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DNA of Technology for Chronic Disease Management

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					      The “DNA” of Technology for
     Chronic Disease Management




David J. Morin – CEO and Co-Founder
       Cielo MedSolutions LLC
                                          What I Will Cover



Elements to consider when evaluating technology for
           chronic disease management




          D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                    The Goal of Technology

 A lot has been said about technology and chronic
  disease management (CDM) /quality improvement
  (QI)
 But the goal isn’t the implementation of
  technologies per se
 It is high-quality, patient-centered care
 The technology is the catalyst – it is what you can
  organize around and through which you can
  enable change


           D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                                  On the Road to QI…

If improved quality of care is your journey, think of…

    • Your technology as your
      vehicle (the enabler)

    • Your care data as the
      vehicle’s dashboard (what
      you are managing)

    • CDM/QI programs as a road
      map (how you get there)
           D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                          Technical Components

Disease management can be enabled through tools like:
   Electronic health records
   Disease registries
   Clinical decision support
Through functionality like:
   Care reminders at the point of care
   Population management functionality to reach due patients
   Performance feedback reports to monitor care delivery
   Patient education reports
But, you need the right underlying elements within the
 technology for success


              D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                              The Technology DNA

 Correct and complete information….
 For all your patients and patient problems…
 Collected, presented and delivered effectively….
 Giving everyone the right information and tools to
  drive care improvement (including the patient)…
 Easily implemented, adoptable in bites….
 And adaptable to future needs….




           D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                                     Correct and Complete
                                               Information
Phillips and Klinkman1 say your data must answer:
  Who has ________ ? [disease registries]
   • the basis for point-of-care decision support and quality assessment
  Who gets ________ ? [the probability of specific diagnoses from
   common presenting symptoms]
   • basic clinical epidemiology in primary care
   • requires episodes of care
  What is the context in which the care is provided?
   • competing demands, social problems, patient goals and priorities
   • multimorbidity
  What happened out there?
   • track care across settings – primary to specialty care, office to
     hospital

              D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                                               A Data Model
Phillips and Klinkman refer to a primary care
information model – simple building blocks to
capture complex reality1
Person                            Clinical Modifiers
    Demographics                      Prevention
    Social structure                  Risk Status
    Goals and                         Significant Events
    preferences                   Actions (“Process”)
Problems                              Decisions
    Current/active                    Interventions
    Severity                          Plans
                                  Time
                                      Episode structure


              D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                             Correct and Complete
                                       Information
Make sure your data is telling you what’s really going on
   Administrative diagnosis data has issues when used for clinical
    documentation and decision support
    • 50% inaccuracy in administrative data (Jollis, et al, 1993)2
    • 43% inaccuracy in administrative data (Peabody, Medical Care, 2004)3
    • Billing and reimbursement coding mindset restricts improvement
      activities (Langley J., Beasley C. 2007)4


   ICD-9 limited in “fit” for primary care
    • 45% of presenting problems don’t fit (White, 1969)5
    • ICD-9-CM captures considerably less than half of the information
      considered important (Chute, C. 1995)6
    • Lack of documentation regarding severity

              D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                              Correct and Complete
                                        Information
 ICD-10
    155,000 terms – still not 100% coverage
 SNOMED-CT
   Over 344,000 concepts – too much?
 ICPC - International Classification of Primary Care
    95% fit to primary care with specificity
    Symptom and social problem diagnoses
 ENCODE
   10,000 primary care clinical terms
   Chronic, acute, family history, social problem, symptom
   Mapped to ICD-9, ICD-10, ICPC



               D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                             Correct and Complete
                                       Information
 Capture all patient problems
   Manage to the patient, not to the disease
    • Be organized by patient; not disease, but responsive to disease
      populations (Austin, 2007) 7
   “Registry of the Day” not a really good idea
    • Expensive, time-consuming, slows benefit
    • Results in silos of data (this is not your goal)
   Capture both billable and non-billable diagnoses
 Know the source of the data
   attribution, administrative or self-reported



              D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                           Correct and Complete
                                     Information
 Know where the patient “is” relative to the care
 they need
   must have context
   a response to a reminder for an evidence-based
    guideline is, in many cases, not a binary response (Y/N)
   you “ain’t done ‘till you’re done”




