Going paperless: from drowning in paper to drowning in data

1. This presentation has extensive speaker’s notes Going paperless: from drowning in paper to drowning in data Dr Jeremy Rogers MRCGP Medical Informatics Group Department of Computer Science University of Manchester www.cs.man.ac.uk/mig i MedicalInformaticsGroup

2. Outline What Medical Informatics is Why go paperless: the medical malaise drowning in paper Why it’s hard how to drown in data how not to drown in data

3. ‘Theory and practice of using information responsibly in the context of health care’ => Doesn’t necessarily require a computer 40 year old discipline computer scientists clinicians psychologists etc. 3 year degree courses in other countries What is Medical Informatics ?

4. What is Medical Informatics ?(AMIA 99) • Knowledge Representation • terminology • protocols • facts • Information acquisition • voice recognition • structured data entry • Change management • process modelling • human factors • Integrating Information • decision support • image processing

5. Medical Informatics in the UK • Government / NHS • NELH, NHS IA, NHS Net, NHS Number, PRODIGY, PRIMIS • Academic • London, Manchester, Salford, Newcastle, Nottingham, Sheffield • Royal Brompton, Royal Cornwall, Royal Marsden, ERDIP etc. • Organisations • BMIS, PHCSG • BMJ • ABC Guide • ‘Information in Practice’ features in last 4 years • ‘informatics’ hits: 7, 21, 27, 18, 43, 59, 36 • RCSEd Diploma

6. Medical Informatics in the UK General Medical Council: Tomorrow’s Doctors (1993) One area of particularly rapid expansion has been in the application of computers to medicine. The extent to which future advances may revolutionise not only systems of communication, but also care procedures and possibly education itself, is unpredictable, but a working knowledge of modern medical information technology will be essential for the doctor of the future. At the end of the course of undergraduate education the student will have acquired … the other essential skills of medicine, including (a) basic clinical method (history & examination)(b) basic clinical procedures (life support, venepuncture)(c) basic computing skills as applied to medicine

7. Why go paperless - The medical malaise (with thanks to Larry Weed)

8. Medical Malaise: The Symptoms • Medical Knowledge growing ever faster • Super-specialisation • focus on ever smaller parts of the problem • ‘collusion of anonymity’ (Balint 1964) • stereotypical diagnostic and treatment behaviour • holistic approaches regarded with suspicion • Meanwhile.. • patients are not a collection of discrete problems • patients becoming expert on their own condition

9. Medical Malaise: The Symptoms • Huge variations in outcome and cost • standardised mortality rates (119 in Walsall cf 68 at UCLH) • cancer survival rates • more patients killed annually in US through medical accident than on roads [IOM Kohn, Corrigan et al. 2000] • 11% of UK hospital patients harmed by medical error, half were avoidable, one third serious or fatal [BMJ 2001;322:517-519 ( 3 March )] • Empirical observation: evidence for best practice doesn’t translate into altered practice • NHS funding may have masked issue in UK • but won’t for much longer • phenomenon just as obvious in well funded systems

10. Medical Malaise: The Diagnosis Information overload: Drowning in Paper ‘The scarcely tolerable burden of information that is imposed taxes the memory but not the intellect’ (GMC 1993)

11. Medical Malaise:Patient is in denial • Pretence that we can cope • ‘Trust me, I’m a doctor’ • Refuge in good intentions • OK, so I’m not infallible, but I mean well • Defence of clinical freedom • To do what, exactly ?

12. Medical Malaise: Patient is in denial • Shipman, Bristol: simple stats could have detected • Don’t trust me, I’m a doctor • Clinical Audit • Death isn’t the only poor outcome we could measure • is it the only one we have a language for ? • Do I really want to know just how fallible I am ? • How could I possibly do better ? • League Tables • Better not frighten the horses (in case they trample me)

13. Options for Treatment:Tighten Manual Systems • Disseminate evidence • NICE, CHI • Regularise Practice • Health Improvement Plans • Careplan pathways • National Service Frameworks • Formularies and protocols • Monitor performance • GMC • Clinical Audit

14. Options for Treatment:Stop pretending ‘The burden of factual information imposed on students in undergraduate medical curricula should be substantially reduced’ (GMC 1993) Q: who (or what) takes up the burden of knowing the facts ?

15. Options for Treatment:Information Technology • The Computer Based Record • digitised (typed, dictated, OCR, images) • info can be in more than one place at a time • relatively easy to implement • mostly a plumbing and data storage issue • Computer Based Knowledge Repositories • NELH, PHLS

16. The Computer Based EPR:How to Drown in Data • Computers can not read ! • find me all patients with joint disease • which protocol should this patient be on ? • is this patient on an anti-anginal ? • is this cryptosporidium part of a known outbreak ? • how to link record to knowledge repository ? • Electronic ‘fat folder’ worse than physical one • Computer as passive conduit: GIGO

