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Robert E. Connors, FACHE, PMP MS in Information Systems Technology MHA, Healthcare Administration PowerPoint Presentation
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Robert E. Connors, FACHE, PMP MS in Information Systems Technology MHA, Healthcare Administration

Robert E. Connors, FACHE, PMP MS in Information Systems Technology MHA, Healthcare Administration

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Robert E. Connors, FACHE, PMP MS in Information Systems Technology MHA, Healthcare Administration

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  1. HIMSS National Capital Area (NCA)“The Role of Informatics in Improving Quality and Safety in the Health Care Delivery System”Key Bridge Marriott Hotel, Rosslyn, VA 16 March 2011, 5:30-8:00 PM Robert E. Connors, FACHE, PMP MS in Information Systems Technology MHA, Healthcare Administration Executive Healthcare IT Manager and IPA Henry M. Jackson Foundation for the Advancement of Military Medicine U.S. Army Medical Research and Materiel Command Telemedicine and Advanced Technology Research Center (TATRC) Fort Detrick, MD Robert.Connors@tatrc.org

  2. The Clinical Looking Glass Project: A Civilian-Military Partnership to Revolutionize Clinical Intelligence http://exploreclg.montefiore.org/

  3. How Empowered Are Your Practitioners?

  4. Break-Through Clinical Intelligence Application Developed By Emerging Health IT, Inc./Montefiore Medical Center and In Use There Loading of Clinical Looking Glass data repository from EHR and other data Ability to create “user defined” patient groups quickly, i.e. “All Medicare patients with CHF with a diagnoses of diabetes within the last 6 months” User defines clinical outcomes of interest “at will” (e.g., lab value within normal limits, readmission, mortality) of a patient group CLG searches for user-defined outcomes in clinical data repository, sends data to statistics engine, then presents user with study results CLG fully documents all criteria, statistical methods and output Clinical Looking Glass – What is it? • QI studies are now executed in minutes, not weeks.

  5. Clinical Looking Glass Story Themes

  6. Information Must Be Patient-Centric

  7. Timeframes must be relative to an individual’s experience.

  8. Cohort Paradigm: Patient-Centric • Each individual starts therapy at different times—rolling enrollment • Period of observation (e.g. for good lab value) are different for each individual • Individual contra-indications taken into account • Stop looking for outcome when patient is no longer at risk. • Example: Patients who die should not be followed for good lab value.

  9. Practitioner Thinking Is Already Patient-Centric • Doctors and nurses are trained to think from this perspective • For example: epidemiology studies, clinical literature • Medical institutions need to nurture and leverage their creative intellectual capacity • Clinicians need the flexibility to explore with ad hoc questions for Quality Improvement, Research, and Education.

  10. Accountable Care Demands • Evaluate success for each patient using metrics achieved within a defined time after the patient’s own start time • Create Patient Cohorts with a unique index date (start time) for each patient when he became eligible for intervention • Evaluate institutions and practitioners by how they manage their cohorts over time

  11. Cohort Paradigm: Patient-Centric • Each individual starts therapy at different times—rolling enrollment • Period of observation (e.g. for good lab value) are different for each individual • Individual contra-indications taken into account • Stop looking for outcome when patient is no longer at risk. • Example: Patients who die should not be followed for good lab value.

  12. Practitioner Thinking Is Already Patient-Centric • Doctors and nurses are trained to think from this perspective • For example: epidemiology studies, clinical literature • Medical institutions need to nurture and leverage their creative intellectual capacity • Clinicians need the flexibility to explore with ad hoc questions for Quality Improvement, Research, and Education.

