DJmlPUTEFS hR mEJEJIF1nEPROMIS. Proc of the 4th Annual Symposium on Computer Applic in Med Care. pp. 762- Practice. Archives of Internal Medicine. August 1975.770, November 1980. 41. Lindberg, DAB: The Growth of Medical Information Systems in the United States.

32. Bleich, HL: Computer Evaluation of Acid-Base Disorders. The J of Clin Investiga- Lexington Books: Lexington, Massachusetts, p. 98, 1975.tion. 48:1689-1695,1969. 42. Report by the U.S. General Accounting Office. Computerized.Hospital Medical In-

33. PROMIS Laboratory, "Representation of Medical Knowledge" and PROMIS. Proc formation Systems Need Further Evaluation to Ensure Benefits for Huge Investments.of the 2nd Annual Symposium on Computer Applic in Med Care. pp. 368-400, November AFMD-81-3, November 1980.1978. 43. Policy Implications of Medical Information Systems. Office of Technology As-

34. Shortliffe, EH, BG Buchanan, and EA Feigenbaum: Knowledge Engineering for sessment, November 1977.Medical Decision Making: A Review of Computer-Based Clinical Decision Aids. Proc of 44. Collen, MF: A Guideline Matrix for Technological System Evaluation. J of Medi-the IEEE. 67 (No. 9):1207-1234, September 1979. cal Systems. 2 (No. 3):249-254, 1978.

35. Pople, HE Jr, JD Myers, and RA Miller: DIALOG: A Model of Diagnostic Logic for 45. Klarman, HE: Application of Cost-Benefit Analysis to Health Systems Technolo-Internal Medicine. Proc of the 4th IJCAI. 2:849-855, September 1975. gy. J Occupational Med. 15 (No. 3):172-186, March 1974.

36. Weiss, S, CC Kulikowski, and A Safir: Glaucoma Consultation by Computer. 46. Drazen, E, and J Metzger: Methods for Evaluating Costs of Automated HospitalComputers in Biology and Medicine. 8:25-40, 1978. Information Systems. Research Summary Series. National Center for Health Services

37. Patrick, EA, J Fattu, and R Uthurusamy: CONSULT I: Consulting and Diagnosis Research, DHHS Publication No. (PHS) 81-3283, July 1981.for Doctors; CONSULT II: Video Player Added. Proc of the 5th Illinois Conf on Med In- 47. NCHSR Research, Demonstration and Evaluation of a Total Hospital Informationform Sys. pp. 1-13, May 1979. System, Summary. 77:3188.

38. DeDombal, FT, DJ Leaper, JC Horrocks, JR Staniland, AP McCann: Human and 48. Waymack, P: An Evaluation of Platelet Transfusion Practices in the Johns Hop-Computer Aided Diagnosis of Abdominal Pain: Further Report with Emphasis on Per- kins Oncology Center. Internal Report, 1980.formance of Clinicians. Brit Med J. 1:376-380, 1974.

39. Warner, HR: Computer Assisted Medical Decision Making. Academic Press: NewYork, 1979. This article is adapted from a paper in Applications of Computers in Medicine, pub-

40. Rosati, RA, F McNeer, F Starmer, et al: A New Information System for Medical lished in 1982 by IEEE/EMBS; edited by M.D. Schwartz, Ph.D.

Computers let medical staffs spend more timeinterpreting clinical data, providing care

By J. ANN COPELAND, Ph.D.,Georgia Institute of Technology, Atlanta

and BARUCH HAMEL, Ph.DUniversity of Miami, Coral Gables, Florida

D ata base management systems If physicians and paramedical staff must Other advantages of DBMS include cost-(DBMS) have been used to facilitate spend their time scrutinizing patient data effectiveness and reduction of data redun-retrieval and update of data in com- instead of giving patient care, it is possible dancy and software maintenance. Automa-

puter systems for many years. However, a that standards of care will diminish. By us- ted medical charts also offer advantagesgap has developed between data manage- ing automated data handling, medical such as time savings, completeness, legi-ment and interpretation, largely because staffs can spend more time interpreting bility, and usefulness in research. Easy ac-computers have not successfully been pro- clinical data and less time manipulating cess and retrieval can contribute to moregrammed with inference capabilities. routine data. intensive system use.This gap is particularly noticeable in Use of computers in medicine ranges The main disadvantages are those asso-

