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Identifying deficiencies in long-term condition management using electronic medical records

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Jim Warren
University of Auckland
(Wednesday, 11.15, Data Analysis Workshop)

Published in: Health & Medicine

Identifying deficiencies in long-term condition management using electronic medical records

  1. 1. Identifying deficiencies in long- term condition managementusing electronic medical records Prof Jim WarrenChief Scientist, National Institute for Health Innovation Chair in Health Informatics The University of Auckland The National Institute for Health Innovation
  2. 2. Overview• Why we do long-term condition data mining / analysis• Case study in hypertension management – Context, tools/methods, findings and implications• Case study in electronic referrals• What’s it mean for you?
  3. 3. Why the interest?• Burning platform – Increased rates of chronic illness (that’s not entirely bad, btw) – Ageing population – Cost / workforce / delivery meltdown!• Lots of data – Computerisation naturally lays down data – Seems wrong not to use it • Both for refinement of processes, and to guide the search for more radical transformations in health delivery
  4. 4. Chronic conditions, esp. Hypertension (or anything ‘vascular’)• Amenable (and interesting) for analysis of long sequences in transactional electronic health records (EHRs)• Blood pressure (BP) is a huge risk factor – Cardiovascular disease (CVD) risk doubles for every 20/10 mm Hg • Also implicated in kidney failure – Very controllable with medication – Several classes of medication with different side-effects and benefits/indications (makes it interesting!)• Statins/cholesterol and blood sugar control also very relevant
  5. 5. General Practice Computing• Highly computerized – High percentage penetration and good maturity/depth of use – In common with Australia and UK• NHI – Well-established National Health Index numbers for patients – Allows research linkage to national data collections (notably PHARMAC for dispensing)• Variable (but good) rates of problem classification – Dx often coded with Read Codes ver 2 (rogue former UK system) – Lower quality entry of local observations (e.g., BP)• Electronic test results and hospital discharge summaries received as HL7 messages – GP can accept lab result items into their database
  6. 6. Study context• General practice PMS (practice management system) software data include: electronic prescribing, lab test results review, problem lists, observations (e.g. BPs), practice notes• Work with West Fono Health Care – Pacific led practice in West Auckland – Iterative analysis of PMS data to identify opportunity for improvement in management of long-term conditions
  7. 7. Criteria model• Abstracted audit classes from general practice opportunities for quality improvement Criterion Failure to Sustained Contra- Unsustained Measure Failure to indicated Treatment Outcome Meet Target Treatment Lapse, low MPR (medication possession ratio)
  8. 8. ChronoMedIt Architecture PMS Cohort Report 1 Data Canonical Query Uptake Case Report EMR Processor App PMS 4database Timeline Graph Criterion 5 3 Criteria Domain Authoring Ontology App 2Oral presentation (full paper) – MIE 2011, 28-31 August 2011 Warren Slide 8
  9. 9. Visualisation: Bad pattern, low MPROral presentation (full paper) – MIE 2011, 28-31 August 2011 Warren Slide 9
  10. 10. Good pattern
  11. 11. Ontology (OWL, maintained w/ Protégé)
  12. 12. State-transition model• Times at which therapy changes indicate opportunities to critique performance / decision• Designed set of alerts with 26% sensitivity and 93% specificity by GP panel assessmentDeveloping high-specificity anti-hypertensive alerts by therapeutic state analysis ofelectronic prescribing records. JAMIA 14(1), 2007Gadzhanova S, Iankov II, Warren JR, Stanek J, Misan GM, Baig Z, Ponte L.
  13. 13. Temporal reasoning on intervals• There are a LOT of cases to consider when evaluating a couple of transactions - each with a temporal ‘shadow’ - against an interval – Practical contribution of ChronoMedIt is to ‘funnel’ a wide range of practical parameters into a small set of well-tested queries A Prescription begins and ends before contraindication B Treatment continues into contraindication C Treatment and diagnosis begin together D Treatment is contraindicated when it commenced Classification (ongoing chronic condition) Investigation Period 1 Investigation Period 2Four temporal relationships (A-D) of a treatment and a problem classification that contraindicates it
