EJOI1PUTEFS ill IlEIJICIFlEis limited. Current systems are still largely characterized by auto- use of cancer treatment protocols will use the clinical data basemation of manual procedures with few decision-making or knowl- to maximize the amount of treatment, to monitor levels for toxici-edge-sharing features, and use of these systems is peripheral to ty, to check effectiveness of treatment, and to establish a re-the practice of medicine. search data base to evaluate the value of treatment. Costs can beThe future of medical computing will be influenced by several further reduced by using case mix analysis to define the most

major factors. Computers are the most rapidly developing inno- economical, effective treatment for a given problem. Overusevation the world has known. Personal computers have quickly and unnecessary use of laboratory testing will be better con-moved from toys to serious, powerful machines, offering in small trolled by reporting and computer-controlled algorithms. Localsize and low cost the computational power of the computer gi- and regional networks will permit sharing data between facilitiesants of less than a decade ago. The developments of networking and physicians, thereby reducing testing, improving decisionand distributed systems make accessible resources almost un- making, and coordinating treatment.limited. New techniques of data storage and data collection and The future of medical computing is promising. Hardware andpresentation, including color, graphics, and voice will overcome system software technology are adequate. We need merely to letmost resistance to use. The physician of less than half a century our imaginations expand as we develop the applications. Tomor-ago had to be concerned with only a few laboratory tests, treat- row's physician can have the world's medical experts as conver-ments, and possible diagnoses. The exponential explosion of sational partners in the treatment of patients.lZknowledge has made it impossible for today's physician to retainan appropriate knowledge base much less keep it up-to-date. References

New developmentsinnatural language text processing with con- 1. Lindberg, DAB: The Growth of Medical Information Systems in the United States.New developments in natural language text processing with con- Lexington, Maine: Lexington Press, 1979.

versational query capabilities will permit rapid disseminaton of 2. Kak, AC: computerized Tomography with X-Ray, Emission, and Ultrasound

new medical data in an immediately useable form.8 The use of ar- Sources. IEEE Proc. 67:1245-1272, 1979.tificil intlligeceprgrams will erhap havethe mst sinifi- 3. Barnett, GD, NS Justice, et al: COSTAR - A Computer-Based Medical Intormation

tificial intell1igence programs9 w ill1 perhaps have the most s ig9nifi System for Ambulatory Care. IEEE Proc. 67:1226.1237, 1979.cant impact on the practice of medicine by the end of this de- 4. Blume, RL: Discovery, Confirmation, and Incorporation of Causal Relationshipscade. These Al programs depend largely on decision-making from a Large Time-Oriented Clinical Data Base: The RX Project. Comput Biomed Res.

strategies based on judgment and experience which are express- 15:164-187, 1982.5. Hammond, WE, WW Stead, MJ Straube, and FR Jelovsek: Functional Characteris-

ed symbolically to capture the knowledge and problem-solving tics of a Computerized Medical Record. Meth Inform Med. 19:157-162, 1980.abilities of medical experts. Clinical data bases will be acquired 6. Stead, WW, and WE Hammond: How to Realize Labor Savings with a Compute-

rized Medical Record. Proc 4th Annual Sump Computers Applic Med Care. 1200-1205,routinely and used to predtct outcomes, natural history, and re- 1980.sponse to various modalities of treatment through comparison of 7. McDonald, CJ: Protocol-Based Computer Reminders, the Quality of Care and the

new patients with those already on file.10 The third factor, the Non-Perfectability of Man. New EngI J Med. 295:1351-1355,1976.cost of delverohelthcae-,wilicresetherataw8. Shortliffe, EH, BG Buchanan, and EA Feigenbaum: Knowledge Engineering for

cost of delivery of health care; will Increase the rate at which Medical Decision Making: A Review of Computer-Based Clinical Decision Aids. IEEEcomputers are assimulated into the health care process. Im- Proc. 67:1207-1224,1979.proved diagnostic accuracy and computerized treatment proto- 9. Walker, DE, and JE Hobbs: Natural Language Access to Medical Text. Proc 5th An-cols will reduce cost thro-ugh qutcker and more effective treat- nualSump Computers Applic Med Care. 269-279, '981.

10. Rosati, RA, JF McNeer, CF Starmer, et al: A New Information System for Medical

ments, and futile treatments will be reduced. For example, the Practice. Arch Intern Med. 135:1017-1024, 1975.

