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Re-engineering Computational Research to Improve Medical Care


NY state (30,000 cases) and Colorado/Utah (15,000 cases) studies of randomly ... 'A Thousand Doctors, A Thousand Opinions' - French proverb ' ... – PowerPoint PPT presentation

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Title: Re-engineering Computational Research to Improve Medical Care

Re-engineering Computational Research to Improve
Medical Care
  • Peter Szolovits
  • Prof. of EECS HST
  • September 24, 2003

Re-engineering Computational Research to Improve
Medical Care
How to Help Stop Screw-ups in Medical Care
  • Peter Szolovits
  • Prof. of EECS HST
  • September 23, 2003

Re-engineering Computational Research to Improve
Medical Care
How to Help Stop Screw-ups in Medical Care
What to do when success fails
  • Peter Szolovits
  • Prof. of EECS HST
  • September 23, 2003

  • Medical Informatics vision 30 years ago
  • AI Contributions
  • Lack of impact
  • Current medical hot topic quality improvement
  • New needs/research opportunities

Medicine and the ComputerThe Promise and
Problems of Change--W. B. Schwartz, NEJM 1970
  • Ever-expanding body of knowledge, limited memory
  • Physician shortage and maldistribution
  • Computer as an intellectual, deductive tool
  • Improve medical care 2nd opinion, error monitor
  • Separate practice from memorization
  • Allow time for human contact different
    personalities in medicine the healing arts

Practice of Medicine is
  • Art
  • Learning by apprenticeship
  • Individual variation creativity
  • Science
  • Baconian hypothetico-deductive reasoning
  • Engineering
  • Systems to reduce failure, optimize care

Consider the following
  • Middle-aged woman complains of severe pedal edema
    (foot swelling), which is neither painful or
    erythematous (red), symmetric (both feet),
    pitting, lasting for weeks.
  • She drinks heavily, has jaundice, painful
    hepatomegaly (enlarged liver),
  • 50 other facts from lab, physical exam, etc.
  • Conclusions Cirrhosis, hepatitis and portal
    hypertension possible constrictive pericarditis

Reasoning Tasks
  • Diagnosis
  • Prognosis
  • Therapy
  • Management

Medicine provided challenges for AI, and AI
  • Probabilities
  • ?Bayes nets, qualitative probabilistic networks,
    partially-observable semi-Markov decision
  • Temporal patterns and uncertainty
  • ?Temporal belief nets, temporal constraints,
  • Spatial localization
  • ? vision, not reasoning
  • Causality, physiology and pathophysiology
  • ?Feedback models, multi-level models,
  • Combinatorial explosion of hypotheses
  • ?Symptom clustering, theories of abduction
  • Modularity
  • ? Rule-based systems,

  • Long, Reasoning about State from Causation and
    Time in a Medical Domain, AAAI 83

So why arent computers in your medical life
  • 7-minute doctors visit
  • We forgot about , workflow, usability,
  • Medical records still primitive
  • We forgot about needing data
  • Paper, thus inaccessible
  • English text, thus incomprehensible
  • Unsuccessful investments in health IT
  • We dont know how to turn quality?

Current Challenges/Opportunities
  • 44-98,000/year die in hospitals from medical
    errors, at least ½ preventable (IOM)
  • Cost of health care growing without bounds
  • GM spends more on health than steel
  • Aging population ? chronic health care

IOM To Err is Human report
  • NY state (30,000 cases) and Colorado/Utah (15,000
    cases) studies of randomly selected hospital
    discharges Adverse events occur in 2.9-3.7 of
  • 50 minor, temporary injuries
  • 7-14 result in death
  • 2.6 result in permanent disabling injury
  • 53-58 preventable
  • 28 due to negligence (failed to meet reasonable
    standard of care)

Process Errors
  • Majority of errors do not result from individual
    recklessness, but from flaws in health system
    organization (or lack of organization).
  • Failures of information management are common
  • illegible writing in medical records
  • lack of integration of clinical information
  • inaccessibility of records
  • lack of automated allergy and drug interaction

Suboptimal performance everywhere
of ideal candidates who received Rx for AMI by
hospital type
JAMA, Sept 2000
  • In the absence of facts, opinion prevails (85
    of healthcare) - T. Clemmer, M.D.
  • A Thousand Doctors, A Thousand Opinions -
    French proverb
  • We practice healthcare as if we never wrote
    anything down. It is a spectacle of fragmented
    intention. - L. Weed, M.D.
  • Healthcare is labor intensive and information
    bereft - B. Hochstadt, M.D.
  • Until clinicians are paid by the word and not
    by the procedure, medical records will remain
    unsupported, unmanageable and of limited value.
  • - I. Kohane, MD, PhD