  14. 14. Uses• ChronoMedIt analysis of PMS data provides a basis for – Research cohort identification (how do low and high MPR groups differ?) – Intervention cohort identification (follow up to raise MPR) – Tracking of progress over time, and variation between sites – Interactive decision support – Critique of criteria per se
  15. 15. Some ChronoMedit findings• For 646 patients prescribed at least one of simvastatin, metoprolol succinate, bendrofluazide, felodipine, cilazapril and metformin in a 15-month period, 50% had high adherence MPR (Medication Possession Ratio) ≥80% to all (out of those 6) that they were prescribed – High adherence to individual medications was from 68% (felodopine) to 55% (metformin)• For patients prescribed ACEi or ABR with Dx of hypertension and diabetes, non-adherent patients (MPR <80% or lapse >30 days in 12 months) are three times more likely to have uncontrolled BP (odds ratio = 3.055; p = 0.012).Mabotuwana T, Warren J, Harrison J, Kenealy T. What can primary care prescribing data tell us about individual adherence to long-term medication?-comparison to pharmacy dispensing data. Pharmacoepidemiol Drug Saf 2009;18(10):956-64.Mabotuwana T, Warren J, Kennelly J. A computational framework to identify patients with poor adherence to blood pressure lowering medication. Int J Med Inform 2009;78(11):745-56
  16. 16. The Lack of attention to medication adherence (sometimes termed ‘compliance’)• From a study of therapy intensification and adherence of mid-Western VA patients “Patients’ prior medication adherence had little impact on providers’ decisions about intensifying medications, even at very high levels of poor adherence…. suggests that providers are simply not taking patients’ prior medication adherence into account in making medication management decisions.”Heisler M, Hogan MM, Hofer TP, Schmittdiel JA, Pladevall M, Kerr EA. When more is not better: treatment intensification among hypertensive patients with poor medication adherence. Circulation 2008;117(22):2884-92.
  17. 17. Understanding adherence in the Pacific population• 20 Samoan patients (10 high MPR, 10 low) – Lower adherence: ‘lack of transport’, ‘family commitments’, ‘forgetfulness’, ‘church activities’, ‘feeling well’ and ‘priorities’ – High adherence: ‘prioritising health’, ‘previous event’, ‘time management’, ‘supportive family members’ and ‘relationship with GP (language and trust)’ – Common to both: ‘coping with the stress of multiple co-morbidities’Chang Wai K, Elley CR, Nosa V, Kennelly J, Mabotuwana T, Warren J. Perspectives on adherence to blood pressure lowering medications among Samoan patients: qualitative interviews. Journal of Primary Health Care 2010;2(3):217-224
  18. 18. Intervening – AIM-HI• Adherence Innovation in Medication use for Health Improvement – Worked with West Fono Health Care – Used ChronoMedIt to define a register of some 200 patients with anithypertensive MPR<80% (for a 6 month period) – Two nurses undertook Chronic Disease Management (CDM) on these patients – Significantly improved MPR for the intervention year as compared to similar (low MPR) patients in a control Pacific-led practice • Marginally significant improvement on systolic BP as measured ambiently at the practice
  19. 19. Onward with medication adherence• Adherence promotion deserves more attention – By reminder • Packaging, alerts/reminders, invoking ‘whānau’ (family) – By mobile phone • Assess and modify the belief model underpinning non- adherence• Continue to improve the epidemiology – Planning study with larger cohorts (45andUp Study in New South Wales; Auckland regional TestSafe) to better assess statistical impact of MPR
  20. 20. Other users for ChronoMedIt?• It applies to other long-term medications, too – E.g. repeat short-term users of anti-depressants• But who operates in a healthcare setting where they really want clever tools to find more work for them?! – A truly rational healthcare system would be seeking this information – Know of any?
  21. 21. NIHI Evaluation• National Institute for Health Innovation – Based at School of Population Health – Dedicated to innovative use of health IT to deliver better, and more equitable, health outcomes• Engaged in evaluation of the benefits, and areas for improvement, around innovative use of IT in the NZ health sector – Electronic referrals – Home telemonitoring – General practice PMS functionality – Shared care planning