medical information systems as physicians from simple and routine financial book- ciated with the installation of every neware faced with ever-increasing numbers of keeping to experimental research on con- system: cost, implementation, and organi-medical records. Managing such volu- sultation, diagnosis, and advice to physi- zation. The almost unlimited storage capa-minous amounts of information is virtually cians. Using a good DBMS gives the user bilities may cause confusion rather thanimpossible with conventional paper-and- his own view of the data according to his improvement in decision making, simplypencil methods. The problem is especially own modeling. The user then does not because too much data may be generated.2striking in the case of chronically ill pa- have to worry about the format of the data. Also, the novice user may see computer-tients, such as those suffering from renal As a result, faster, more accurate, and ized clinical records as rigid, unreliable,failure. Data from such patients can accu- more flexible data structures and data up- costly, inaccurate, impersonal, and inap-mulate at a very high rate. dates are possible.1 propriate.3

32 EMB MAGAZINE JUNE 1982 0278-0054/82/0200-0032$OO.75t 19821EEE

E3OI1PUTEFIS Iii EIJIEJIF1EIn order for automated systems to be- ensure correct data transmission; specified symptoms associated with the

come a routine part of clinical medicine, - ancillary support systems, designed disease.personnel in the medical community must forspecializedsupport;and Bayes' Theorem and the associatedcome to accept and rely on computerized .- medical information systems, con- probabilities are often used in conjunctionsystems for handling clinical records. Such cerned primarily with the data's medical with decision strategies to associate dis-systems are not widely used at present, significance. eases with patient symptoms. Such strate-largely because of unfamiliarity and appre- Barnett says that, in a MIS, the comput- gies include decision trees,18,19,20,21,2223hension in the medical community. Physi- er's job is to recognize and categorize "the heuristics and Al,242526 Bahadur's model,27cians and other staff have shown a lack of content of the medical data.. it must be mixed-data,28 threshold logic,2930 clusterunderstanding and unwillingness to accept an active or responsive system." Barnett analysis,31323334 Ford's algorithm,35 Vennand learn a new system. These in turn re- calls such a MIS a "total hospital informa- diagrams,36 distance measures (also calledsult in mistrust and apprehension. tion system" that uses a "pragmatic hill- Bayesian distance),37 graphs and semanticTo overcome such negative attitudes, climbing" approach. nets383940 (such as CASNET,4142 PLAN-

hospital information systems (HIS) must be One such system, or rather a software NER,43 DIALOG,44 and QA45), discriminantsimple, understandable, guiding, and trust- environment for developing such systems, analysis,464748,49 pattern recogni-worthy. Easy access to statistical testing is MUMPS (Massachusetts General Hospi- tion,5051,525354.55 judgmental knowl-of hypotheses, guidance in selecting the tal Utility Multi-Programming System).5 An- edge,56,57,58,59,60,61,62 frames, 6364 flowproper statistical procedure, and tabulated other is the Harvard Community Health charts,65 matrices,66 and Markovor graphic displays of the results can en- Plan (HCHP)6 described by Justice et al. chains.6768courage physicians to use computer sys- HCHP provides for storage and retrieval of In an attempt to overcome some of thetems. New research in natural language information necessary for primary patient criticism of diagnostic systems, such assystems also points a way toward remov- care and also meets some administrative rigidity and poor interfacing to the user, aning the barrier between computer systems needs. Janssen and Everett,7 at the Univer- alternative type of system has been sug-and clinical decision makers. sity of Minnesota Hospitals and Clinics, gested. It emphasizes the interaction with

In this paper, we will review devel- achieved similar goals with their system. the physician in arriving at a diagnosis.opments in HIS that have given medical This model recognizes the physician aspersonnel greater access to using comput- Software stems availabl "the captain of a health care team" ander systems. Particular attention is given to