Computerized Clinical Decision Support
  • Reference
  • Bates DW et al. A randomized trial of
    computer-based intervention to reduce utilization
    of redundant laboratory tests. Am J Med 1999
  • Aim
  • To determine the impact of giving physicians
    computerized reminders about apparently redundant
    laboratory tests.
  • Methods
  • Randomized trial of giving physicians immediate
    feedback upon ordering of tests via computer
    order entry system vs. no feedback

Computerized Clinical Decision Supportnecessary
but not sufficient to overcome opinion
  • Results
  • 939 apparently redundant lab tests among 77,609
    ordered on 5700 intervention Pts and 5886 control
  • In intervention group, 300 of 437 tests (69)
    were cancelled in response to alerts. Of 137
    overrides, only 41 justified on chart review.
  • Nevertheless
  • In control group, 51 of ordered redundant tests
    were performed vs. 27 in intervention group.

Short-term solutions
  • If computers can capture even some of what goes
    on, they can help avoid errors, assure
  • One-rule expert systems
  • If youre about to prescribe a lethal dose of
    medicine, dont!
  • Guidelines routine methods for routine care
  • E.g., remember x-ray after appendectomy
  • Ready surgical team when doing balloon
  • Workflow integration
  • E.g., persistent paging for critical situation

The communication space
  • is the largest part of the health systems
    information space
  • contains a substantial proportion of the health
    system information pathology
  • is largely ignored in our informatics thinking
  • is where most data is acquired and presented

How big is the communication space?
  • Covell et al. (1985) 50 info requests are to
    colleagues, 26 personal notes
  • Tang et al (1996) talk is 60 in clinic
  • Coiera and Tombs (1996,1998) 100 of non-patient
    record information
  • Safran et al. (1998) 50 face to face, EMR
    10, e/v-mail and paper remainder

What happens in the communication space?
  • Wilson et al. (1995) communication errors
    commonest cause of in-hospital disability/death
    in 14,000 patient series
  • Bhasale et al. (1998) contributes to 50
    adverse events in primary care
  • Coiera and Tombs (1998) interrupt-driven
    workplace, poor systems and poor practice

ER communication study
  • Medical Subject 4
  • 3 hrs 15 min observation
  • 86 time in talk
  • 31 time taken up with 28 interruptions
  • 25 multi-tasking with 2 or more conversations
  • 87 face to face, phone, pager
  • 13 computer, forms, patient notes

Implications (Coiera)
  • Clinicians already seem to receive too many
    messages resulting in
  • interruption of tasks
  • fragmentation of time, potentially leading to
  • potential for forgetting, resulting in errors

Communication options
  • We can introduce new
  • Channels eg v-mail
  • Types of message eg alert
  • Communication policies eg prohibit sending an
    e-mail organisation-wide
  • Communication services eg role-based call
  • Agents creating or receiving messages eg web-bots
    for info retrieval
  • Common ground between agents eg train team members

Communication channels
  • Synchronous
  • face to face, pager, phone
  • generate an interrupt to receiver
  • Asynchronous
  • post-it notes, e-mail, v-mail
  • receiver elects moment to read

Hijacking Administrative Computing
  • Referrals and Authorization major pain

Oct. 1997
Feb. 1998
Apr. 1998
Oct. 1998
Nov. 1999
Dec. 1999
Feb. 2000
Jun. 2000
Jul. 2000
Jan. 2001
Apr. 2001
Summer 2001
Sep. 2001
Initial discussions
Pilot commences
Incorporation as NEHEN LLC
Seventhand eighthmembers join
Two affiliates join
Ninth and tenth members join
Members 12-14 join
Commitment in principle
Eligibility live at founding members
Specialtyreferrals live
Claim statusinquiry pilotcommences
Eleventh member joins
Referral auth and inquiry pilot
  • Expanding membership interest
  • Additional integrated delivery networks
  • Smaller payers
  • Smaller community/specialty hospitals
  • Multi-specialty practices and their business
    partners (i.e., third-party billing companies,
    practice management software vendors)
  • State agencies and task forces
  • Current membership represents
  • 40 Hospitals
  • Over 7,500 licensed beds
  • Over 5,000 affiliated physicians
  • 2 million covered lives (not including Medicare
    and Medicaid)

NEHENlite and Integrated Options
  • Intranet version NEHENLite
  • Use when integrated EDI is unavailable in core
  • Supports ad hoc business processes like
  • Provides means of acquiring early experience with
    process change (in parallel with core system
  • Extends functionality to outlying practices and
    business processing areas
  • Integrated version IDX, Meditech, Eclipsys,
  • Preferred method for workflow improvement in core
    business processes
  • Avoids double-keying / re-keying
  • Eases distribution and reduces training
    requirements for registration clerks, billing
    clerks, etc.