Barriers there, but can be overcomeBy EDWARD SHORTLIFFE, M.D., Ph.D

Stanford University, California

uch of a physician's training deals with how to make opti- systems have appeared. Critical reviews have provided usefulmal, informed clinical decisions. Yet, the challenges of analyses of many of these challenges.12 Recent strides in thediagnosis and treatment planning have become partic- field,3 however, have given those working in it reason to be opti-

ularly severe as medical knowledge has grown and the need for mistic about the successful application of technology in the next

specialization has increased. Why, then, has it been so difficult decade.to incorporate decision support systems successfully into clini- Investigators must contend with a variety of conflicting forces.cal settings despite the computer's vast storage capacity and On the one hand, a profession that has heard the potential ofcomputational capabilities? clinical computing extolled for more than 10 years but has yet to

In the past two decades, several significant barriers to effec- see a widely accepted decision support system now says "showtive implementation of computer-based medical decision-making me." On the other hand, there are indications that there is an in-

creased acknowledgement that clinical decision-making re-search can validly contribute to medical practice. For example,

Dr. Shortliffe is an Assistant Professor of Medicine and Comput- significant clinical changes have resulted from theoretical worker Science at Stanford University, Palo Alto, California. He re- in clinical decision analysis (e.g., the recent American Cancerceived the A.B. in applied mathematics from Harvard University Society recommendations in mammography and PAP smearin 1970. He received the Ph.D. and M.D. from Stanford, the former screening). An ambitious well-received journal in the field hasin 1975 in medical information sciences and the latter in 1976. been developed4. Studies of physicians' attitudes5 also show a

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growth in curiosity about computers and a heightened faith in encourage physicians to interact with computer programs weretheir potential. The emergence of doctors with home computers conducted. These included systems using light pens15 or touchand customized office systems and the success of educational screens16 and decision support programs integrated into large-programs designed to introduce physicians to computers for scale hospital information systems.17 These efforts and othersboth business and clinical applications further demonstrate the demonstrate that physicians will learn to use computers and ac-environment is changing. cept their role, if the benefits of the technology outweigh theMedical decision-making research in the 1960s emphasized us- costs of learning to use the devices and the costs of integrating

ing a computer to deal with probabilistic information, to recog- them into one's normal routine.nize patterns using numerical techniques, to model physiolog- However, a litany of recent accomplishments also emphasizesical processes that were amenable to mathematical simulation or the significant problems still remaining. Many of the experi-to encode algorithmic approaches to routine clinical chores. The ments cited above are only the first steps in developing clinicallyfield was in its first decade as an identifiable research area, and useful tools. As is true with any developing science, short-termthe emphasis was on how to get machines to make accurate and solutions to given problems tend to lead to a new understandingreliable decisions. Formal statistical approaches that had been of the nature of the remaining problems and help to define the re-impractical before computers were, quite naturally, the first tech- search directions for the future. Results suggest that the follow-niques the physicians and engineers tried as they began to ap- ing problems are some of those that may require attention in thepreciate the computer's potential as a clinical tool. decade ahead:

In the 1970s, the research direction shifted. Investigators in- 1) additional psychological studies, similar in motivation tocreasingly realized that there were several key problems that es- some of the pioneering studies of the 1970s,18,19 which will pro-caped attention if the research focused solely on the devel- vide new insights into optimal methods forSimulating expert de-opment of good decision-making techniques. These include: cision-making performance and may suggest novel approaches

1) data acquisition - how to acquire, encode, and control for to the organization of knowledge and its interaction with probabi-variations in the descriptors that define patients and popula- listic information;tions; 2) improved techniques for representing and using causal and

2) knowledge acquisition and representation - how to acquire mechanistic relationships (because expert decision-making be-and encode the kinds of judgmental perceptions and the com- havior depends in large part on an ability to reason from "firstmon sense approaches that characterize expertise in the clinical principles" rather than to rely on empiric associations betweendecision-making areas being modeled; observations and hypotheses);

3) explanation - how to build decision support programs that 3) enhanced explanation capabilities, ideally guided by an im-not only give advice but can defend their decisions in terms phy- proved understanding of how human beings explain things tosicians can understand, and; one another, particularly, how they adapt their explanations to