  13. Analytic Steps • Create a cohort: • Patients seen in three outpatient clinics with a HgbA1c of >9 • Define patients w/ “Good” and “Bad” outcomes • Identify as “Good” those patients who achieve a HgbA1c of 0-7 • Identify as “Bad” those Patients who achieve of HgbA1c of 9-25 • Compare the hospitalization experience of the two groups over the ensuing year

  14. Overall Vision of Healthcare Market - Direction Movement away from Fee For Service to Longitudinal Responsibility for the Patient CMS - Pay for Performance (P4P) Stimulus Funds for: Research, HIE/RHIO’s and EMR’s Movement toward Universal Health Care Insurance Coverage Patient Centric – Medical Home

  15. Who are the Constituencies? The Ultimate Market? • Classically • Insurance companies – classic utilization review (so 1980’s) • The Future • Clinicians • Patient Remediation Management (a new concept and core to cost effective care), • Physician Education • Administrators • Concierge oversight of delivery of care • Nurses • Quality Improvement Professionals • Health care delivery systems – to supervise and improve longitudinal care delivery and coordination • Immediate customers: DOD, VA, Kaiser, HHS?, FDA? • Ultimate customers all patients in single payer system. • Patients themselves might query – what is going to happen to a patient like me (mortality complications – what if scenarios bp, cholesterol..)

  16. Why Is Montefiore confident that the market is moving in CLG’s direction? CMS pay for performance (primitive but a start) JCAHO standards speak to this longitudinal oversight responsibility American Board Internal Medicine – expects MD to review their own patient charts and learn from errors – required part of Recertification American College of Graduate Medical Education Obama administration emphasis on effectiveness research and importantly continuing the Bush administration’s emphasis on the centrality of the electronic medical record to obtain value at an affordable cost.

  17. What are the nuances between healthcare information technology management, clinical informatics, and biomedical informatics? • My perspective: We don’t need to dwell on this too much, but…. • Healthcare Information Technology Management is not the same as Clinical or Biomedical Informatics • Information Technology = Hardware and Software • Clinical Informatics is about using information technology to help improve healthcare access, availablity, acceptability, cost-effectiveness, continuity and quality. (3A2CQ) • Clinical Informaticists must first understand the practice of medicine and business of healthcare delivery, then understand how to apply technology to improving that business • Healthcare CIOs have typically failed because they don’t understand the practice of medicine, and have been too focused on technological issues. Successful CIOS act as true clinical informaticists and must understand what outcomes are possible, given an investment in IT • Hence, the evolution of new roles: CIO versus CTO versus CKO versus CMIO, etc. CAIO? CNIO? CFIO?

  18. What are the nuances between healthcare information technology management, clinical informatics, and biomedical informatics?, continued • Traditional clinical informatics involves the application of technologies to improve the capture, use, distribution, and storage of inpatient admissions and clinical encounters • Biomedical Informatics is more involved with the application of technologies to improving life sciences processes, proteomics, genomics, pharmacovigilance, post-marketing drug surveillance, and the research surrounding the fundamental causes of disease • Given the move to “personalized medicine”, proteomic and genomic information will merge with traditional clinical encounter and inpatient observation documentations in the Electronic Health Record, demanding new data models that account for combined data • Semantic web technologies may be able to help mediate terminology differences between traditional EHR and life sciences data (i.e. Mirhaji/Kashyap’s work) • Predictive modeling and data mining are important technologies used by both clinical and biomedical informaticists

  19. Patient Trajectory • Business Intelligence Tools (BI) use Timeframes such as “calendar year”, “quarterly” • BI tools Slice and dice events in calendar time but give no sense of the patient’s temporal trajectory experience

  20. Patient-Centric Cohort is Key Concept 1/1/2005 1/1/2007 1/1/2006 Patient # 1 0 Diabetes Control 2 0 0 = index date 3 0 (EG start therapy) 4 0 5 0 = outcome 6 0 (EG achieve lab value) 7 0 8 0 0 = patient experience 9 0 10 0 4 / 10 = 40% What % of new diabetic patients were controlled in the year 2005?

  21. Cohort Concept (cont) Enrollment 2 Years 1 Year Patient # Diabetes Control 3 0 0 = index date 8 0 (EG start therapy) 9 0 1 0 = outcome 4 0 (EG achieve lab value) 7 0 0 = patient experience 5 0 10 0 2 0 (same data, re-sorted) 6 0 5 / 10 = 50% What % of new diabetic patients were controlled within 1 year?

