1 Predictive Analytics and Risk Models to Prevent Sepsis, Patient Falls, and Readmissions
2 Today s Agenda: Provide an overview of Mount Sinai s end-to-end informatics and clinical data analytics approach for quality initiatives. Purpose Solutions Approach Delivery Within our end-to-end framework we will illustrate how we: Leverage risk models and best practice alerts in the electronic health record to identify patients at risk for events. Use phenotypic modeling and informatics-enabled registries to identify and select patient cohorts. Use data trusts and data aggregation for unit level and service area scorecards, to measure hospital performance for QI initiatives. Leverage data for systemized and measurable operational change.
3 Mount Sinai Health System, Who We Are 7 hospital campuses and the Icahn School of Medicine Some facts: 3,535 beds and 135 operating rooms 169,532 inpatient admissions 2,600,000 outpatient visits 489,508 emergency department visits 18,000 babies delivered a year 6,200 physicians, including general practitioners and specialists 2,000+ residents and fellows 36,000 employees
4 Mount Sinai Hospital Office For Excellence in Patent Care Advancing Quality, Safety and Service across the Mount Sinai Health System We intend to produce the safest care, the best outcomes, the highest satisfaction, and the best value of any health system or provider in the New York Metropolitan area Chief Medical Officer Big Data HIT Forum Clinical Data Analytics Team Ken McCardle Senior Director Data Analytics Quality Initiatives Allison Glasser Director Operations Patient Safety
5 Purpose Solutions Approach Delivery Provide an overview of Mount Sinai s end-to-end informatics and clinical data analytics approach for quality and safety initiatives. Purpose in our context is Quality Initiatives
6 Quality Improvement (QI) Model Dimensions add further definition to the model: RN & MD Dyads Service Area Units / Wards Disease Audience etc. External Reporting Monitor and Measure Identify Patient Cohorts Optimize Care Processes Improve Patient Outcomes Take Away: QI is built around the patient experience and patient satisfaction
7 Cohort Identification What patient population do I want to impact? Adult vs. Pediatrics Disease specific (e.g. transplant surgery, sepsis, diabetes, etc..) Financial class (e.g. Medicare, Medicaid, ACO, etc ) Who are our high risk patients? Morbidity and mortality Readmissions Surgical site infections Inpatient falls Take Away: Quality initiatives require a clear definition of who you want to impact Where are the patient s being treated? Emergency department Inpatient ICUs Outpatient (e.g. clinics, faculty practices, etc )
8 Optimize Care Processes Electronic Healthcare Records (EHR) Patient Flags Care Guidelines Clinical Decision Support Risk Models Predictive Analytics Take Away: An EHR is a tool to support clinical processes and does not replace clinical judgment
9 Improve Patient Outcomes Decrease morbidity: e.g. falls, hospital acquired infections, unexpected ICU admissions, etc Decrease mortality: In-hospital, 30-day, raw vs. risk adjusted Improve patient satisfaction: Communications, discharge information, hospital quietness, staff responsiveness, etc Take Away: QI involves making, systemized operational changes that can be measured along with impact to outcomes Decrease readmissions: 7-day, 14-day, 30-day Decrease avoidable admissions
10 Monitor & Measure Monitoring Quality and Outcomes Assessment Using Clinical Data: More timely than hospital billing data (billing data available ~15-30 days after discharge) Used for predictive analytics, risk adjustment, decision support systems, and clinical registries Useful and more usable to clinicians for monitoring care protocols and adherence to guidelines Most often used for evidence-based care, translational research Take Away: Monitoring Measuring Measuring Using Administrative / Billing Data: Used for reimbursement policies and pay-for-performance metrics and is useful for measuring hospitals Large, national datasets made available for health service research
11 External Reporting Who is measuring our performance? CMS / Core Measures Department of Health NQF US News and World Report Registries & Data Consortiums (e.g. Premier, UHC, etc ) What are they measuring? Which patient cohort and how are they defining the cohort? Which outcomes? What processes? What data do I need to report? Administrative vs. clinical data Data dictionary requirements Take Away: External reporting requirements do not typically align with QI initiatives
12 QI Data Challenges Alignment of standard definitions across stakeholders Reconciliation of clinical data and hospital billing/administrative data Data quality assurance, mapping, nomenclatures, data standards, governance EHR Data discrete data elements vs. free text Getting the right data in the right format at the right time in the right hands
13 Purpose Solutions Approach Delivery Provide an overview of Mount Sinai s end-to-end informatics and clinical data analytics approach for quality and safety initiatives. Delivery of portfolio of products for purposes of Quality Initiatives
14 Putting QI Concepts into Action 1. Readmissions: How can we identify and inform clinicians that a patient is a high risk for readmission? How can we prevent avoidable admissions? 2. Sepsis: What is the adherence rates to our stop sepsis bundle? How do our internal metrics compare to the NYS Department of Health metrics? 3. Patient Falls: What units have the highest rates of patient falls? Why are the rates higher on these units? 4. Outcomes: What are the post-operative ventilator failure rates in the cardiac surgery patients?