4) the logistics of integration - how to design and implement the knowledge and experience of the individual requesting ad-computer-based decision aids that fit smoothly into the daily vice;routine of physicians' practices, acknowledging their hectic 4) experimentation with new machine architectures (e.g., paral-schedules, and aids that seek to demystify and simplify the me- lel processing or networking of multiple-coordinated processors)chanics of the human-computer interface. that may permit an optimal assignment of languages and inter-

Several successful early approaches to these problems were faces for the individual subtasks required by high-performancedeveloped during the last decade. Large patient data bases were decision-making programs;constructed and used to aid in defining the prognosis for new 5) experiments that seek to provide an optimal melding of sym-cases.67,8 Investigators who depend on valid statistics to sup- bolic techniques drawn from artificial intelligence research andport their decision-making systems began to look at geograph- the analytic techniques of formal statistics, pattern recognition,ical variations in populations to assess the transferability of pro- and decision theory;grams.9 Hospital information systems became more common 6) research into new ways of developing technologies for per-and provided promising early models for routine acquisition of sonal computing and graphics might heighten both the accepta-relevant data.10 Complementary work in the development of large bility and cost-effectiveness of systems that aid physicians withcomputer-based text documents designed to bring up-to-date decision-making tasks.knowledge of a domain to the practicing physician also took There is little doubt that additional challenges could be addedplace.11 to this list, and some readers may quibble with those include.During the same period, non-numerical approaches based on But it is clear that two additional issues stand foremost on the

artificial intelligence techniques became prominent,12 and these medical computing agenda for the 1980s: 1) the improvement ofsuggested several new methods for encoding uncertainty, rep- education of medical students and practicing physicians regard-resenting expert knowledge, and modeling the reasoning proc- ing computers and decision making, and 2) the enhanced accep-esses of accomplished clinicians. The symbolic reasoning tech- tance of medical computer science as an intrinsic part of theniques suggested ways for decision-making programs to explain modern academic medical environment. The financial and aca-their reasoning to physicians, thereby allowing the user to de- demic support necessary for tackling difficult tasks such ascide whether to follow the system's recommendations.13 Interac- those outlined above will be available only if there is improvedtive techniques have been developed that also allow experimen- recognition of the fundamental research questions that exist fortal systems to interview experts and to acquire new knowledge the medical computing community.directly from them.14

Finally, several notable experiments that sought new ways to

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EXJI1PUTEFS Win IlEIJIEJIREReferences: 10. Lindberg, DAB: The Growth of Medical Information Systems in the United States.

1. Croft, DJ: Is Computerized Diagnosis Possible? Comput Biomed Res. 5:351-367, Lexington, Massachusetts: Lexington Books, 1977.1972. 11. Bernstein, LM, ER Siegel, and CM Goldstein: The hepatitis knowledge base: A

2. Friedman, RB, and DH Gustafson: Computers in Clinical Medicine: A Critical Re- prototype information transfer system. Ann Int Med. 93(part 2):169-181, 1980.view. Comput Biomed Res. 8:199-204,1977. 12. Szolovits, P, and SG Pauker: Categorical and probabilistic reasoning in medical

3. Shortliffe, EH, BG Buchanan, and EA Feigenbaum: Knowledge Engineering for diagnosis. Artificial Intelligence, 11: 115-144, 1978.Medical Decision Making: A Review of Clinical Decision Aids, Proc of the IEEE. 13. Shortliffe, EH: Computer-Based Medical Consultations: MYCIN. New York: Elsevier-67:1207-1224, 1979. /North Holland Publishing Company, 1976.

4. Lusted, LD: A Society and a Journal, (editorial). Medical Decision Making. 1:7-9, 14. Davis, R: Interactive transfer of expertise: Acquisition of new inference rules. Ar-1981. tif icial Intelligence. 12:121-157, 1979.