Man stemsav e emphasizes his role through interactionMEDQUEL, a program that incorporates Many commercial software systems ex- with the system in the process of decisionnatural language. ist that provide for either data storage and making.69The number of hospitals with computer retrieval or data analysis. However, there Pryor et al 70 at the LDS Hospital in Salt

systems has increased dramatically in re- are few systems that can do both. Several Lake City, Utah, used such an approach incent years, starting usually with a finance systems have been developed to help fill developing HELP (Health Evaluationsystem in the business office. Various the gap These Include CLINFO (Clinical In- through Logical Processing). HELP is de-types of HIS have emerged. Some simply formation), PROPHET,8,9 MUMPS,10,11 signed to isolate the physician from directstore and display patient data; others, TOD,12 and GEMISCH.13 These systems data collection, allowing him instead totermed medical information systems (MIS), represent a step toward the goal of inte- concentrate on evaluating computer-de-collect and store data in order to assist in grating and simplifying the processes of fined problem statements. The volume ofdecision making.4 Most HIS can be classi- clinical data collection and analysis. data handled by the physician in makingfied into one of the following categories: The Rand Corporation studied methods decisions and medical record entries is re-

v- transaction-oriented systems, used of integrating data collection and interac- duced considerably. Such a system is use-primarily for billing and finances; tive application of standard statistical pro- ful not only for consultation, but also for

i communication systems, provided to cedures. After a survey of needs and exist- evaluating patient care, teaching medicaling systems, a prototype Data Analysis students, and providing an effective clini-

Dr. Copeland is a research scientist at the System (DAS) was produced.14 It allowed cal research tool.Engineering Experiment Station of Georgia users to access data and interactively ap- Another approach focuses on alliedInstitute of Technology in Atlanta. She re- ply standard statistical routines in their health care personnel. In such systems,ceived a B.S. in Computer Science and data analysis.15 With the support of NIH, data is entered from a lab slip, instead ofMathematics from Furman University in further development resulted in the CLIN through physician interaction at a terminal.Greenville, SC in 1974, a M.S. in Systems FO system.1617 CLINFO combines data The computer compares a patient's data toand Information Science in 1975, and a mana n l h net of that of similar patients in the data bank andPh.D. in Computer Science in 1980, both cicastde.produces a report to the physician, show-from Vanderbilt. Dr. Hamel received his In another area, effort has been directed ing possible treatments and patient re-B-S-E-E- in 1968 from the Israeli Institute Of toward providing deductive systems for sponse.71Technology, and his M.S. and Ph.D. in E.E. "automatic" diagnosis. Bayes' Theorem is MYCIN was designed as an advice-givingfrom Vanderbilt in 1974 and 1977, respec- used most frequently in these systems. In system, using a hierarchical type AND/ORtively. He was Associate Professor of Bi- medical application, Bayes' Theorem pro- tree. Certainty (or probability) factors areomedical Engineering at the University of vides a way of determining the probability associated with each conclusion or moveMiami, from August 1980 until May 2, 1982 that a patient has a particular disease, on the tree.72when hewas killed in acar accident. based on the observance of previously A criticism of the decision tree approach|

EMB MAGAZINE JUNE 198233

EflI11PUTEFS Iii IlEIJIEJIflEhas been that it is biased by a single deci- definitions,84 which may contribute to the The capacity to enter requests in naturalsion maker. To counter such criticism, an- lack of formalism for diagnosis in medical language, combined with easy access toother model assumes two or more decision education.85 Before a computer system can statistical routines, can help make the sys-makers - for example, a physician and a be implemented, some algorithm must be tem appealing to medical personnel whopatient - engaged in cooperative games. developed based on a clear understanding have little computer experience.The system makes guesses and sugges- of heuristics used by the physician.86tions for tests by employing both prior and Many people believe that computers may MEDQUELconditional probabilities.73 never be of much help in diagnostic work MEDQUEL was designed in two parts:

