Not merely a computerized medical information system, PROMISis a comprehensive approach to individual health care, from

research and education to overall management of health and illness.

IMedicalGuidance

and PROMISPeter L. Walton

Roert R. Holland

Lawrence I. WolfUniversity of Vermont

Health care for an individual is the aggregate ofmany decisions and actions, each requiring knowl-edge of facts and of how to go about solving prob-lems. A health care guidance system can provide thisknowledge as decisions and actions occur. This arti-cle deals with the fundamental process of makingdecisions and how this process can be incorporatedinto health care guidance. PROMIS-the ProblemOriented Medical Information System-has been de-signed and implemented using these ideas about gui-dance.The Problem, Oriented Medical Information Sys-

tem began with the paper-and-pencil problem-oriented medical record.' But PROMIS is not simplya record-keeping system, nor is it only a computerizedmedical information system. PROMIS is a compre-hensive approach to health care, from research andeducation to overall management of health and in-ness, with emphasis on the individual's role in hisown health care.2

The decision-making processand stepwise guidance

The decision-making process shown in Figure 1 in-volves weighing data, values, and options. Thesedetermine the context of the decision-the more thatis known about patient, provider, hospital, and com-munity, the more every patient's health care needscan be recognized and accommodated.Data about a patient describe the individual, his

health, and his care, including previous decisions andactions and their results. These data make up the

health record and include, for example, the initial pa-tient history and physical examination, the list ofproblems requiring management, and notes describ-ing the progress of each problem. The completehealth record characterizes the uniqueness of the in-dividual.Personal and social values to be considered include

those held by the patient, the care provider, and thecommunity in which they live and work. The effectsof the patient's and the provider's values on decisionsare apparent-a patient's reluctance toundergo a cer-tain treatment, for example, will alter the context ofthe decision about that treatment. Less apparent,however, is how community and societal values aremanifested. These are hidden in such things as FDAdrug approvals, licensing of care providers andfacilities, office fees, reimbursement practices, and ahospital's spending decisions. Hidden in the questionof office fees, for example, are both patient and pro-vider attitudes about the value of health care.Options are drawn from knowledge about health

problems and how they are solved. Options vary fromdecision to decision, depending not only onwhat is be-ing decided but also on how the decision is made.Data on the individual's unique characteristics helpsort out the possibilities.Medical decision makers must weigh all three com-

ponents-data, values, and options. The process inFigure 1 is applied iteratively in making a health caredecision. Decisions are decomposed into smaller,more manageable ones, permitting a series of suchsmaller decisions instead of a single, large one. Byorganizing these stepwise decisions into a structure,a hierarchy of interlinked decisions emerges and ex-plicit health care guidance results.

0018-9162/79/1100-0019$00.75 1979 IEEE 19November 1979

PROMIS

In PROMIS the decision-making process is explic-itly defined by a structured set of displays presentedon a touch-sensitive terminal. The informationoriginator-patient, provider, other health worker-interacts directly with the terminal by making selec-tions from the options presented on the displays. Useof this branching network of displays integratesvalues, knowledge, and patient data in arriving at adecision or carrying out an action. This directed net-work currently uses over 45,000 frames and tables tostructure health care decisions and actions. As heuses the system and makes selections from the ter-minal screen, the user generates entries to the patientrecord. These entries may be orders specifying ac-tions to be carried out, reports of data collected, theuser's assessments of the situation, or values of thepatient or provider stated as goals, objectives, andpriorities. The logic for an entry is preserved by thestructure of the record. The user must specificallystate that the entry is to be added to the record. Onceadded, the entry is immediately available to thosewho need to know or act on it. For example, the phar-macy is notified about a new drug order so that it canbe checked against the patient's complete recordbefore being filled.3The user's interface with the system is a high-speed