Real-Time and Batch Alternatives
  • Batch submission and review
  • Eligibility
  • Submit all appointments scheduled for the next
    day and work the 20-30 of problem cases
    (patient not found, wrong date of birth, patient
    inactive, etc.)
  • Can be used in conjunction with and in addition
    to real-time request at point of registration or
    scheduling (i.e., no-cost double-checking)
  • Claim Status Inquiry
  • Submit inquiries for all claims more than 10 days
    old and review the results
  • Interactive submission and review
  • Eligibility
  • At point of registration or scheduling (or both)
  • Referral Submission
  • Complete online form rather than paper form and
    submit directly to plan
  • Response usually not required real-time (can be
  • Claim Status Inquiry
  • Efficiency tool for billing and collections

NEHENLite Specialty Referral Submission
NEHENLite Claim Status Inquiry
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  • Add clinical details to referral transactions
  • Integrate with patients own records
  • Research foci
  • Scale
  • Confidentiality
  • Usability

Current Opportunities
  • Involve the patient
  • Most concerned, knowledgeable, representative,
    motivated, and inexpensive
  • Life-long active personalized secure health
    information system (Guardian Angel)
  • Persistent over lifetime (PING project)
  • communication channel among patient, provider,
  • expert guidance, education
  • Home health
  • Non-intrusive intensive care

DCCT Diabetes Control and Complications Trial
  • Lowering blood glucose reduces risk
  • Eye disease 76 reduced risk
  • Kidney disease 50 reduced risk
  • Nerve disease 60 reduced risk
  • Elements of Intensive Management in the DCCT
  • Testing blood glucose levels 4 or more times a
  • Four daily insulin injections or use of an
    insulin pump
  • Adjustment of insulin doses according to food
    intake and exercise
  • A diet and exercise plan
  • Monthly visits to a health care team composed of
    a physician, nurse educator, dietitian, and
    behavioral therapist.
  • New England Journal of Medicine, 329(14),
    September 30, 1993.

Home Care for Chronic Illness
  • Who else?
  • Treatment titration
  • E.g., heart disease, renal dialysis
  • Compliance nagging
  • Instrumentation walking ICU

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  • Genomic Medicine
  • Human phenome project to learn clinical
    correlates of gene expression
  • Customized interventions/drugs
  • Customized decision making
  • But, how to get the clinical data?

Clinical data
Autonomous Witness
  • Natural language (and speech) understanding
  • Knowledge representation standards for what is
  • Perceptually aware systems
  • See, hear, record and present data
  • Real autonomous health agent
  • Dont forget communication!

Automated messages
  • Notification - that an event has occurred
  • Alert (push)- draws attention to an event
    determined to be important eg abnormal test
    result, failure to act
  • Retrieve (pull) - return with requested data
  • Acknowledgment (push or pull) - that a request
    has been seen, read, or acted upon

Notification systems
  • Channel
  • typically asynchronous eg e-mail, pager, fax
  • synchronous modes feasible
  • Message
  • existing messages eg lab alerts
  • new messages eg task acknowledgment

Effects of notification systems
  • Channel effect shift existing events from
    synchronous to asynchronous domain, reducing
  • Message effect generate new types of events in
    the asynchronous domain, increasing message load,
    demanding time, and creating a filtering problem
  • potential to either harm or help

Interpretation 1 - communication is replaceable
  • Problem is size and nature of communication space
    i.e. need to shift to formal information
  • Implies a 11 hypothesis i.e. communication tasks
    replaceable with computational tasks
  • Strong hypothesis (100 replacement) a matter of

Interpretation 2 - the necessity of communication
  • Size of communication space is natural and
  • Communication tasks are different
  • Reflects informal and interactive nature of most
  • Problem lies with the way we support those tasks,
    either ignoring them or shoe-horning them into
    formal IT solutions

Choosing Channels
  • Highly grounded conversations need
  • low bandwidth
  • frequent small updates
  • Poorly grounded conversations need
  • high bandwidth
  • prolonged initial priming exchange
  • Building common ground should be specifically
    supported e.g. shared information objects,
    images, designs

  • Students Colleagues
  • Esp. Zak Kohane
  • Collaborators
  • Childrens Hosp.
  • Tufts/NEMC
  • Harvard Med
  • BU

Finally, back to the fun reasoning!
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