  22. 22. NIHI commission for eReferral evaluation• Conducted from Aug 2010 to Jun 2011• Evaluate electronic referral (eReferral) implementations • Hutt Valley • Northland • Canterbury • Auckland Metro region (entering pilot operation at time of study)• Stakeholder feedback and analysis of IT system records
  23. 23. The Hutt Valley Solution• Implemented in 2007• 30 general practices referring in electronically from Medtech32• 28 services at Hutt Hospital receiving eReferrals into Concerto • 16 service-specific forms, 12 services using a generic form• Electronic management of workflow • GP notified of receipt, triage/decline and FSA • Hospital can see list of referrals w/ triage pending • Any authorised user can see content on Concerto (e.g. from ED)
  24. 24. Hutt Valley Uptake GP referral volume by year (iSoft, Concerto)• 1000 eReferrals per month in 2008; 1200/mo in 2010• 56% of total referrals electronic by 2010• 71% electronic from practices that sent at least one eReferral
  25. 25. Hutt Valley Triage Latency 1 0.9 0.8 0.7 Cumulative frequency 0.6 p07 0.5 e08 p08 0.4 e09 0.3 p09 0.2 0.1 0 0 5 10 15 20 25 30 DaysTime from GP letter date to date priority assigned at Hutt Hospital
  26. 26. Hutt Valley Upsides and Downsides • Upsides • Greater transparency • Faster turnaround • Downsides • IT done once and left • Some persistent usability issues • Slow attachment opening • Difficulty attaching photos • Never revisited form content
  27. 27. The rest of the eReferrals evaluation…• Well, actually there’s a talk on that in the main conference!• One other data mining point, though… – For Canterbury, we did a content analysis of decline messages sent back to GPs – Developed thematic clusters with frequencies and examples – This is a powerful kind of analysis often possible with certain sources of uncoded EHRs • We’re doing more of this with Shared Care Planning • Can do a random sample if you lack the resources to theme your whole collection • Characterising author role/qualifications, message length and message variety are other aspects to this approach
  28. 28. NIHI’s Evaluation Framework for Innovative Health IT• Establish a relevant benefits framework – What’s this technology supposed to be good for?• Always use the transactional EMR data – Uptake, cycle times• Always ask the users and other stakeholders – Action research, grounded theory, theoretical sampling• Disseminate broadly and often• After ~12 evaluation projects in NZ, would like to work with partners overseas
  29. 29. So what have we learned?• The data is there – It can reveal opportunities for process improvement – It can confirm benefits of recent innovations – It can be used integrally with on-going innovations • E.g. to track user uptake and types of uses• It doesn’t necessarily do what we ‘want’ – It can be quite haphazard exactly what the ambient data can/cannot usefully describe • With general practice we were led to MPR and adherence • With eReferrals we couldn’t completely comment on referral letter quality (decline messages give a hint, but quite confounded) – Carefully track the limitations of your data sources • E.g. what is systematically absent
  30. 30. Planning for data mining• Should we try to engineer the situation in advance so that we have the right data? – Sure, but don’t ruin the usability of the system by adding user data entry just for evaluation• Certainly it helps to plan what you’d want to know for evaluation as you do the implementation – Some fields, esp. ‘meta data’ are cost free for end users • E.g. keeping a good audit log (which you should anyway!) – openEHR might be a good answer • Design a good ‘archetype’ for each data element, with good tools and process to back up this data definition (talk to Dr Koray Atalag)
  31. 31. Go forth and analyse!• So go mine your EHRs! – Or hire NIHI to do it for you• Postscript on research ethics approval – Get it – so you can publish to journals and let everybody know the findings – Not too hard for this kind of work • Don’t need names or addresses, encrypt the NHIs, avoid getting precise dates of birth
  32. 32. Questions / further info• Jim Warren, Professor of Health Informatics – jim@cs.auckland.ac.nz – Also, try PubMed on ‘Mabotuwana’ Questions ???! The National Institute for Health Innovation

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