(307,200 bits per gecond) CRT terminal with a touch-sensitive screen and typewriter keyboard.4 Ter-

minals and other peripheral devices communicatewith the hardware base over a cable TV-type systemusing mass-produced CATV parts and speciallydeveloped modems to convert between serial digitaldata and modulated VHF carrier signals.5 The hard-ware base is a network of minicomputers, connectedvia a similar CATV bus. Each node in the network in-cludes a Sperry-Univac V77-600 minicomputer with16-bit words plus parity, 800-nanosecond cycle time,and 256K-word central memory; three Control DataCorporation storage module drives with 300 mega-bytes per spindle; and peripherals. (All hardware isavailable from commercial sources.) The applicationsoftware is written iirPPL-PROMIS ProgrammingLanguage, designed specifically for thePROMIS ap-plication. It allows a large data base to bemanipulated in a highly interactive environment.PPL is a procedural language with embedded datadescription and manipulation capabilities.6'7 The ma-jor application programming areas are shown inFigure 2.Four major principles underlie the design and

development of PROMIS as a system for guidance inmedical decision making: the focus must remain onthe patient as an individual; the system must accom-modate a network structure of information; guidancemust be maintainable; and guidance must be adap-table to different health care settings.

Requirements for guidance

Certain requirements must be met to implementexplicit, stepwise guidance. Rapid and accuratemanipulation of two large data bases-the individualpatient data and medical knowledge data-is essen-tial. These data must then be synthesized to choosean option. And when acted upon, it often requires thecoordination of many people. The stepwise decision-making structure in PROMIS defines which data,values, and options are required at each step for everymember of the health care team. This is accomplishedby taking a comprehensive approach to making suc-cessive approximations, bringing the system closerand closer to complete, coordinated care for the in-dividual. The system must meet the followingcriteria:

(1) It must be a defined system. This means that itis explicit, i.e., the options to be considered at eachdecision-point are explicitly stated. The system mustbe flexible and not be defined by fiat, no matter howwell-meaning.

(2) It must allow for audit and correction ofper-formance. Auditing performance is essential in mak-ing sure that the process in Figure 1 is carried outwithin the limitations of current values, information,and capabilities. Actions decided on must bejustifiable in these terms. Without such auditing ofperformance, patients cannot be assured of gettingthe kind of care that the system is capable of pro-viding.

COMPUTER20

(3) It must allow for audit and correction of itself.After actions taken and results of those actions havebeen evaluated, the system can be modified to meetthe current understanding of how Figure 1 should beimplemented. This potential for modification appliesto all parts of the system. Medical knowledge may beupdated; distribution and training of health care per-sonnel and resources may be altered; decision-making strategies may change. Even the structure ofthe system itself may need to be changed.

In this presentation, major emphasis will be placedon the first criterion, a system defined for flexibility.Auditing and correcting performance and auditingand correcting system characteristics are equally im-portant and have been discussed elsewhere.2'8'9To meet these criteria, four major capabilities are

necessary: memory for data, options, and the deci-sion-making process; coordination of decisions andactions among care providers; preservation of thelogic for decisions and actions; and feedback formanaging and improving the health system. (SeeWeed and PROMIS Staff2,9 for further discussion.)PROMIS is designed to provide these capabilities.

Patient-care guidance

The first level of guidance for individual care con-sists of four medical actions (see Figure 3a). The firstis collection of a Data Base; from this the ProblemList is formulated; for each active problem InitialPlans are written; and each problem is followed inProgress Notes.The Data Base is a defined set of procedures

designed to detect whether something needs atten-tion. A problem that may require active attention ormanagement occurs with its own particular "con-

stellation" of symptoms, physical findings, and othermanifestations. The Data Base procedures seek prob-lems by way of their constellations. Indicators ofdisease are sought, not disease itself. Once these in-dicators are recognized, they can be worked up to seewhat may be behind the abnormalities. Known diag-noses are of course reported if uncovered while collec-ting the Data Base, particularly the patient history.(This medical notion of Data Base should not be con-fused with the more common concept of data base,literally a collection of data organized for computerprocessing. In this sense, the data base for a patient ishis complete health record, the "data" in Figure 1.The Data Base, on the other hand, is a medical actionand thus is one of the "options" in Figure 1).The Problem List is formulated from the findings

of the Data Base. It states each problem discovered,i.e., each indicator of disease, in terms of current dataand understanding. The Problem List is a usefulabstraction of the patient record, serving both to pro-vide rapid recall of the major events and to organizemanagement and data. A problem can be active or in-active. Active problems are those requiring somelevel of management, from simple periodic monitor-ing to full diagnosis and treatment.