15 Sepsis Pathway
16 Readmissions Using predictive analytics, clinical and demographic variables determine a risk score to flag patients at risk for readmissions Very high risk High risk Not at risk Clinical decision support in the EHR, calculates the score and flags the patient
17 Readmissions Targeted interventions are employed throughout the hospital and upon discharge Upon discharge, patients with very high and high risk flags are monitored by RNs, regarding follow-up on outpatient appointments Reports are used to track appointments: Date & time With whom and location Appointment status
18 Readmissions Patient Encounter List Emergency Room Tracking Board Once a patient is determined to be at risk to be readmitted, a flag appears in the EHR for all clinicians regardless of patient encounter type Outpatient / Clinics Emergency Department Inpatient Header in Patient Record
19 Sepsis Nursing Clinical Decision Support in the EHR alerts nurses that a patient is at risk for sepsis Nurse Managers receive daily reports to monitor timely completion of sepsis screenings and identify patients at risk
20 Sepsis - Clinical Case Confirmation For patients identified as at risk for Severe Sepsis, clinical reports and scorecards for components of the sepsis bundle are generated and distributed for clinical review Clinical dyad teams confirm diagnosis and provide feedback on each case via an intranet portal
21 Sepsis Internal Monitoring & Measuring Monthly internal report cards are generated with: Outcomes Process metric adherence rates Report cards include results by: All hospital Service area Patient level Distributed to: Hospital leadership Service area leadership Program leaders Program champions
22 Sepsis - External Reporting About 70 data elements are submitted to the NYS DOH quarterly, and the issues reports back to Mount Sinai, benchmarking us to the entire State
23 Purpose Solutions Approach Delivery Continue overview of Mount Sinai s end-to-end informatics and clinical data analytics approach for quality and safety initiatives. Solutions in our context is how to be an effective data support team for purposes of Quality Initiatives
24 Solution Sets Care Process Predictive Models Study Design Cohort Identification Patient Registry Inclusion/Exclusion Reporting Scorecard (metrics) Algebra Outcomes Studies Risk Adjustment (measures) Statistics Business Need Leveraging clinical data for patient quality initiatives, hospital safety improvement, and cohort outcomes assessment.
25 Care Process How can we identify and inform clinicians that a patient is a high risk for readmission? How can we prevent avoidable admissions?
26 Predictive Models Solutions for Quality Improvement Using administrative/claims/ehr data Predictive risk of readmission Using EHR data from nursing documentation MORSE model for falls prevention Using EHR/clinical data Improve c statistics on MORSE model Sequential, mulit-variate modeling (eg. medmed, labs, ICU monitoring, etc) CDIFF, MEWS+ Unexpected ICU admissions/bouncebacks Take Away: Clinical phenotyping helps to identify patient cohorts for care process, care interventions, disease registries, and outcomes studies.
27 % hospitalizations w/ falls Predictive Models Diagnostic characteristics of Morse Score (predictive model for Patient Falls) Sensitivity (ability to identify a condition correctly) 76.3% Specificity (ability to exclude a condition correctly) 69.0% Positive Predictive Value (precision, poor at confirming the fall) 3.8% Negative Predictive Value 99.5% Accuracy (correct classification rate) 69% C-statistic ,000 hospitalizations ~30% patients have high MORSE Take Away: Using baseline data (nursing assessment) from EHR at time of admission is good but can it be improved using big data?
28 Predictive Models Improving MORSE: Additional clinical data variables Hospital Characteristics Bed units, room locations Patient Characteristics Nursing assessment At time of admission (age, BMI, previous hospitalization and OP visits, labs and meds at time of admission, etc) Using EHR/clinical data that are sensitive to hourly/daily patient assessment Med administration sequencing Med-Med combinations and side effects Active monitoring lab results Take Away: Using baseline data (nursing assessment) from EHR at time of admission is good but can it be improved using big data?