Person-machine interaction through dia- because the nuances and subtleties of the natural language user interface and thelogue is emphasized in a system described clinical judgment may be impossible to for- statistical and exploratory data analysisby Gorry et al.74 The system was designed malize for machine implementation. Even system (Figure 1). These two systems com-to assist in clinical management of acute rudimentary theories can be too complex municate with the DBMS that performs theliguric renal failure. Once presented with a to be accommodated by conventional com- appropriate data manipulations.case, the system presents the disease, puter science and technology. The most re- The system was programmed in "C"90asks questions interactively, and updates cent hope for overcoming this challenge is under the UNIX91 operating system, on athe problems. in artificial intelligence research.87,88 PDP 11/34 with 96K words of memory. A re-A combination of health care and eco- Other concerns about computer applica- lational DBMS called INGRES (Interactive

nomics in a system is described by Chalice tions to medical settings involve the social Graphics and Retrieval System)92 was usedet al.75 This system produces patient data impact of systems. Many people are re- to manipulate a small subsection of a datasummaries and audits patient care. It also pelled by the notion that considerations base containing clinical, neurobehavioral,makes suggestions for reduction in health about quality of life, pain, and risk can be psychometric, and general informationcare costs, such as the use of alternate lab quantified. Automated health care delivery about kidney failure patients and a grouptests. challenges social conceptions that medi- of normal control subjects.93 INGRES can

cal decision making is an exclusively hu- be accessed by QUEL (Query Language)Implementation problems man domain. Other criticisms that em- and by EQUEL (Embedded Query Lan-

The many types of systems are accom- phasize the computer as a machine point guage). QUEL is a formal language used forpanied by an equally varied set of chal- out that the computer can't perform a phys- data retrieval. EQUEL allows execution oflenges to their medical application. These ical examination, doesn't talk with the pa- data base manipulations from use of pro-challenges range from criticism of theory tient's family, and can't verify questionable grams written in the general purpose lan-use and data acquisition to broader social lab data.89 guage "C."concerns. Lack of standard medical defi- The system described in the next sec- In the MEDQUEL system, users can in-nitions also compounds the problems of tion is an attempt to overcome at least teractively enter natural language requestsdeveloping acceptable systems. some of the problems of computer applica- for data retrieval and analysis, though re-

In the area of theory application and data tions to health care delivery. It provides for trievals are at present limited to simple re-acquisition, the use of Bayes' Theorem has automatic guidance in selection of statisti- quests involving one patient, one variable,received the most criticism.76 The basis of cal analyses coupled with easy, informal and one time epoch.the theorem, which requires mutual exclu- manipulations of a small clinical data base. The user natural language interface con-sion and statistically independent el-ements, may also serve to invalidate itsmedical application.77 That is, strict appli-..-1cation would stipulate that there cannot be English Lexical Purged Parser DUEL User Validated INGRES Retrieved|more than one disease present in any one query analyzer query (AFSM) validation query (03) daispatient.78 In reality, though, physicians I _must consider the possibility that a patient Dic- Clincialhas more than one problem at once. tionary tabaRulesd basThough the use of Bayes' rule is appropri- L_...-_ate for many situations, users must becareful not to oversimplify and make incor-.l.Irect generalizations.7980 Results

Regardless of the theory used in a sys- Item, more problems are encountered when lRerivegathering data in the first place. There is a dagRtriva sysemanailyspeisi plSatitckage Ilack of large, reliable medical data bases | dt sselaayi akGcontaining probabilities of any disease and Iany symptom subset.81 82 Explore Generation Data Choice ot Suggested User I

Thi prbe tm fo ako of data sets sets analyses analysis approvaljudgmental values, since experts' opinions l_ __ _ __ _ _. _.1___ _usually vary according to the heterogeneityand complexity of medical knowledge.83The result is a lack of standard medical Figure 1: Block diagram of the MEDQUEL system

34 EMB MAGAZINE JUNE 1982

EXJI11UTEFS Iii EIJIEJIflEflects attempts to reduce the work of the

at was iko' parser by minimizing material at earlylowest Jan. stages. The lexical pass formalizes theCreatn type and meaning of each input portion.

The output is then analyzed according torules provided by a grammar, which is im-

Cleanup m plemented as an AFSM (augmented finiteinput& state machine). After the AFSM stops, in-removenoisewords > Please formation is saved in the registers during

11 l ff | : r= | t ,eenter parsing. This information is the basis forI Find each reuetthe formal query, constructed in QUEL.