Initial Plans are the first approximations to themanagement plan for each active problem. The Ini-tial Plan requires careful thinking about the problembefore more specific action begins. It sets forth a ten-tative overall management plan for the problem, in-cluding goals, low-power (contextual) looks, monitor-ing, and more specific work-up and treatment. Itshould include contingency plans for situations thatmay arise later, such as the development of vomitingin a cancer patient on chemotherapy.Progress Notes comprise the follow-up for each

problem and its plan. Follow-up includes collection ofdata, carrying out orders for specific actions, assess-

November 1979 21

ment ofwhere things stand with the problem, and ad- mention of each step is possible. More detailedjustment of management plans. discussions have been presented elsewhere.2'9'10Both the Initial Plan and Progress Note, then, en-

compass problem management planning. An under- Step 1: State aims forproblem management. Aimsstanding of the eight steps in planning (see Figure 3d) include goal, objectives, priority, coordinator, andis essential for an understanding of the PROMIS limits. Aims are not static but require constant revi-guidance system. In this article, however, only a brief sion as the situation changes. Clear, up-to-date aims

Cxx, lx;111 Cx, Mx; . FiO30 00-000-0...

-4 Phases of Medical Acti--------- --------------RETRIEVE ------- ------- ADD TO: ----

-Data Base -Exp- -Data Gas -Exp-

-Problen List -Problem List -Exp-

-Initial Plant -Initial PlIat -Eop-

-Pr-grxss Notes -E,p- ;-gressNotes

-Other retrievals -Other Actions

-F1 oshet retrievels -Elp- -Emergeno- Managesent -Esp-

-traph retrievals -Eep- -Consult repqI -Attnding note

-To printer -Audit

-ECbose ether eard Iother -unotions -Choose other patient on this wardeter L. Walt_--------------.___________________------------64.101----

Cr;; Sen Review Erase I -Opts- -nfirm -Fred- Orient Retrieve

C.x. M.III CEx, Mnx.

--------.Add tn Data DS.-----

-I. Data to col-let -n dmiosi-n

-2. Patient's sickness I(m"oJr coplaints.) -Esp-

-3. HNxlth tore profile -E,p-

-4. Soc-i profile -E.p-

S. History data b.s.: -Exp- -q..sti-nnaire

-6 Physi-al exam data base -Ep-

-7 Laboratory dta base -E,p-

S. Prosent illness:-E,p- -begin -ee PI

'eter L. Waltt----------------------------------------Pcas Sen Ret/Add Review Erase I -Opts- Confirm -Fred- Or

N 0 000-000-0...

-other dditions

-add to exiotong P1

-Next Frame-._____-----.64R131----i-nt Retriev

Cxx, lo .Iil Cxs. 30000-000-0... Initial Plwn

---------------- Active Problems for Initial PI.. -----------------------------

HosPitalilatiun 8/27/79

- I Anemia, chronic, hypo-hromic, micro-tic 8/27/79satisac tory stayseg same.Z t":lNY 81~~~~~~~~A27/*- 2 F/H diabetes mellitus.79

3 Drug allergy to penicillins. 0/27/79-T 4. H-da-he. /27/79

satisan tnrg & fluctuating.- S Abdominal pain, left lower uadrant of abdomen. /27/79

satisactru & GETTING WORSE.