29 Cohort Identification What is the adherence rates to our stop sepsis bundle? How do our internal metrics compare to the New York State Department of Health metrics?
30 Cohort Identification Oh, here s the problem. He s got a doohickey on his thingamabob.
31 Cohort Identification Sepsis Incidence: 6 months data, ,080 cases Dombrovskiy model for sepsis and severe sepsis (incidence rate methodology using ICD-9 codes for septicemia and organ dysfunction) ,501 cases possible inclusion criteria as sepsis and severe sepsis (NQF) using or cases possible severe sepsis (Mount Sinai Hospital) using EHR predictive model Take Away: Different definitions yield different cohorts. Concurrent patient identification allows opportunity for improvement at the point of care.
32 Patient Registries Solutions for Quality Improvement Coding terminology versus Clinical ontology Regulatory data registries, meaningful use, and public reporting typically identify patients through admin/claims ICD-9 coding. CMS/JC measures, NQF Society-based patient data registries typically use clinical ontologies for case ascertainment. Evidence-based practices, P4P measures Allows opportunity for improvement at the point of care. Take Away: Clinical data registries include case adjudication and data reconciliation process. (At MSH, weekly patient lists also double as data QA process)
33 Reporting What units have the highest rates of patient falls? Why are the rates higher on these units?
34 Reporting Solutions for Quality Improvement Data registries allow for conformed dimensions to meet the needs of care monitoring and quality measuring Registries combined with additional data sources to develop data marts (eg. procedures, episodes) Analytics tables for dimensions, calculations Risk adjustment: empirical and MSH cohort Outcomes Studies Data Analytics Data Quality Clinical Data Registries Data Stewardship Clinical Informatics Phenotypic Modeling Take Away: Clinical data registries become conformed dimensions with data marts built for agile analytics, reporting, and ad-hoc queries.
35 Outcomes Studies What is the respiratory failure rate in the cardiac surgery patient? Is respirator failure defined by prolonged ventilator times > 24 hours? What is ventilator times for CT ICU excluding OR ventilator period? Maybe we need to exclude transplant and VAD cases? Does this ventilator time include re-intubation periods? Are bounce-back CT ICU patients included in these counts? Maybe we need to exclude those patients with trach? Do you have risk adjustment model for prolonged ventilation? What is the correlation of surgeon clamp time to CT ICU ventilator? Is there a difference in patients returning from OR after 5pm? Is there a difference in patients extubated on weekend? In the CT ICU we changes our Attending staff model in October 2013, did ventilator times drop after this with statistical significance?
36 Outcomes Studies Solutions for Quality Improvement Electronic Health Record Patient Cohort Clinical Data Registries Data Dictionary Enterprise Data Warehouse Data Mart Take Away: Outcomes studies and statistical data analysis used for quality measures, risk adjustment, and assessment of rules-based clinical pathways. Statisticians use same data sets for data mining and charting.
37 Purpose Solutions Approach Delivery Continue overview of Mount Sinai s end-to-end informatics and clinical data analytics approach for quality and safety initiatives. Approach is implementation of data management best practices in alignment to purpose and delivery of Quality Initiatives.
