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Going paperless from drowning in paper to drowning in data

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					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




                                             MedicalInformaticsGroup
             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
 What is Medical Informatics ?
‘Theory and practice of     • 40 year old discipline
    using information          – computer scientists
    responsibly in the         – clinicians
  context of health care’      – psychologists etc.


=> Doesn’t necessarily      • 3 year degree courses
   require a computer         in other countries
 What is Medical Informatics ?
                     (AMIA 99)

• Knowledge                 • Change management
  Representation              – process modelling
  – terminology               – human factors
  – protocols
  – facts

• Information               • Integrating
  acquisition                 Information
  – voice recognition         – decision support
  – structured data entry     – image processing
  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
   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
 Why go paperless -
 The medical malaise


(with thanks to Larry Weed)
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
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
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)
            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 ?
               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)
         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
         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 ?
       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
       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
                                 Drowning In Data
 EPR - Dr Kildare - 26th Oct 2000

                John Doe              Active Problems      Current Medication             Alerts / Reminders
                36 yrs                Asthma               Salbutamol                     Asthma check
                Engineer                                   Hydrocortisone                 BP
                Married, 2 children     Letters       Results      Appt                   Flu Vaccine
Encounters                            This Visit
 12.10.96 GP Surgery: Dr Kildare       Code             Notes                       Action
 13.10.96 GP Surgery: Dr Kildare       PEFR             550 l /min                  Salbutamol inh 2 puff qds 1op
 20.10.96 GP Surgery: Dr Finlay        Asthma           Chest NAD. No Problems.     Influvac im BN #035679A4
 24.10.96 GP Surgery: Dr Kildare       C/o Low Mood     Declined antidepressant
 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 result
 30.06.98 GP Surgery: Dr Kildare
                                                          BP                      PEFR                      WCC
 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 Duffy
 21.03.99 GP Surgery: Dr Kildare
 07.10.99 GP Surgery: GP Registrar
 26.01.00 GP Surgery: Nurse Duffy
                                    Drowning In Data
 EPR - Dr Kildare - 26th Oct 2000

                 John Doe                                Active Problems      Current Medication             Alerts / Reminders
                 36 yrs                                  Asthma               Salbutamol                     Asthma check
                 Engineer                                                     Hydrocortisone                 BP
                 Married, 2 children                       Letters       Results      Appt                   Flu Vaccine
Encounters                                               This Visit
 12.10.96 Coryza: chest NAD: reassure                     Code             Notes                       Action
 13.10.96 URTI: wheezy: amoxycillin                       PEFR             550 l /min                  Salbutamol inh 2 puff qds 1op
 20.10.96 Anxiety: child admitted to H: reassure          Asthma           Chest NAD. No Problems.     Influvac im BN #035679A4
 24.10.96 PEFR : 300 :                                    C/o Low Mood     Declined antidepressant
 10.11.96 PEFR : 400: CXR requested
 12.11.96 CXR Basal Consolidation: : erythromycin
 27.11.96 : Chest clear :
 07.03.97 Depression: death in family: paroxetine
 19.04.97 Gastoenteritis: : reassure
 01.06.97 : : rpt Rx paroxetine
 18.10.97 Sick note : :
 03.03.98 Viral URTI: PEFR 350: salbutamol
 04.03.98 WCC NAD : :
 30.06.98 PMR report : BP, ECG NAD :
                                                                             BP                      PEFR                      WCC
 15.09.98 Eczema : : hydrocortisone
 05.11.98 Depression : : paroxetine
 03.01.99 Fibrositis: trigger spot lwr back: ibuprofen
 17.02.99 Allergic Asthma: PEFR 300: salbutamol
 21.03.99 Chest Inf: L base: erythromycin
 07.10.99 Med4: anxious :
 26.01.00 Asthma Review: :Repeat Rx Salbutamol
       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
    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 ?
       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
      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
       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
                 Why its hard
         Computers                   Humans
• can’t read                   • prefer natural language
• have no context to resolve   • are often non-specific
  ambiguity
• need complete data           • are rarely complete
• require precision            • prefer vagueness
• are capable of coherent      • think heuristically
  reasoning too complex for
  a human to comprehend
  (or debug, or trust)
• never tire                   • are easily bored
               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
                     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

				
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posted:5/23/2011
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