their training are now outdated. As such, blood transfusion practices vary widely, and overutilization remains a major quality and cost problem.
Patient Blood Management (PBM) programs are designed to optimize the use of transfusions through a team-based approach, evidence-based guidelines, and algorithms that together guide decisions regarding specifically which patients and clinical procedures warrant blood products, and how much to transfuse. PBM programs have been quite successful in improving patient morbidity and mortality outcomes and generating millions of dollars in savings for hospitals.
Laboratory analytics can be an effective means of instituting restrictive transfusion programs, and advanced lab analytics can be critical in implementing PBM programs, as lab testing and tracking blood usage is central to decision making, changing behavior, and improving performance.
Watch a presentation by Dr. Eleanor Herriman, Chief Medical Informatics Officer at Viewics. She unveils a new suite of advanced analytics tools that support PBS and other restrictive blood management programs, enabling health systems to better leverage their valuable lab medicine assets and fully integrate this key service line into these programs.
You’ll learn:
• How inappropriate blood transfusions are burdening our healthcare system, and the need for better utilization management tools
• New guidelines restricting red blood cell transfusions
• The role of advanced lab analytics in PBM programs
• How Viewics is leveraging advanced lab analytics to help health systems more easily and cost-effectively implement PBM programs
Tackling the U.S. Healthcare System’s Infectious Disease Management ProblemViewics
The United States healthcare system has a serious infectious disease management problem. The antibiotic resistance crisis is widespread, serious, costly, and deadly. Delays in pathogen identification lead to poor clinical outcomes, including increased mortality risk. And, optimally managing outbreaks is critical to health systems whose reimbursement is tied to the health of a population, such as ACOs.
Eleanor Herriman, MD, MBA, Chief Medical Informatics Officer at Viewics led an informative panel discussion with industry leaders on the issues surrounding the infectious disease management crisis. Margret Oethinger, MD, Ph.D., Medical Director of Providence Health & Services, and Susan E. Sharp, Ph.D., DABMM, FAAM, Regional Director of Microbiology and the Molecular Infectious Disease Laboratories, Department of Pathology, Kaiser Permanente and President-Elect, American Society for Microbiology cover the current state of infectious disease management in the U.S., and what can be done to improve it.
You’ll learn about:
• The magnitude of the U.S. health system’s infectious disease management problem
• The most serious concerns and trends for healthcare institutions and communities across the nation
• The most promising solutions to health systems’ most urgent infectious disease management challenges
A Dartmouth Microsystem Assessment was conducted to examine a hospital unit\\’s functionality and to highlight opportunities for improvement. To enhance the gathering of data, a statistical tool was created to measure a wider sample population. The CNL student implemented a more reliable and valid data gathering system. The nurse educator asked to use the graduate student’s tool on the unit and throughout the hospital.
Presented at the 2015 IHI International Forum byThe Royal Melbourne Hospital of Victoria,Australia, this poster,speaks to the power of Shadowing to engage patients and families in decisions of care, specifically the post-discharge planning process.
Predicting Patient Adherence: Why and HowCognizant
To contain costs and improve healthcare outcomes, players across the value chain must apply advanced analytics to measure and understand patients’ failure to follow treatment therapies, and to then determine effective remedial action. This white paper lays out a framework for enabling patient adherence management and some general prescriptions on how to convert lofty concepts to meaningful action.
EDM ForumEDM Forum CommunityeGEMs (Generating Evidence & M.docxgreg1eden90113
EDM Forum
EDM Forum Community
eGEMs (Generating Evidence & Methods to
improve patient outcomes) Publish
4-20-2017
Reducing Healthcare Costs Through Patient
Targeting: Risk Adjustment Modeling to Predict
Patients Remaining High-Cost
Jonathan A. Wrathall
Intermountain Healthcare, [email protected]
Tom Belnap
Intermountain Healthcare, [email protected]
Follow this and additional works at: http://repository.edm-forum.org/egems
Part of the Other Medicine and Health Sciences Commons, and the Social Statistics Commons
This Methods Case Study is brought to you for free and open access by the the Publish at EDM Forum Community. It has been peer-reviewed and
accepted for publication in eGEMs (Generating Evidence & Methods to improve patient outcomes).
The Electronic Data Methods (EDM) Forum is supported by the Agency for Healthcare Research and Quality (AHRQ), Grant 1U18HS022789-01.
eGEMs publications do not reflect the official views of AHRQ or the United States Department of Health and Human Services.