' dictionary The formal query is then submitted tothe user for approval. After approval ismissumel _" Wor given, data retrieval, analysis, or both takeVmisspelling) ,,-/\ /Word\ | place. A more detailed description of theyI

COI1PUTEFS Iii FIEEJIEJIFlErange of c is chemretrieve (c.creat)

datrieved where (c.name = "bwO" andNYdata c.date "78-01-01" andc.date c "78-12-31 ")

l | Store in | | The retrieval parameters that describe theSTATISTICAL ANALYSES data selection - the patient's name, varia-

-m.-' ble name, and time span-are stored onUser-specifie Check for . parallel push-down stacks with the retrieval

Keyword analysis constraints routine results.- - - -

- -- - - Retrieval 2: Next, the user may enter,

r XI,Explore mode | "List awl's BUN for August 1977-FebruaryGenerate list of 1978." The formal query that will be gener-current patients, ated will be:variables, and range of c chemtime periods retrieve (c.bun)

where (c.name = "awl" andLinked lists c.date ' "77-08-01" and

c.date "78-02-28")Examine for common Again, the patient's name, the variableparamTieters ______ name, the time span, and data are stacked

L Lists of for later references.combinations For any retrieval, the user may request

that certain analyses be performed. For ex-Choose combination ample, after the last retrieval, the user

l g 8 of data sets j | might enter the request, "Do a five numbersummary." A summary such as that shown

l Pointer in Figure 4 would be displayed. The user,to subtree | | may continue to request other analyses

select_______s such as "simple statistics" (Figure 5), "his-

togram" (Figure 6), "box and whiskers"YES More YES M (Figure 7), and "regression" (Figure 8).

analyses/expl Retrieval 3: The user may ask for both re-trieval and analysis in one request by enter-

0po\> ing, "Give me a scatter plot of his CREAT."YES More NO |Examine next The system will analyze and fill in the pa-

omb. user request tient's name and time span in order to gen-erate the formal query:range of c is chemretrieve (c.chem)

Figure 3: Block diagram of the data analysis process (The Expert) where (c.name = "awl" andc.date "77-08-01 " andc.date c "78-02-28")

As in the other examples, the pertinent in-# 25 = number of points formation is stacked. The keyword would

.r--------

-_ r-_________.._5 | simply be an integer key indicating "scat-Il I ter plot."

M13 101l000 The user may request analysis of re--13 I101.000I1| median | | trieved data sets, such as "correlation ana-

H 7 I88.000 103.000 hinge hinge lysis" (Figure 9). Here the user is asked1 75.000 118.000 I minimum maximum | whether he wants the analysis of "data vs.

l l l l ~~~~~~~~~~~~~~~~data"or "data vs. date." In the example,| | .~~~~~the response indicates the user wants cor-relation analysis performed on two data

Fieumerum arfoaw'sBU sets. The analysis is done for the two mostFigure 4: Fvnu bru mrfoa1sBNrecently retrieved data sets: awl's CREATand awl's BUN. If the user had indicated"data vs. date," the analysis would havebeen on the most recently retrieved set ofdata pairs, such as awl's CREAT.Each of these requests deals with one

36 EMB MAGAZINE JUNE 1982

CCJIIPUTEFS Iii TIEIJIDFEEdata date

Minimum: 75.000 2792.000Maximum: 118.000 2955.000 BUNRange: 43.000 163.000Sum: 2426.000 71878.000 118.0 118.0Sum of squares: 238154.000 2.07e+08 115.9 |Mean: 97.040 113.7 I IVariance: 113.957 111.6 |Standard deviation: 10.675 109.4 ISkewness: -0.114 107.3 * IKurtosis: -0.831 105.1Mode: 101.000 103.0 I103.0F 1100.8 101.0 i-

98.71Regression line: Y =0.070X + -103.041 96.5Note: Trend computed with date (YY-MM-DD, no hours or minutes) 94.4 1

as independent variable and data as dependent variable 92.2 I I 190.1 I I| ~~~~ ~ ~ ~~~~~~~~~~~~~~~87.9188.0 L J Z

Figure 5: Simple statistics for awl's BUN 8I88. TI8316181.51| ~~~~~~~~~~~~~~~~~~~~~~~~79.3

CLASS FREQUENCIES 77.2frequency 1 2 3 3 2 1 9 1 2 1 72.8 L 75.0

18

1514

12 _

109 |

......Figure 7: Box and Whiskers Plot7 | . |forawl's BUN

2 1*---*.***... . :: . ....o and he may also request and specify graph-1 i.ical output and display, such as scatter____________________________ plots and histograms.

