Re-engineering Computational Research to Improve Medical Care

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Re-engineering Computational Research to Improve
Medical Care

• Peter Szolovits
• Prof. of EECS HST
• CSAIL
• September 24, 2003

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Re-engineering Computational Research to Improve
Medical Care
How to Help Stop Screw-ups in Medical Care

• Peter Szolovits
• Prof. of EECS HST
• CSAIL
• September 23, 2003

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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
• CSAIL
• September 23, 2003

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Outline

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

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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

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Practice of Medicine is

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

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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

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Reasoning Tasks

• Diagnosis
• Prognosis
• Therapy
• Management

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Medicine provided challenges for AI, and AI
responded

• Probabilities
• ?Bayes nets, qualitative probabilistic networks,
  partially-observable semi-Markov decision
  processes,
• 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,

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DiagnosticReconstruction

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

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So why arent computers in your medical life
today?

• 7-minute doctors visit
• We forgot about , workflow, usability,
  technophobia,
• 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?

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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

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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
  hospitalizations
• 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)

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Problems
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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
  systems
• inaccessibility of records
• lack of automated allergy and drug interaction
  checking

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Suboptimal performance everywhere
of ideal candidates who received Rx for AMI by
hospital type
JAMA, Sept 2000
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Why?

• 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

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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
  Feb106(2)144-50
• 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

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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
  Pts.
• 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.
  (Plt.001)

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Short-term solutions

• If computers can capture even some of what goes
  on, they can help avoid errors, assure
  consistency
• 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
  angioplasty
• Workflow integration
• E.g., persistent paging for critical situation

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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

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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

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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

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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

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Implications (Coiera)

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

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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
  forwarding
• Agents creating or receiving messages eg web-bots
  for info retrieval
• Common ground between agents eg train team members

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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

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Hijacking Administrative Computing

• Referrals and Authorization major pain

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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
Sixthmemberjoins
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)

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NEHENlite and Integrated Options

• Intranet version NEHENLite
• Use when integrated EDI is unavailable in core
  system
• Supports ad hoc business processes like
  collections
• Provides means of acquiring early experience with
  process change (in parallel with core system
  integration)
• Extends functionality to outlying practices and
  business processing areas

• Integrated version IDX, Meditech, Eclipsys,
  others
• 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.

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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
  asynchronous)
• Claim Status Inquiry
• Efficiency tool for billing and collections

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NEHENLite Specialty Referral Submission
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NEHENLite Claim Status Inquiry
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nMesh

• Add clinical details to referral transactions
• Integrate with patients own records
• Research foci
• Scale
• Confidentiality
• Usability

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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,
  community
• expert guidance, education
• Home health
• Non-intrusive intensive care

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DCCT Diabetes Control and Complications Trial
(83-93)

• 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
  day
• 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.

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Home Care for Chronic Illness

• Who else?
• Treatment titration
• E.g., heart disease, renal dialysis
• Compliance nagging
• Instrumentation walking ICU

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Long-term

• 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
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Autonomous Witness

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

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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

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Notification systems

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

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Effects of notification systems

• Channel effect shift existing events from
  synchronous to asynchronous domain, reducing
  interruption
• 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

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Interpretation 1 - communication is replaceable

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

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Interpretation 2 - the necessity of communication

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

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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

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Thanks

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

Finally, back to the fun reasoning!
http//medg.lcs.mit.edu