38 Team Approach Team Roles Knowledge Sharing Data Stewardship Agile Approach The goal is to provide routine data monitoring and implementation of data management best practices to increase and optimize the value of clinical data. Solution Sets Care Process Cohort Identification Reporting Outcomes Studies
39 Team Roles RN Clinical Data Coordinators Clinical Data Managers Result Quality Data Quality Data Management Analysts Information Quality Data Analysts Statisticians Take Away: The people and work effort behind tools, logic, and databases are most important assets in making useful and usable clinical data for QAPI initiatives. QAPI Managers Decision Quality Data Scientists
40 Knowledge Sharing Solutions for Quality Improvement Data dictionary Clinical definitions Measures catalog Data stewardship OPs manual STS_Ver STS_Short_Nam Measure_Name Measure_Description STS_Section sion e Indicates whether the patient was alive or dead at discharge from the hospitalization in which surgery occurred. Includes patients who died after transfer to another acute InHosMort care hospital. Q. Mortality 2.73 MtDCStat Indicates whether the patient was alive or dead at 30 days post surgery (whether in 30DMort hospital or not). Q. Mortality 2.73 Mt30Stat Operative Mortality includes: (1) all deaths, regardless of cause, occurring during the hospitalization in which the operation was performed, even if after 30 days (including patients transferred to other acute care facilities); and (2) all deaths, regardless of cause, occurring after discharge from the hospital, but before the end of the thirtieth OpMort postoperative day Q. Mortality 2.73 MtOpD Number of patients undergoing isolated CABG who require return to the operating room for mediastinal bleeding with or without tamponade, graft occlusion, valve AnyReopNQF dysfunction, or other cardiac reason P. Postoperative Events 2.73 COpReBld Number of patients undergoing isolated CABG who require return to the operating room for mediastinal bleeding with or without tamponade, graft occlusion, valve AnyReopNQF dysfunction, or other cardiac reason P. Postoperative Events 2.73 COpReVlv Number of patients undergoing isolated CABG who require return to the operating room for mediastinal bleeding with or without tamponade, graft occlusion, valve AnyReopNQF dysfunction, or other cardiac reason P. Postoperative Events 2.73 COpReGft Number of patients undergoing isolated CABG who require return to the operating room for mediastinal bleeding with or without tamponade, graft occlusion, valve AnyReopNQF dysfunction, or other cardiac reason P. Postoperative Events 2.73 COpReOth Indicate whether a deep sternal wound infection or mediastinitis was diagnosed DeStWoInf within 30 days of the procedure or any time during the hospitalization for surgery. P. Postoperative Events 2.73 CIStDeep Take Away: Staff share in clinical and knowledge discovery process through common data dictionaries and better assist in data wrangling activities.
41 Data Stewardship Solutions for Quality Improvement The value of clinical data as used for quality improvement and data analytics are defined through key attributes and contributing factors: Purposeful Timeliness Reconciliation Data Quality Accessible Informative Result Quality Data Quality Information Quality Take Away: Clinical data registries in data stewardship program have support model, staff productivity metrics, and data quality assurance measures. Decision Quality
42 Agile Approach Solutions for Quality Improvement Agile approach promotes iterative process and team interaction Internal teams work off of JIRA (issue tracking product) Internal documentation through sharepoint and wiki pages Collaborative teams share files through Box.com (account through Mount Sinai) Take Away: Perfect is the enemy of good is an aphorism commonly attributed to Voltaire. (also can be said, Perfect is the enemy of done. )
43 Concluding Thoughts Impacting operational change in clinical settings requires systemization, focus, team approach, and agile responsiveness. Throughout our presentation, we shared a few takeaways with you. In summary: Systemization knowing where/when/why in the care processes Focus - there is complexity of working with large clinical datasets: fraught with multiple paths (project creep, changing measures, etc.) Team approach communication across clinical and technical and front-end and back-end is essential Agile responsiveness quality improvement methodology requires an iterative design and adaptation to dynamic environment
44 Ken McCardle Allison Glasser Mount Sinai Health System
46 Delivery: Sepsis - Providers Providers receive an alert on their patient list when a patient meets criteria for risk of sepsis. Guidelines, order sets and documentation templates are in the EHR Service Area Leaders receive daily reports to monitor timely completion of provider documentation
2014 Physician Quality Reporting System Data Collection Form: Coronary Artery Bypass Graft (CABG) (for patients aged 18 years and older) Physician Name: Patient Name: Last First MI Date of Birth: / / mm
Leveraging the Continuum to Avoid Unnecessary Utilization While Improving Quality Leadership Summit for Hospital and Post-Acute Long Term Care Providers May 12, 2015 Karim A. Habibi, FHFMA, MPH, MS Senior
Saving Lives, Reducing Costs of Trauma Care Trauma Center Association of America Model of Value Based Trauma Care to Evaluate, Test and Pilot July 25, 2013 Unique Nature of Trauma Injury and Treatment:
Predicting What Matters Using Predictive Analytics to Reduce Suffering, Save Lives, and Optimize the Cost of Care Predictive Analytics for Population Health Management NCHICA Learning Objectives By the
Toward Meaningful Use of HIT Fred D Rachman, MD Health and Medicine Policy Research Group HIE Forum March 24, 2010 Why are we talking about technology? To improve the quality of the care we provide and
WHITE PAPER Clintegrity 360 QualityAnalytics Bridging Clinical Documentation and Quality of Care HEALTHCARE EXECUTIVE SUMMARY The US Healthcare system is undergoing a gradual, but steady transformation.