            D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                          Correct and Complete
                                    Information
Examples
 Colorectal cancer screening – many times the first
  occurrence of this reminder leads to a discussion on the
  options on this screening. Patient usually goes home to
  decide and screen is ordered after a 2nd discussion.
 • Reminder is flagged “discussed” on first visit, “ordered” on second
   visit. Only when the screening is completed is guideline considered
   “done”.
 A1C evaluation – usually the patient is given a lab
 requisition to have blood drawn and tested at a later date.
 • Reminder is flagged “ordered” on first visit. Only when a result from
   the test is returned is guideline considered “done”.


           D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                          Collected, Presented and
                               Delivered Effectively
 All patient encounters must utilize the technology
   If not all-patient, will not become routine in care delivery,
    adoption will suffer


 Presenting information has to be simple and fit into
  the existing workflow
   Shellhase (2003)8 found that 75% of physicians using an
    EHR ignored or did not observe flashing reminders for
    preventive services
   How many clicks and/or screens to get to the info you
    need?
             D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                        Collected, Presented and
                             Delivered Effectively
 Inaccurate or untimely information will lead to
  frustration and adoption will suffer
 Examples with regards to reminders:
  Prompt for A1C, but patient had already been given lab
   req.
  Prompt for pap smear, but it is not due for six months
  Lack of comorbidity data, wrong evidence-based
   guideline presented (like diabetes and renal disease vs.
   diabetes)
  Prompt for mammogram, but mammogram already
   delivered
           D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                          Collected, Presented and
                               Delivered Effectively
 Care reminders must take into account the correct
  variables
 Examples include:
       Severity                               Diagnosis comorbidity
       Family history                         Age and gender
       Prior services                         Not a candidate situations
       Prior service values                   Time of year
       Vitals (BP, BMI, etc..)                Patient voice




             D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
      Delivering Actionable Data to
                      Improve Care
 Just reporting a score, “good”
 Reporting such that you can increase your score
  (and increase quality), “priceless”
 You must have at your fingertips timely, accurate,
  actionable and forward-looking data to continually
  drive improvement across the population
 A care report should help you to DO something




           D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                Want This Type of Report?




Met Requirement                           Did Not Meet Requirement
268 (52.2%)                               245 (47.8%)




       D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
    How About This Type of Report?



Met Requirement              Did Not Meet Requirement

268 (52.2%)                  245 (47.8%)

                             22 - discussing options with patient
                             68 - service ordered, no result returned
                             111 – patient has not been seen
                             73 – patient refused
                             11 – patient not a candidate




         D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                           Or This Type of Report?

268 Met           245 Did Not Meet Requirement
Requirement
268 (52.2%)       245 (47.8%)

                  22 - discussing options with patient
                        Stan C (734) 555-1212 – discussed on 4/11
                  68 - service ordered, no result returned
                        Bob B – (313) 555-1212 - 327 days since ordered
                        Sal L – (906) 555-1212 - 155 days since ordered
                  111- patient has not been seen
                        Dave P – (734) 555-1212 – last value of 15.5 on 12/3/06
                  73 - patient refused
                        Sally P – sp@gmail.com – last asked on 3/3/07




        D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                                                                       Reporting

 A good reporting module should:
   be the “front windshield”, not the “rearview mirror”
   give you access to your data
   allow you to monitor the population but action the
    individual
   provide an easy way to modify and configure
   enable analysis differently than how data is collected
    data collection to the guideline – reporting to the quality program




              D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                 Giving the Entire Care Team the
                Right Tools to Drive Improvement

 Physician-directed primary care team managing patient
  care
 Your technology should support a “Team Sport” concept
   Everyone in a practice has a role in improving care quality
   Everyone in a practice should have tools to improve care quality
   Reporting and actionable data provides that
 Some examples:
   Pre-visit planning
   Patient outreach
   Data sharing




              D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
        Giving the Patient Simple and
        Effective Tools to Participate
 Patient Health Summary/Care Plan
   Individualized document showing patient’s status on key indicators
    and needs
   List of future needed services with dates
    • In simple language, no guess work
 Personal Health Record (PHR) – patient’s electronic file of
  health data
   This sounds easy, but in reality is tough to fit into workflow
    • Which PHR will you support? - Over 100 efforts underway to build a “PHR”
    • How are you going to access it?
       • Do you want outside devices plugged into your network?
       • What if it isn’t simple to get the information?
    • Can you trust the information it? - What’s the source?




