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!