Big Data Analytics in Healthcare In pursuit of the Triple Aim with Analytics David Wiggin, Director, Industry Marketing, Teradata 20 November, 2014 Agenda The Triple Aim Population Health in Russia The
FROM DATA TO KNOWLEDGE: INTEGRATING ELECTRONIC HEALTH RECORDS MEANINGFULLY INTO OUR NURSING PRACTICE Rayne Soriano MS, RN Manager of Nursing Informatics and Clinical Transformation Program Kaiser Permanente
WHITE PAPER QualityAnalytics Bridging Clinical Documentation and Quality of Care 2 EXECUTIVE SUMMARY The US Healthcare system is undergoing a gradual, but steady transformation. At the center of this transformation
Empowering Value-Based Healthcare Episode Connect, Remedy s proprietary suite of software applications, is a powerful platform for managing value-based payment programs. Delivered via the web or mobile
Heart Surgery Ratings Background and Methodology August 2011 Background The Society of Thoracic Surgeons (STS) is a not-for-profit organization representing more than 6,000 surgeons, researchers, and allied
Empowering Value-Based Healthcare Episode Connect, Remedy s proprietary suite of software applications, is a powerful platform for managing value based payment programs. Delivered via the web or mobile
Find the signal in the noise Electronic Health Records: The challenge The adoption of Electronic Health Records (EHRs) in the USA is rapidly increasing, due to the Health Information Technology and Clinical
M*Modal White Paper WP CI Collaborative Intelligence: Unlocking the Power of Narrative Documentation See us at HIMSS booth 5725 WP CI Page 2 Current Situation The healthcare industry is currently undergoing
Criterion AMERICAN BURN ASSOCIATION BURN CENTER VERIFICATION REVIEW PROGRAM Criterion Level (1 or 2) Number Criterion BURN CENTER ADMINISTRATION 1. The burn center hospital is currently accredited by The
Title: An Organized Strategic Focus to Achieve National Patient Safety Goals (NSPGs) Augmented by an Electronic Medical Record (EMR) Authors: Stephen T. Lawless, MD, MBA, Vice President, Quality and Safety
Accountable Care Organization April 13, 2011 The Indianapolis Association of Health Underwriters Drivers of Payment Reform Increased attention to regional variation in costs and quality Payment for care
Using Predictive Analytics to Reduce COPD Readmissions Agenda Information about PinnacleHealth Today s Environment PinnacleHealth Case Study Questions? PinnacleHealth System Non-profit, community teaching
June 25, 2012 Marilyn B. Tavenner, RN, Acting Administrator Centers for Medicare and Medicaid Services Department of Health and Human Services Attention: CMS-1588-P P.O. Box 8011 Baltimore, MD 21244-1850
Transformational Data-Driven Solutions for Healthcare Transformational Data-Driven Solutions for Healthcare Today s healthcare providers face increasing pressure to improve operational performance while
1a-b. Title: Clinical Decision Support Helps Memorial Healthcare System Achieve 97 Percent Compliance With Pediatric Asthma Core Quality Measures 2. Background Knowledge: Asthma is one of the most prevalent
Using Predictive Analytics to Improve Sepsis Outcomes 4/23/2014 Ryan Arnold, MD Department of Emergency Medicine and Value Institute Christiana Care Health System, Newark, DE Susan Niemeier, RN Chief Nursing
Technology and Analytics Roadmap Positioning for the Future ASU Summer Institute July 15, 2015 Suzanne Rabideau, Rabideau Consulting Brian Jung, MSS Technologies www.msstech.com Introductions Background
2.b.vii Implementing the INTERACT Project (Inpatient Transfer Avoidance Program for SNF) Project Objective: Skilled nursing facilities (SNFs) will implement the evidence based INTERACT program developed
1 IDENTIFYING CLINICAL RESEARCH QUESTIONS THAT FIT PRACTICE PRIORITIES Module I: Identifying Good Questions Objective Describe how to find good clinical questions for research. 2 ntifying good clinical
The State of U.S. Hospitals Relative to Achieving Meaningful Use Measurements By Michael W. Davis Executive Vice President HIMSS Analytics Table of Contents 1 2 3 9 15 18 Executive Summary Study Methodology
Journey to Excellence Kevin W. Sowers, MSN, RN, FAAN President, Duke University Hospital 2 Agenda Introduction to Duke Medicine Call to Action: The Jesica Santillan Story Duke University Hospital s Journey
Comprehensive EHR Infrastructure Across the Health Care System The goal of the Administration and the Department of Health and Human Services to achieve an infrastructure for interoperable electronic health