Recommended Citation
Wrathall, Jonathan A. and Belnap, Tom (2017) "Reducing Healthcare Costs Through Patient Targeting: Risk Adjustment Modeling to
Predict Patients Remaining High-Cost," eGEMs (Generating Evidence & Methods to improve patient outcomes): Vol. 5: Iss. 2, Article 4.
DOI: https://doi.org/10.13063/2327-9214.1279
Available at: http://repository.edm-forum.org/egems/vol5/iss2/4
Reducing Healthcare Costs Through Patient Targeting: Risk Adjustment
Modeling to Predict Patients Remaining High-Cost
Abstract
Context: The transition to population health management has changed the healthcare landscape to identify
high risk, high cost patients. Various measures of patient risk have attempted to identify likely candidates for
care management programs. Pre-screening patients for outreach has often required several years of data.
Intermountain Healthcare relied on cost-ranking algorithms which had limited predictive ability. A new risk-
adjusted algorithm shows improvements in predicting patients’ future cost status to facilitate identifying
patient eligibility for care management.
Case Description: A retrospective cohort study design was used to evaluate high-cost patient status for two
of the next three years. Modeling was developed using logistic regression and tested against other decision tree
methods. Key variables included those readily available in electronic health records supplemented by
additional clinical data and estimates of socio-economic status.
Findings: The risk-adjusted modeling correctly identified 79.0% of patients ranking among the top 15% of
costs in one of the next three years. In addition, it correctly estimated 48.1% of the patients in the top 15% cost
group in two of the next three years. This method identified patients with higher medical costs and more
comorbid conditions than previous cost-ranking methods.
Major Themes: This approach improves the predictive accuracy of identifying high cost patients in the future
.
Hospitals profitability can be increased by boosting patient satisfaction, reducing readmissions and understanding revenue cycle performance.
In this period of healthcare reform, numerous organizations continue to change their business practices so they can obtain more hospital profitability while also delivering quality care. Healthcare expenditures are expected to reach $4.4 trillion by 2022, and this high level of spending activity has hospitals currently under a lot of pressure to reduce costs.
A few months ago I wrote an article entitled Unplanned Readmissions: Are They Quality Measures or Utilization Measures? It explained the Hospital Readmissions Reduction Program (HRRP) that began in October 2012 as part of the Affordable Care Act (ACA). That article explained the program and its results over the past 5 years. However, more and more healthcare leaders and organizations are beginning to question whether HRRP is a valuable program or whether it is time to move on to something that focuses on quality of care and clinical outcomes, rather than cost savings. This article will address those issues. (In this article “readmissions” mean unplanned or preventable readmissions).
Great article on how to integrate machine learning and optimization technique.
One group of researchers was able to reduce heart failure readmissions by 35% by combining machine learning and decision science technique, see "Data-driven decisions for reducing readmissions for heart failure: general methodology and case study" (Bayati, et. al., 2014).
BENCHMARK 1
Evidence-Based Practice Project: PICOT Paper
Daysha Y. Polk
NUR 550
Grand Canyon University
June 1st, 2021
Evidence-Based Practice Project: PICOT Paper
Generally, a high level of patient satisfaction for the clients in the emergency department (ED) is vital, especially at this time when the healthcare system is shifting towards patient-centered care. Prakash (2010) notes that patient satisfaction levels significantly impact on medical malpractice claims, patient retention, and clinical outcomes. That is, it affects quality healthcare’s timely, efficient, and patient-centered delivery, making it both a proxy but a very effective key indicator for measuring the hospitals and doctors’ success. Consequently, supporting the improvements of patient satisfaction levels can positively affect several healthcare organizations’ components, such as preventive possible malpractice lawsuits, securing a positive local reputation, and enhancing patient retention rates. Thus, there is an increased need to develop strategies to improve ED patient’s satisfaction with the provided care services. Increasingly, the use of real-time location systems (RTLS) by hospitals to track patients, instead of relying on the traditional, manually-entered status updates, is increasingly being viewed as a better strategy to decrease the number or rate of Left Without Being Treated (LWBT) patients, and thus, improve ED patient’s satisfaction levels and hospital’s revenue collection (Boulos & Berry, 2012). Thus, the paper will explore whether the utilization of RTLS in the hospital’s ED, compared to manually-entered status updates to tract patients, help decrease the rate of LWBT and to raise revenue collection within 6 months, for ED patients with decreasing satisfaction levels with the provided healthcare services.