  22. We have more than a report card

  23. We have a tool for targeted remediation

  24. Identify “Failing” Patients and their Providers

  25. Object Oriented Design Philosophy • The Output for the first analysis becomes the input for the next supporting rapid chains of sequential discovery • Each analysis creates reusable objects for reuse, modification, and sharing with colleagues supporting an adaptive intellectual ecosystem

  26. Comparing Hospitalization Rates for Diabetics Diabetics brought under good control vs. Diabetics who achieve awful control

  27. Analytic Steps • Create a cohort: • Patients seen in three outpatient clinics with a HgbA1c of >9 • Define patients w/ “Good” and “Bad” outcomes • Identify as “Good” those patients who achieve a HgbA1c of 0-7 • Identify as “Bad” those Patients who achieve of HgbA1c of 9-25 • Compare the hospitalization experience of the two groups over the ensuing year

  28. Impact of “Good” Diabetes Management: A reduction of almost 1/3 in hospitalizations

  29. Benefits of Clinical Looking Glass • Monitor efficiency of our processes • Monitor the quality of the outcome • Provide report Cards • Provide a tool for ongoing QI • Identify areas of focus • Track those patients over time • Analyze processes and results • Target remediation for improvement of care • Over longer term, • Rationalize the evaluation of care and • Bring down true costs

  30. CLG SOA Integrated Architecture

  31. The DOD-Clinical Looking Glass Project • Congressional Special Interest Project and DOD Joint Program Committee for Healthcare IT Research Project • Research Objectives: • Technological Assessment to determine if Clinical Looking Glass clinical intelligence tool in use at Montefiore Medical Center, can be used with Military Health System (MHS) data • Test scalability • Evaluate clinician response to usability for quality assurance and patient remediation issues • Use in actual QA and research studies (Medical Home)

  32. The DOD-Clinical Looking Glass Project, continued • Phase I (complete): • Developed Clinical Looking Glass Platform with de-identified clinical data from National Capital Area • Demonstrated platform to hundreds of clinicians and executives with very favorable clinician reviews • Worked through Institutional Review Board, Data Use Agreements, HIPAA requirements

  33. The Clinical Looking Glass Project, continued • Phase II (underway): • Establishing platform with live PHI data supplied from new USAF Health Data Services Warehouse • Re-engineering application to make it comply with all DOD Security Requirements • CAC-enabling access to the application • Working on alternative bolt-on application to existing Military Health System (MHS) data warehouses, versus mapping of data from warehouse to Clinical Looking Glass • Assessing scalability of application to the size of the MHS using Teradata, Netzeeza, and cloud computing • Will make application available for continued evaluation by clinicians at Walter Reed National Military Medical Center

  34. Project Funding • Total Research Funding To Date is $5.8 million • FY 06, 08, 09, and 10 Congressional Special Interest Funding • FY 10 Department of Defense Joint Program Committee for Healthcare IT Research Funding • Oversight by U.S. Army TATRC, USAF, USN SPAWARS, and DOD JPC for Healthcare IT

  35. What is the IT Impact on “3A2CQ”? • Seek technologies that can help improve healthcare: • Availability • Acessibility • Acceptability • Cost-Effectiveness • Continuity • Quality • If no potential for“3A2CQ”, don’t invest in it! Clinical Looking Glass is About Promoting All Of These !

  36. Points of Contact: Robert E. Connors, FACHE, PMP Executive Healthcare Manager and IPA Henry M. Jackson Foundation for the Advancement of Military Medicine working at U.S. Army Telemedicine and Advanced Technology Research Center Robert.Connors@tatrc.org (571) 308-9534 (Google Voice: Forwards Automatically To Apple iPhone, Work, and Home) Eran Bellin, MD Vice President Clinical IT Research and Development Emerging Health Information Technology Department Outcomes Analysis and Decision Support Montefiore Medical Center ebellin@emerginghealthit.com

  37. Questions?

  38. Backup

  39. Clinical Looking Glass Was Developed By Montefiore Medical Center and Is In Use There Large Urban Medical Center (Bronx, New York) 4 Hospitals, 1491 beds 94,000 Discharges 280,000 Emergency Department Visits 21 Clinics , 2 Million Clinic Visits 150,000 Covered Lives Through IPA Chronic Disease Treatment and Prevention Challenges: Asthma Diabetes HIV Heart Disease