1 Improving Health with Healthcare Intelligence iht2 Health IT Summit Seattle Thursday, 23 August 2012 Dick Gibson MD PhD Chief Healthcare Intelligence Officer Providence Health & Services Renton WA Agenda
Enterprise Analytics Strategic Planning June 5, 2013 1 "The first question a data driven organization needs to ask itself is not "what do we think?" but rather "what do we know? Big Data: The Management
HIMSS Electronic Health Record Definitional Model Version 1.0 Prepared by HIMSS Electronic Health Record Committee Thomas Handler, MD. Research Director, Gartner Rick Holtmeier, President, Berdy Systems
THE ROLE OF LONG TERM ACUTE CARE HOSPITALS IN THE ACUTE CARE CONTINUUM Wednesday, June 02, 2010 As A Provider Of Continuing Nursing Education, Triumph Healthcare Is Required By Texas Nurses Association
Readmissions as an Enterprise Priority April 24, 2014 Presenters Vincent A. Maniscalco, MPA, LNHA Administrator Middletown Park Rehabilitation and Health Care Center Vmaniscalco@parkmanorrehab.com Eileen
New York ehealth Collaborative Health Information Exchange and Interoperability April 2012 1 Introductions Information exchange patient, information, care team How is Health information exchanged Value
Understanding Diagnosis Assignment from Billing Systems Relative to Electronic Health Records for Clinical Research Cohort Identification Russ Waitman Kelly Gerard Daniel W. Connolly Gregory A. Ator Division
Predictive Analytics in Action: Tackling Readmissions Jason Haupt Sr. Statistician & Manager of Clinical Analysis July 17, 2013 Agenda Background Lifecycle Current status Discussion 2 Goals for today Describe
Business Intelligence Decision Support Through Better Data Management Thursday, 16 August 2012 1 "Do not go where the path may lead; go instead where there is no path and leave a trail." -Ralph Waldo Emerson
Not all NLP is Created Equal: CAC Technology Underpinnings that Drive Accuracy, Experience and Overall Revenue Performance Page 1 Performance Perspectives Health care financial leaders and health information
Online Supplement to Clinical Peer Review Programs Impact on Quality and Safety in U.S. Hospitals, by Marc T. Edwards, MD Journal of Healthcare Management 58(5), September/October 2013 Tabulated Survey
CMS Quality Measurement and Value Based Purchasing Programs Kate Goodrich, MD MHS Director, Quality Measurement and Health Assessment Group, CMS American Urological Association Quality Improvement Summit
Improving Outcomes and Saving Lives in Real Time: How Hospitals Can Use Predictive Analytics Across the Care Continuum Essential Hospitals Engagement Network February 18, 2015 CHAT FEATURE The chat tool
The use of EHR data in quality improvement reports and clinical automatic calculators in ICU Jun 2014 Vitaly Herasevich, MD, PhD, MSs Assistant Professor of Medicine and Anesthesiology, Department of Anesthesiology,
High Rehospitalization Rates: Evaluation and Impact May 29, 2009 Denise Remus, PhD, RN Chief Quality Officer, BayCare Health System BayCare Health System BayCare is the largest full-service, community-based
Excellent Care for All Act, (ECFAA) MSH Quality Improvement Plans (QIP): Report for QIP The following template has been provided to assist with completion of reporting on the progress of your organization
Defining the Core Clinical Documentation Set for Coding Compliance Quality Healthcare Through Quality Information It is time to examine coding compliance policy and test it against the upcoming challenges
Concurrent Session 2.3 Building an ACO Framework: Opportunities and Challenges for Providers and Hospitals Dr. Edward G. Murphy Chairman Amin Neghabat SVP, Business Development 1 Introduction Sound Physicians,
ACCOUNTABLE CARE ANALYTICS: DEVELOPING A TRUSTED 360 DEGREE VIEW OF THE PATIENT Accountable Care Analytics: Developing a Trusted 360 Degree View of the Patient Introduction Recent federal regulations have
Using Medicare Hospitalization Information and the MedPAR Beth Virnig, Ph.D. Associate Dean for Research and Professor University of Minnesota MedPAR Medicare Provider Analysis and Review Includes information
Hospitalizations Inpatient Utilization General Hospital/Acute Care (IPU) * This measure summarizes utilization of acute inpatient care and services in the following categories: Total inpatient. Medicine.