                 D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
               Adaptable to Future Needs

Don’t implement technology to support today’s needs,
implement technology to support both today’s and tomorrow’s
needs
 All-problem, all-patient registry – as new quality programs emerge, an
  all-problem, all-patient registry supports them “on-the-fly”

 Use of a simple data model – a database that is easy to understand and
  query is one that is easy to write reports against. A database with 100s
  of tables is very difficult to use for report writing (and very expensive)




               D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
              Adaptable to Future Needs
Support of data sharing standards – your technology must be able to
 both send and receive data to other systems and entities

Table-based versus programming-based decision support engine –
 adoption of new guidelines can be done in a matter of hours

         Table Based                           Programming Based
         Gender: MF                            Sub A1C (Gender, Age)
         Age: > 6,570                           If Gender = M or F and
         Dx: T90                                  Age is > 6,570 and
         Service to Satisfy: 83036                Dx = “T90” and
         Days Since Last Svc: 365                 DysSinceLastSvc(83036)
                                                    <366
                                                Then…..



              D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                                     Adoptable in Bites

 Keep it simple! (aka “dumb it down”)
 Go after this in a phased approach
 Start with something simple that will provide a
  “win” and is easily implemented
 Rollout new pieces in phases
  Make sure all stakeholders have buy-in and a voice
   in design and rollout
  Keep each phase manageable, well-defined and
   focused
  Over-communicate and get at fears right away

          D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                                                            In Summary

 Technology is a tool to help with your chronic
  disease management/quality improvement
  program
 When evaluating technology, focus on its “DNA” as
  much as usability and features
 Keep things simple, easy and effective
 Ensure you are buying for both today and the
  future
 It will work! It will improve care delivery! It will
  have a positive return on investment!
           D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                                                                     References
1 Phillips R, Klinkman M. “Health IT to Support the Patient-Centered Medical Home”
 www.ncvhs.hhs.gov/071127p1.pdf

2 Jollis JG, Ancukiewicz M, DeLong ER, Pryor DB, Muhlbaier LH, Mark DB.
  Discordance of Databases Designed for Claims Payout versus Clinical Information
  Systems: Implications for Outcomes Research” Ann Intern Med. 1993 Oct 15;119(8):844-
  50.

3 Peabody JW, Luck J, Jain S, Bertenthal D, Glassman P. “Assessing the Accuracy of
  Administrative Data in Health Information Systems” Med Care. 2004 Nov;42(11):1066-72.

4 Langley J, Beasley C. Health Information Technology for Improving Quality of Care in
  Primary Care Settings. Preparted by the Institute for Healthcare Improvement for the
  National Opinion Research Center under contract No. 290-04-0016. AHRQ Publication 07-
  0079-EF. Rockville, MD: Agency for Healthcare Research and Quality. July 2007
http://healthit.ahrq.gov/portal/server.pt/gateway/PTARGS_0_1248_661809_0_0_18/AHRQ_
  HIT_Primary_Care_July07.pdf


                  D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09
                                                                       References
5 White K. “Improved Medical Care Statistics and the Health Services System” Public Health
  Reports Vol. 82, No. 10, October 1967

6 Chute C. “Moving Toward International Standards in Primary Care Informatics”.
  www.ahrq.gov/research/pcinform/dept3.htm November 1995

7 Austin B. “A Tour of the Model: Clinical Information Systems and Decision Support”. Dec
  10 2007. www.improvingchroniccare.org/downloads/redesigning_chronic_illness_care__the_ccm.ppt

8 Schellhase KG, Koepsell TD, Norris TE. “Providers' reactions to an automated health
  maintenance reminder system incorporated into the patient's electronic medical record” J
  Am Board Fam Pract. 2003 Jul-Aug;16(4):350-1.




                    D. Morin - "Technology DNA for Chronic Disease Management" - Sep 09

				
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