5. Teach, RL, and EH Shortliffe: An Analysis of Physician Attitudes Regarding Com- 15. Watson, RJ: Medical staff response to a medical information system with directputer-based Clinical Consultation Systems. Comput Biomed Res. 14:542-558,1981. physician-computer interface. MEDINFO-74. Amsterdam, the Netherlands: North Hol-

6. Feinstein, AR, JF Rubenstein, and WA Ramshaw: Estimating Prognosis with the land, pp.299-302, 1974Aid of a Conversational Mode Computer Program. Annlnt Med. 76:911-921, 1972. 16. Schultz, JR, and L Davis: The technology of PROMIS. Proc of the IEEE. 67:1237-

7. Fries. J: Time-oriented medical records and a computer data bank, J Amer Med As- 1244, 1979.soc. 222:1536-1542, 1972. 17. Warner, HR, CM Olmstead, and BD Rutherford: HELP - a program for medical

8. Rosati, RD, JF McNeer, CF Starmer, et al: A new information system for medical decision making. Comput Biomed Res. 5:65-74, 1972.practice. Arch Int Med. 135:1017-1024, 1975. 18. Elstein. AS, LS Shulman, and SA Sprafka: Medical Problem Solving: An Analysis

9. deDombal, FT: Acute abdominal pain: An OMGE survey. Scand J Gastroent. of Clinical Reasoning. Cambridge, Massachusetts: Harvard University Press, 1978.14(Supplement 56):30-43, 1979. 19. Kassirer, JP, and GA Gorry: Clinical problem solving: A behavioral analysis. Ann

Int Med. 89:245-255, 1978.

BMEs needed to engineer medical knowledgeBy ROBIN B. LAKE and HANG-YAT TAM

Case Western Reserve University, Cleveland, Ohio

T his is an exciting and challenging time for medical decision * must perform the physical examination (at least in thismaking. Several computer-based medical decision-making decade);systems, such as MYCIN1 with clear clinical value, have * can reach presumptive diagnoses in most cases during the

been demonstrated. Advances in computer technology now al- history taking and physical examination;low inexpensive, competent systems with large amounts of mass * is more expert in his own field, because his correlationstorage. New perspectives on software engineering enable of knowledge and reasoning transcends that of the computerclean, rapid implementation of novel, sophisticated strategies system.for decision assistance. The major impediments to developing comprehensive andComputer-assisted decision making is needed because: medi- complete decision systems are:

cal knowledge is expanding rapidly - beyond any individual phy- * The logical sequences underlying the processes of clini-sician's ability to absorb it; specialist consultants are not avail- cal diagnosis are very incomplete in medical textsable to every physician all the time; and practitioners in any and articles, and therefore difficult to express inendeavor are "human" - with errors of omission and forgetful- algorithmic form;ness. Clinical decision-making systems do help a practitioner * There is incomplete knowledge relating probabilities ofprovide better quality care (in the sense that "quality" means clinical findings, given a specific disease, and vice versa.consistency) by prompting, suggesting, and querying. In this Medical records are uniformly deficient in notsense, even simple reminder systems that present only possible recording residual signs and symptoms followingalternative diagnoses, with all decision making left to the physi- treatment and cure;cian, may have value. * There are, as yet, no uniformly satisfactory methods inA computer-based system can perform better than a human in approaching medical diagnosis. The logic approach, which

certain areas. The computer can: seeks specific findings or attempts to resolve certain inter-* accumulate knowledge "instantly" from a wide variety of mediate goals, greatly limits the scope of diseaseexpert sources, and not forget it; possibilities.

* thoroughly and exhaustively search through all information The probability approach can exhaust all possibilities, butimpartially; suffers from fundamental problems when multiple diseases

* calculate probabilities, statistics, and measures of informa- are present.tion value quickly and accurately. Why not just capture all medical texts and records in machine-

The human however: readable form as a complete knowledge base? Again formidable________________________________________________ obstacles arise. They are:

Dr. Lake works in the Department of Biometry at CWRU and in * While much progress has been made in analyzing thethe research laboratory at The Standard Oil Company (Ohio). He content of English text, it IS very difficult toreceived a B.S. in EE in 1960 from Rensselaer Polytechnic Insti- haveacomputer extract even simple facts fromtute, a M.A. from Harvard in 1964, and a Ph.D. from CWRU in acorpusof text.

' ' ~~~~~~~~~~~~~~~*Standard nomenclatures exist only in certain medical1969, both in Biomedical Engineering. Dr. Tam is a NLM trainee seilis hl hs etitdvcblreat CWRU in the Department of Biometry. He received his M.D.seills hltee etitdvcblrefrom China Medical College in Taiwan in 1974 and did surgical are used to support medical records, authorsresidency at the State University of New York at Buffalo.oftxb ksaentocntrid.T res

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