75.0 79.3 83.6 87.9 92.2 96.5 100.8 105.1 109.4 113.7 If the user requests guidance in choos-79.3 83.6 87.9 92.2 96.5 100.8 105.1 109.4 113.7 118.0 ing statistical analyses, the keyword indi-

cates the "explore" mode. The data param-eters or "clinical data descriptors" that

Figure 6: Histogram for awl's BUN were used in data retrieval are used again,in order to choose applicable data analysis

patient, one variable, and one time epoch. words. These are the keywords detected by procedures.After the natural language preprocessor the lexical pass when analyzing the user's Before any analysis is conducted, cer-analyzes each query and performs the re- intitial query. If the user has specified ex- tain checks are performed. For example, atrieval, the data-date pairs are displayed actly the intended data analysis procedure, check for normal distribution of data wouldand mnade available to the data analysis the keyword simply indicates the request- indicate whether parametric or nonparame-system. The retrieved data sets are then ed analysis to beperformed. tric analyses should be used. Other checksstacked. Simple analyses (mm, max, range, aver- inldnubrodaapntadlmt-Simple requests concerned only with age, standard deviation) can be requested tion of computation.

data retrieval may be fulfilled through the with a simple data request, and the system Discussionformal QUEL request. However, the user will immediately branch to execution of MEDQUEL's distinctive attribute is themay want certain analyses performed or analysis after data retrieval. The user-may integration of data base management fa-special graphical displays. Such user re- also request statistics that use more than a cilities with natural language, graphical,quests are drived from the non-QUEL key- single data set (regression, correlation), and statistical analysis procedures. In addi-

EMB MAGAZINE JUNE 1982 37

EXJFJ1PUTEF Iii ITEIJIEJIflEtion, the system can examine and aggre-gate data sets and suggest statistical ana- BUNlyses for specific data set combinations. 118.0 I *The best application of the system may 115.9 *

be in situations where data accumulates 113.7rapidly, as with chronic disease patients. 111.6 |The system itself was developed using 107.3data from renal patients. However, if data 105.1 *collected in other areas of clinical medi- 1068 I * *0 .cine is organized in an INGRES data base, 98.there is no restriction on MEDQUEL's ap- 96414 I . *..splication. Because of the high level of sys- 92.2tem modularity, the "expert" statistical 90.1 | . ... *and graphical analyses are independent of 87.9 * *the data being analyzed. Most changes 83586would have to occur in the front-end natu- 81.5 *ral language preprocessor, in order to 79.3adopt the system to the universe of a dif- 7570ferent medical discipline. The modularity 72.8also allows for easy additions or deletions date 77-8-23 78-2-2of statistical functions in the code.

Both physicians and nurses accepted Slope: 0.070(+ or-0.083)the mode of operation where the user com- Intercept: -103.041 (+ or- 238.084)

Regression line: Y = 0.070X + - 103.041municates directly with the data base and Standard error: 10.252the "expert" retrieval and statistical analy- Significance of slope: p < 0.093 (f = 3.023 with 1 and 23 df)ses using QUEL. The natural language and 1: Default scalingor 2:Yourspecifications?1data exploration modes are still experimen- Ital. The major limitation to front-end natu-ral language is the limited capability to Figure 8: Line regression forawi's BUNcomprehend complicated requests. Howev-er, because the statistical "expert" proc-ess is not limited, users can directly ac-cess it with QUEL requests. 1: datavs.data or 2: datavs. ate? 1Expanding the scope of the front end is