PCMH and Care Management: Where do we start? Patricia Bohs, RN, BSN Quality Assurance Manager Kelly McCloughan QA Data Manager Wayne Memorial Community Health Centers Honesdale, PA Wayne Memorial Community
Unified Communication Strategy Improves Patient Experience, Outcomes Sue Murphy, RN BSN MS Chief Experience and Innovation Officer Sunitha K. Sastry, MPH Director, Experience Improvement and Innovation
Case Study - Clinical Value University of California Davis Health System 2315 Stockton Blvd., Sacramento, CA 95817 Hien Nguyen, MD, MAS Medical Director, Electronic Health Records firstname.lastname@example.org
Interconnectivity Respiratory Therapy and the Electronic Health Record A non-geeks understanding Prepared for the ISRC state conference June 2011 Speaker: Patti Baltisberger RRT (turquoise rhinestone alien)
Population Health Solutions for Employers MEDIA RESOURCES ABOUT MISSIONPOINT MissionPoint s mission is to make healthcare more affordable, accessible and improve the quality of care for our members. MissionPoint
ADVANCING MEASUREMENT OF PATIENT- CENTERED OUTCOMES AND QUALITY METRICS WITH ELECTRONIC HEALTH RECORDS Tina Hernandez-Boussard, PhD, MPH, MS Director, Surgical Health Services Research Unit Assistant Professor
DSRIP QUARTERLY REVIEW PROCESS: PPSs will submit a quarterly report to the Independent Assessor throughout the DSRIP program via the automated MAPP tool which includes Domain 1 DSRIP Requirement Milestone
April 29, 2015 Karen DeSalvo, MD, MPH, MSc National Coordinator Office of National Coordinator for Health IT Department of Health and Human Services 200 Independence Ave, SW Washington, DC 20201 Re: Comments
Application of Engineering Principles to Patient Flow & Healthcare Delivery Jeanne M Huddleston, MD, MS Medical Director, Health Care Systems Engineering Mayo Clinic 2013 MFMER slide-1 2013 MFMER slide-2
Modified Early Warning Score (MEWS) Ruchika D. Husa, MD, MS Assistant t Professor of Medicine i in the Division of Cardiology The Ohio State University Wexner Medical Center MEWS Simple physiological scoring
Leveraging EHR to Improve Patient Safety: A Davies Story Claudia Colgan, Vice President of Quality Initiatives Bruce Darrow, MD, PhD, Interim Chief Medical Information Officer Jill Kalman, MD, Director
Benefit Design and ACOs: How Will Private Employers and Health Plans Proceed? Accountable Care Organizations: Implications for Consumers October 14, 2010 Washington, DC Sam Nussbaum, M.D. Executive Vice
C h a p t e r 1 4 Guidelines for the Operation of Burn Centers............................................................. Each year in the United States, burn injuries result in more than 500,000 hospital
CMS Innovation Center Improving Care for Complex Patients ECRI Institute Dr. Patrick Conway, M.D., MSc CMS Chief Medical Officer and Deputy Administrator for Innovation and Quality Director, Center for
ID Prefix Tag (X4) R000 R200 Provider's Plan of Correction (Each corrective action must be cross-referenced to the appropriate deficiency.) Submission and implementation of this Plan of Correction does
Enterprise Decision Support Advantage Suite Delivers Enriched Information For Improved Decision Making For the past two years, the Patient Protection and Affordable Care Act (PPACA) has been at the center
Succeed with Population Health Management in a Fee-for-Service Environment and Improve Clinical Quality Measures While Transitioning to Value- Based Care Michael J. Tronolone, MD, MMM, Chief Medical Officer
Quality Metrics in Palliative Care R. Sean Morrison, MD Director, National Palliative Care Research Center Director, Hertzberg Palliative Care Institute Hermann Merkin Professor of Palliative Care Professor,
HAI LEADERSHIP PARTNERING FOR ACCOUNTABLE CARE Cepheid s Government Affairs Department Advocating for Patient Access to Molecular Diagnostics in the Era of Healthcare Reform A TEAM APPROACH Legislative