17. Alerts / Reminders Active Problems Current Medication Asthma checkBPFlu Vaccine Asthma SalbutamolHydrocortisone C/o Low Mood Declined antidepressant PEFR BP Drowning In Data EPR - Dr Kildare - 26th Oct 2000 John Doe 36 yrs Engineer Married, 2 children Results Appt Letters This Visit Encounters Code Notes Action 12.10.96 GP Surgery: Dr Kildare13.10.96 GP Surgery: Dr Kildare 20.10.96 GP Surgery: Dr Finlay 24.10.96 GP Surgery: Dr Kildare 10.11.96 GP Surgery: Dr Kildare 12.11.96 Radiology: reported film 27.11.96 GP Surgery: Dr Kildare 07.03.97 GP Surgery: Dr Kildare 19.04.97 GP Surgery: Dr Kildare 01.06.97 GP Surgery: Dr Kildare 18.10.97 GP Surgery: GP Registrar 03.03.98 GP Surgery: Dr Kildare 04.03.98 Path Links: WCC result30.06.98 GP Surgery: Dr Kildare 15.09.98 GP Surgery: Dr Kildare 05.11.98 GP Surgery: GP Registrar 03.01.99 GP Surgery: Dr Kildare 17.02.99 GP Surgery: Nurse Duffy21.03.99 GP Surgery: Dr Kildare 07.10.99 GP Surgery: GP Registrar 26.01.00 GP Surgery: Nurse Duffy Salbutamol inh 2 puff qds 1op PEFR 550 l /min Asthma Chest NAD. No Problems. Influvac im BN #035679A4 WCC

18. Alerts / Reminders Active Problems Current Medication Asthma checkBPFlu Vaccine Asthma SalbutamolHydrocortisone C/o Low Mood Declined antidepressant PEFR BP Drowning In Data EPR - Dr Kildare - 26th Oct 2000 John Doe 36 yrs Engineer Married, 2 children Results Appt Letters This Visit Encounters Code Notes Action 12.10.96 Coryza: chest NAD: reassure13.10.96 URTI: wheezy: amoxycillin20.10.96 Anxiety: child admitted to H: reassure24.10.96 PEFR : 300 :10.11.96 PEFR : 400: CXR requested12.11.96 CXR Basal Consolidation: : erythromycin27.11.96 : Chest clear :07.03.97 Depression: death in family: paroxetine19.04.97 Gastoenteritis: : reassure01.06.97 : : rpt Rx paroxetine18.10.97 Sick note : :03.03.98 Viral URTI: PEFR 350: salbutamol04.03.98 WCC NAD : :30.06.98 PMR report : BP, ECG NAD :15.09.98 Eczema : : hydrocortisone05.11.98 Depression : : paroxetine03.01.99 Fibrositis: trigger spot lwr back: ibuprofen17.02.99 Allergic Asthma: PEFR 300: salbutamol21.03.99 Chest Inf: L base: erythromycin07.10.99 Med4: anxious :26.01.00 Asthma Review: :Repeat Rx Salbutamol Salbutamol inh 2 puff qds 1op PEFR 550 l /min Asthma Chest NAD. No Problems. Influvac im BN #035679A4 WCC

19. Drowning In Data:Data analysis and Coding Chaos Sore Throat Symptom 0.6 117 Visual Accuity 0.4 644 ECG General 2.2 300 Ovary/Broad Ligament Op 7.8 809 Specific Viral Infections 1.4 556 Alcohol Consumption 0 106 H/O Resp Disease 0 26 Full Blood Count 0 838

20. How not to drown in data:1. Ask what the record is for • Post-hoc documentation for medico-legal protection ? • Aide-memoir of treatment plan for author ? • Aide-memoir of treatment plan for team ? • Objective record of patient state ? • Input for decision support ? • Part of population data-set • for resource planning ? • for care quality analysis ? • for data mining ? ?

21. How not to drown in data: 2. Examine The Typical Medical Record • Much is only implied • Ambiguity and imprecision is rife • gastrointestinal disturbance: what does this mean ? • Significant findings often not recorded • focus on significant positive findings is usually a good strategy • except when it isn’t • symptomatic of traditional diagnostic process • common things are common • rush to stereotype patients • Problem, Plan and checkpoints not stated or linked

22. How not to drown in data: 3. Conclude: current MR inadequate • Written MR does not support all tasks even when you attempt to perform them manually • therefore computers have no chance • Need to tighten up EPR capture, and tune it to intended tasks • need much more explicit info • EPR as active resource, not historical document • Input and Output must be developed together

23. Options for Treatment:Computer as active agent • Computerised EPR • digitised (but not typed, dictated, OCR, images) • because computers can not read • info can be in more than one place at a time • computer must also ‘understand’ content • recommend protocols • measure quality of care • find patient groups • filter record • data mine • otherwise it can’t help

24. can’t read have no context to resolve ambiguity need complete data require precision are capable of coherent reasoning too complex for a human to comprehend (or debug, or trust) never tire prefer natural language are often non-specific are rarely complete prefer vagueness think heuristically are easily bored Why its hard Computers Humans

25. Why its urgent: the new medicine • The new genetics • idiosyncratic patient responses are genetically determined • mechanisms not understood (or, even, understandable) • Solution: • data mine to associate genotype with phenotype • Problem: • how to describe phenotype consistently and completely • when you don’t know what you’re looking for • patient record becomes primary diagnostic tool

26. Conclusion • Already drowning in paper • but reluctant to admit it • Risk drowning in data • computer based EPR not a solution • need computer as active partner, not passive conduit • Information explosion imminent • either we must recruit computers • or we choose not to use the information • but people will suffer