A wide array of factors is responsible for the decreased rate of satisfaction levels amongst ED patients. The current delays, long waits, leaving without being treated, decreased revenue collection from the ED unit, and reduced patient satisfaction scores have negatively portrayed the hospital's reputation to the public. As a result, the daily patient visits have continued to decrease as people attribute the facility to poor emergency care services delivery. All these complications result from the use of combined data resources and manual entry status updates when tracking patient records. This manual tracking cannot meet the demand for many patients and leads to overcrowding due to and reduced patient flow in the ED. Therefore, there is a need to install an automatic patient tracking system to increase the flow.
Patient satisfaction level, especially for hospital’s emergency department (ED) is increasingly becoming a key health quality indicator. Patient satisfaction regards the degree to which patients are happy with their healthcare (Heath, 2016). Patient satisfaction levels is a care quality measure and gives healthcare providers infor ...
Top seven healthcare outcome measures of healthJosephMtonga1
The seven healthcare outcome measures are meant to understand the quality of health systems and how they could be measured and how quality care could be provided to clients.
A study on patient satisfaction with special reference to government hospital...Tapasya123
In this study researchers analyse the satisfaction level of patients regard to facilities
available in government hospitals. A sample of 100 patients is taken from Pandit Brij
Sundar Shama Government General Hospital (GGH) at Bundi District in the state
of Rajasthan in India. Four dimensions of perceived quality were identified—Admission
Procedure, Diagnostic Services, Behaviour of the staff, Cleanliness. The developed
scale is used to evaluate perceived quality at a range of various types of facilities
for patients. Perceived quality at public facilities is only marginally favourable, leaving
much scope for improvement. Better staff and physician relations, interpersonal skills,
good diagnostic and cleanliness service can improve the level of satisfaction among
employees.
Keywords:
To lower health costs, physician networks and medical homes must employ a closed loop population management program that focus on patient SOH stratification, chronic disease management, care coordination and incentive management. This approach will enable them to consistently reduce ER and inpatient admissions, which are the greatest expenditures in health care today.
1. Title: Assessing Patient Readmission for Improving Quality of Healthcare
Name: Manali Shah
Preceptors: Dolores Van Pelt, Director of Organizational Effectiveness & Karen Hepworth,
Performance Improvement Specialist
Agency: Hunterdon Healthcare
Purpose: To analyze and evaluate patient knowledge about disease and discharge instruction
which can result in and affect hospital readmission
Significance: In 2014, the Center for Medicare and Medicaid Services (CMS) penalized around
2,610 hospitals for high readmission rates, which cost up to $500,000 per hospital (Reardon). In
order to lower the number of readmitted patients, the CMS established the Hospital Readmission
Reduction Program (HRRP), which requires the CMS to reduce payments it gives to a hospital
with surplus readmissions rate (Reardon). Under the HRRP, hospitals with high readmissions
rates have 3% of their financial aid withheld (Brown). A 3% penalty could affect a hospital's
entire profit margin thus providing strong incentive to reduce readmission rates. Therefore,
Hunterdon Healthcare is reevaluating the program and gathering data on factors that contribute
to patients’ readmission, which will hopefully decrease the readmission rate in the future.
Method/Approach: The evaluation will be based on 30-day readmission criteria to analyze
patient’s knowledge on the disease and discharge instructions. A 15 open-ended questions survey
mixed with yes/no responses was designed to ask patients about readmission. For example,
questions such as, “What is your understanding as to why you were readmitted,” and “Do you
feel any of the following contribute to readmission: transport, lack of support, etc,” were asked.
In order to assess the quality of healthcare where changes can be made, 57 patients were asked
questions to determine which factors were repeated among patients. The methodology was to
review 30-day readmission reports, create the questionnaire, interview patients, and compile data
to look for trends. Data was shared with the organizational effectiveness department on a weekly
basis to monitor the effectiveness of the questionnaire.
Outcomes: The data is compiled of patients who vary in ages from late 40s to early 90s, with the
exception of one 3-month old. The data findings show three potential patterns for patient
readmission: 1) 82% of patients are not able to obtain a follow-up appointment with a primary
care physician, 2) the majority of patients (57%) stated that transportation was the primary
reason for readmission, and 3) 70% of patients cannot state the cause. In order to see a possible
decline in the readmission rate, more education on diet and self-care should be provided to
patients and caregivers.
Evaluation: The implementation of changes to the discharge process may reduce readmission,
thus reducing hospital penalties. Hunterdon uses a software system that collects data on
readmissions. The readmissions team monitors this data monthly to see if interventions are
2. working. A drop in readmission rate will determine to what extent the project is successful and if
any changes need to be made.