re-engineering computational research to improve medical care peter szolovits prof. of eecs &...
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Re-engineering Computational Research to Improve Medical Care
Peter SzolovitsProf. of EECS & HST
CSAILSeptember 24, 2003
Re-engineering Computational Research to Improve Medical Care
Peter Szolovits
Prof. of EECS & HST
CSAIL
September 23, 2003
How to Help Stop Screw-ups in Medical Care
Re-engineering Computational Research to Improve Medical Care
Peter SzolovitsProf. of EECS & HST
CSAILSeptember 23, 2003
How to Help Stop Screw-ups in Medical Care
What to do when success fails
Outline
• Medical Informatics vision 30 years ago
• AI Contributions
• Lack of impact
• Current medical hot topic: quality improvement
• New needs/research opportunities
“Medicine and the Computer:The 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 observe
plan
decidepatient
data information
diagnosistherapy
initial presentation
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, …
DiagnosticReconstruction
DiagnosticReconstruction
weak heart
heart failure
digitalis effect
retain
losediuretic effect
high
low
edemafluid therapy
water blood volume
low cardiac output
definite cause
possible cause
possible correction (not all shown)
Long, Reasoning about State from Causation and Time in a Medical Domain, AAAI 83
0345678910now
futurepast
presentnorm high ? norm low
retain ? loss ?low
presentpresent
presentpresent
edema blood volume water cardiac output heart failure weak heart diuretic effect diuretic
12
So why aren’t computers in your medical life today?
• 7-minute doctor’s 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 don’t 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 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)
Problems
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
Suboptimal performance everywhere
Intervention Community Academic
ASA 80% 90%
ACE 58% 62%
Beta Blockers 36% 48%
Reperfusion 55% 60%
% of ideal candidates who received Rx for AMI by hospital type% of ideal candidates who received Rx for AMI by hospital type
JAMA, Sept 2000
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 clinician’s 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 Feb;106(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
Computerized Clinical Decision Support:necessary 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. (P<.001)
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 you’re about to prescribe a lethal dose of medicine, don’t!
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
The communication space
• is the largest part of the health system’s 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
inefficiency– 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
forwarding– 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 painNEHEN Membership, Oct. 2001
Additional Members
Non-Member Payers with Secondary Connectivity Solutions
BC/BS of Massachusetts
Massachusetts Medicaid
Medicare
Contract Affiliates
The New England Healthcare EDI Network (NEHEN LLC) is a consortium of payers and providers in Massachusetts.
Oct. 1997
Initial discussions
Feb. 1998
Commitment in
principle
Apr. 1998
Pilot commences
Oct. 1998
Eligibility live at founding members
Nov. 1999
Incorporation as
NEHEN LLC
Dec. 1999
Sixthmember
joins
Feb. 2000
Seventhand eighth
members join
Jun. 2000
Specialtyreferrals
live
Jul. 2000
Two affiliates
join
Jan. 2001
• Current membership represents
– 40 Hospitals
– Over 7,500 licensed beds
– Over 5,000 affiliated physicians
– ~2 million covered lives (not including Medicare and Medicaid)
• 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
Claim statusinquiry pilotcommences
Apr. 2001
Ninth and tenth
members join
Eleventh member
joins
Summer 2001
Referral auth and
inquiry pilot
Sep. 2001
Members 12-14 join
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
NEHENlite and Integrated Options
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.
Real-Time and Batch Alternatives
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
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
NEHENLite – Specialty Referral Submission
NEHENLite – Claim Status Inquiry
nMesh
• Add clinical details to referral transactions
• Integrate with patient’s 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,
community– expert guidance, education
• Home health– Non-intrusive “intensive care”
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.
Home Care for Chronic Illness
• Who else?
• Treatment titration– E.g., heart disease, renal dialysis
• Compliance nagging
• Instrumentation: “walking ICU”
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
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
• Don’t 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
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
Interpretation 1 - communication is replaceable
• Problem is size and nature of communication space i.e. need to shift to formal information transactions
• Implies a 1:1 hypothesis i.e. communication tasks replaceable with computational tasks
• Strong hypothesis (100% replacement) a matter of debate
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
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
Thanks
http://medg.lcs.mit.edu
– Students & Colleagues• Esp. Zak Kohane
– Collaborators• Children’s Hosp.• Tufts/NEMC• Harvard Med• BU
Finally, back to the fun: reasoning!