an area for further study. Other efforts are Number of pairs found: 25directed at training the MEDQUEL systemto "learn" user preferences in conducting Correlation coefficient: 0.748data analyses. It might even be possible to Explained variance: 0.560extend this requirement so that a user Reliability: 0.515could log on with different tasks in mind. Significance level: 0.005For instance, he may first log on as a clini-cian looking at an individual patient's data.Another time, he might log on as a re-searcher looking at groups of patients, Figure9 Correlationanalysisofawl'sCREATandBUNaccording to a given experimental design.The system's default selections for data References 8. Ransil, BJ: Applications of the PROPHET systems inanalysis would vary each time, depending 1. Seaton, B: Data Processing by Computer: On-line or Human Clinical Investigation. AFIPC-NCC. 43:477-483,on which approach the user chose. A sim- Off-line? computers and Biomed Research. 7:142-156, 1974.pler solution might be to allow the user to 1974. 9. Castleman, PA, CH Russell, FN Webb, CA Hollister,specify his intentions in analysis selection 2. Shain, KE: Keeping up with ... Data Base Manage- JR Siegel, SR Zdonik, and DM Fram: The Implementation

ment Systems and Health Care Managers. Health Care of the PROPHET System. AFIPS-NCC. 43:457-468,1974.each time an analysis was requested. Management Review. 2:51-57, 1977. 10. Barnett, GO: Medical Information Systems - Pre-The system also needs expansion of its 3. McLean, D, and SV Foote: A comparative evaluation sent Illness and Prognosis. Adv Med Sys: The 3rd Cent.

statistical analysis bank. A particular need of automated medical history systems. AFIPS-NCC. ed. EJ Hinman. Miami: Symposia Specialists, 1977.44:733-738,1975. 11. Greenes, RA, AN Pappalardo, CW Marble, and GOis for multivariate analyses. Enlarging the a. V/eazie, 5, and T Dankmyer: HISs, MISs, DBMSs: Sort- Barnett: A System for Clinical Data Management. AFIPS-system's universe to include more variable ing Out the Letters. Hospitals. 51:80-84, 1977. NCC. 35:297-305.and subject categories, as well as more 5. Barnett, GO: Medical Information Systems - Pre- 12. Fries, JF: Time-Oriented Patient Records and a

' ~~~~~~sentIllness and Prognosis. Adv Med Sys: The 3rd Cent. Computer Databank. J of Am Med Assoc. 12:1536-1554,statistical analyses, would result in a much ed. EJ Hinman. Miami: Symposia Specialists, 1977. 1972.more powerful system. Groups of variables 6. Justice, N, GO Barnett, R Lurie, and W Cass: Devel- 13. Lloyd, SC, BA Brantley, WVW Stead, and HK Thomp-and patients could be studied using multi- opment and implementation of a medical-management in- son: A Generalized Medical Information System (GEM-formation system at the Harvard Community Health Plan. ISCH) for Practicing Physicians. Nat'l Conf of the Assocvariate statistical techniques. Such a AFIPS-NCC. 43:159-165, 1974. for Computing Machinery Proc. P-17:684-692, 1971.change definitely represents a more realis- 7. Janssen, E: Diagnostic Information: Full Utilization. 14. Palley, NA, and GF Groner: Information processingtic situation in clinical patient maniage- Adv Med Sys: The 3rd Cent. ed. EJ Hinman. Miami: Sym- needs and practice of clinical investigators -Survey re-

ment ad medial resarch.posia Specialists, 1977. suIts. AFIPS-NCC. 44:717-723, 1975.

38 EMB MAGAZINE JUNE 1982

15. Levitt, G, OH Stewart, and B Yormack: A prototype Proc of Internatil Joint Conf on Al. 1:50-58, 1977. tion Capabilitiea of the MYCIN Syatema. Computers andayatem tor interactive data analyaia. AFIPS-NCC. 43:63- 40. Fikea, R. and G Hendix: A Network-Baaed Knowl- Biomed Research. 8:1-18, 1975.69,1974. edge Repreaentation and ita Natural Deduction System. 62. Trigobott, M, and CA Kulikowski: IRIS - A System