Quick Turnaround with Administrative Health Data Katherine Giuriceo, PhD Research and Rapid Cycle Evaluation Group Center for Medicare and Medicaid Innovation, CMS October 2, 2015 1 Overview Center for
BUNDLING ARE INPATIENT REHABILITATION FACILITIES PREPARED FOR THIS PAYMENT REFORM? Uniform Data System for Medical Rehabilitation Annual Conference August 10, 2012 Presented by: Donna Cameron Rich Bajner
Advisory Panel for Health Care Advancing the Academic Health System for the Future: Profiles in Academic Health System Leadership November, 2013 Project Focus and Methodology Project Focus This project
AnEssentialGuideTo ClinicalDocumentation Improvement Writenby A.Jamal,MBA,CHIM &C.Grant,CHIM Whitepaper, September 2014 Whitepaper, Sept 2014 An Essential Guide to Clinical Documentation Improvement Written
Uncovering Value in Healthcare Data with Cognitive Analytics Christine Livingston, Perficient Ken Dugan, IBM Conflict of Interest Christine Livingston Ken Dugan Has no real or apparent conflicts of interest
Case Study: Using Predictive Analytics to Reduce Sepsis Mortality 1 Learning Objectives 1. Understand how an automated, real time IT intervention can help care teams recognize and intervene on critical,
Advisory Panel for Health Care Advancing the Academic Health System for the Future: Profiles in Academic Health System Leadership November, 2013 Project Focus and Methodology Project Focus This project
49th Annual Meeting Prescription for the Future: Pharmacists Influencing Positive Health Outcomes Daniel E. Buffington, PharmD, MBA, FAPhA Clinical Pharmacology Services, Inc Tampa, FL Disclosure(s) Daniel
Changing Clinical Behaviors to Lower Costs and Reduce Catheter-Associated Urinary Tract Infections (CAUTI) ARKANSAS METHODIST MEDICAL CENTER: How a foley catheter management system combined with education
Data Management, Audit and Outcomes Providing Accurate Outcomes and Activity Data The Trust has in place robust mechanisms for capturing and reporting on all oesophago-gastric cancer surgery activity and
Welcome to the Model 4 CABG Bundled Payment Presentation December 11, 2013 7:30 am Lance Auditorium Please sign in at the Registration table located at the back of the room. CMS BUNDLED PAYMENTS FOR CARE
Realizing ACO Success with ICW Solutions A Pathway to Collaborative Care Coordination and Care Management Decrease Healthcare Costs Improve Population Health Enhance Care for the Individual connect. manage.
MDaudit Compliance made easy MDaudit software automates and streamlines the auditing process to improve productivity and reduce compliance risk. MDaudit As healthcare compliance, auditing and coding professionals,
Value Based Care and Healthcare Reform Dimensions in Cardiac Care November, 2014 Jacqueline Matthews, RN, MS Senior Director, Quality Reporting & Reform Quality and Patient Safety Institute Cleveland Clinic
Using Technology to Reduce Catheter-Associated Urinary Tract Infections Abstract Catheter-associated urinary tract infection, a common and potentially preventable complication of hospitalization, is the
Stephen Nuckolls CEO, Coastal Carolina Health Care, P.A Jeff Spight SVP, ACO Market Operations Universal American Theresa Dolan COO Mount Sinai Care April 25, 2014 Overview of the ACO Beneficiary Assignment
A Population Health Management Approach in the Home and Community-based Settings Mark Emery Linda Schertzer Kyle Vice Charles Lagor Philips Home Monitoring Philips Healthcare 2 Executive Summary Philips
Population Health Management A Key Addition to Your Electronic Health Record Rosemarie Nelson, MS Principal Consultant, MGMA Sponsored by: 1 Contents Introduction... 3 Managing Populations of Patients...
MINISTRY OF HEALTH ELECTRONIC MEDICAL RECORDS MEXICO CITY November 10, 2010 OVERVIEW Why Electronic Medical Records (EMR)? Planning, Implementation & Challenges Observed Outcomes Valuable Lessons 1 EMR
Accountable Care: Implications for Managing Health Information Quality Healthcare Through Quality Information Introduction Healthcare is currently experiencing a critical shift: away from the current the