16. Lloyd, SC, BA Brantley, WW Stead, and HK Thomp- IJACI Proc. 1:235-246, 1977. tor the Propagation ot Interences in a Semantic Net. Procaon: A Generalized Medical Information System (GEM- 41. Weiaa, SM, CA Kulikowski, A Satfir, and S Amarel: A of Internatil Joint Conf on Al. 1:274-289, 1977.ISCH) for Practicing Phyaiciana. Nat'l Conf of the Assoc Model-Based Method for Computer-Aided Medical Deci- 63. Davia, R, B Buchanan, and EH Shortliffe: Productionfor Computing Machinery Proc. P-17:684-692, 1971. sion Making. Artificial Intell J. Special iaaue on Applica- Rulea aa a Repreaentation tor a Knowledge-Baaed Con-

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Solving problems quickly and easilyBy LEAH CATESEMB Staff Writer

ob Wickizer is a curious blend of Mid- Thus, the programmer is able "to see what main-frame companies.western grassroots convictions, ag- he or she is doing to the code, what the In its design, MUMPS meets the require-gressive young businessman and code is doing to them," Wickizer says. ments formulated by Barnett and Greenes

member of a technological avant-garde. He MUMPS is an example of an interpretive, in 1970 for effective hospital data baseis president of Mid-Continent Computer user-oriented language that resulted from management systems. Writing for Comput-Systems Inc., in Columbia, Missouri. The such work, says Wickizer. The user, with ers and Biomedical Research, Barnett andbusiness is a development house, special- the entry of MUMPS and its user-friendly Greenes include speed of output and pro-izing in the MUMPS (Massachusetts Utility features into the computer arena, became cessing and flexibility of permitted inputMulti Programming System) data base directly involved. "What that meant was data type among the requirements. A sys-management system. It is through the spe- that if you wanted to develop a program tem also must have a common data basecialty of his business that Wickizer comes you didn't have to go through the compile with multiple terminals so that a variety ofto his grassroots and avant-garde affilia- stage," Wickizer says. "Just skip it en- users have access to patient information.tions. tirely. Sit down, write your program and see The system must be easily changed to ac-MUMPS was important in Wickizer's pre- the results; it's executed as soon as you commodate new ways of looking at the

vious work, when he used the language for type it." data, and must have the potential for link-data processing in a CAT scanner project. As programmers and users alike began ing with other departments in the hospital.That work was in the late 1970s. Even now, seeing their computer work more directly, And as needs and technologies grow, theWickizer says, MUMPS is considered "a they began to experiment with applications system must be adaptable to a variety ofrelative newcomer to the computer scene." of user-oriented systems. One such appli- media, such as graphics, audio and videoThe language is in part the result of early cation was developing hospital information presentations.data base management systems devel- systems; MUMPS was developed at Massa- One of the most recent projects Wickiz-opment. chusetts General. With increased applica- er's development house is working on isThe data base management systems tion, says Wickizer, came a push for mak- called IRIS (Integrated Radiology Informa-

work was, according to Wickizer, not just ing MUMPS a standard ANSI (American tion System). Wickizer's description of theanother computer development project. National Standards Institute) language. system makes it seem ready-made to meetThe systems that resulted were no longer COBOL, FORTRAN and PL-1 had all been Barnett's requirements.compiled, but interpretive. established as standard languages. Their In IRIS, physicians dictate reports into aWith this development, Wickizer says, re- standardization was the result of moves by voice entry and response station - the VIS

searchers in the late 1960s faced "phenom- large vendor committees, according to or voice information system. Using a highenal philosophical differences." Essential- Wickizer. But MUMPS "was standardized speed data link, the voice information is re-ly, differences were between proponents of as a grassroots petition process. It's the layed to the TIS -the textual informationmain-frame centralized systems that rely only computer language to have gone system. This part of the system includeson the batch process of punching cards through that route." character and graphic display terminals,and compiling data and those who wanted And, he continues, because the push for and users can access other hospital infor-a system to provide more contact between MUMPS' ANSI standardization came from mation systems or the IARS (image ar-the user and the program as it is written, the grassroots level - the users them- chival and retrieval system) of the radiolo-With the development of the CRT (cathode selves -the language has the potential of gy department. The IARS then is linked toray tube), programmers began having more being much more responsive to needs than image display terminals in the radiologydirect contact with the computer program. the languages first developed by the larger department. And the model IRIS ultimately

40 EMB MAGAZINE JUNE 1982 0278-0054/82/0200-0040$00.750 19821EEE