Peter L Walt ----.---- .-Eras en Review Erase I -Opts -Frad- Orient

Cs" M-x . I 1. Anemia, chronic, hyp-ochromic, micro.yti-o0-000-0 ... Initial Plan I Anemia chronic, h .pohromic, icrosytic..----------------Initial Plan fur Ds Entit% or Pathophys Abn ------------------I. State Aims for problxe management -E,p-

-2. Cheok hoe preblee .ay be -cttribotieg to patient's 'sickness0 -Eep-

-3. ChRok for efects./disabilities prodoced by problem -EDp-

-4. Check function/status of syotees that eag be involved With problee -E,p-

-S. As.ess and fell ncowrso -E,p-

-6. Ivestigate problem and its etiolega -Eep-

-7. Watch for/prevent co-plications of proble -E,p-

-A. If indicated, institthe and momitor treatment -Exp-

-Em-rgency m.nagement -Choose an.other problem

-Next Frame- -Rext---3H5. I7--- eter L Walt.--------------------------------------------------------IA

Retrieve Ers Sen Ret/Add Review Erase I -Opts- Confirm -Fred- Orient

t Frame-.17I----R.trien-

COMPUTER

NWRMN moons

22

are essential in keeping a broad context within whichto make decisions.Step 2: Check how problem may be contributing to

patient's sickness. The patient's sickness is what thepatient perceives as wrong with himself. This step in-tegrates the patient's perception into the manage-ment of the problem.Step 3: Check for effects/disabilities produced by

problem. A problem disrupts life in different ways indifferent individuals.Step 4: Check function/status ofsystems that may

be involved This is a "low-power" look at what's go-ing on in the major body systems that could be in-volved with the problem.Step 5: Assess and follow course ofproblem. This

requires regular collection of key indicators of theproblem's current state.Step 6: Investigateproblem and its etiology. This is

diagnosis, including checking current problems andtreatment for possible etiological explanationsbefore beginning more extensive work-up.Step 7: Watch for/prevent complications of prob-

lem. Indicators of serious consequences of the prob-lem are looked for and, if found, added to the ProblemList and managed as additional problems.Step & If indicated, begin and monitor treatment.

Special note should be made of the phrase "if in-dicated." This is an important reminder about con-text-treatment may not be appropriate for thisproblem in this individual.

Plan guidance is an example of how a complex deci-sion-planning the management of a problem-canbe simplified by being broken into smaller, stepwisedecisions. The eight steps presented above are thefirst set of options in planning problem management.Each of the steps also represents a "large" decisionthat requires further steps; thus each branches to aframe that presents the next level of options. Theresult is hierarchically structured, stepwise guidancefor health care. One of the eight steps-Step 6, in-vestigating etiology-will be pursued in more detaillater.In providing guidance of the sort needed for health

care, certain steps are mandatory. Relevant informa-tion must be captured and organized. A guidancecapability can then be built on knowledge derivedfrom this information base. This latter step will notbe dealt with here; instead we will focus on capturingand organizing the information needed to build theguidance capability.

Capturing knowledge

Facts are the basic units of medical knowledge. Afact can simply state a relationship between entities;for example, iron deficiency causes a decreasedtransferrin saturation. A fact might go on to describe(i.e., qualify) the relationship in greater detail:transferrin saturation is always less than 16 percentin iron deficiency (see Figure 4). By stating relation-ships, facts provide a means for structuring medical

knowledge. The structure can be viewed as a networkof relationships. In qualifying relationships, factscapture the content of medical knowledge.Facts are the fundamental data base of medical

knowledge and are structured in ways that facilitateaccess to the data base. For example, the fact inFigure 4 is called a "cause fact." Cause facts state anetiologic relationship between a predecessor (irondeficiency) and a successor (transferrin saturation)when the predecessor is considered sufficient to ex-plain the successor (i.e., iron deficiency "causes" atransferrin saturation of less than 16 percent). Prede-cessor and successor pointers express the direc-tionality of a relationship, but do not always ascribetemporal or causal properties to it. These propertiesdepend on the kind of relationship-i.e., the relation-ship may state cause and effect ormerely a predispos-ing condition. Predecessors and successors are usedto define links which the system traverses to accessthe knowledge pertaining to a particular entity.Fact entry is controlled in a way similar to patient

record data entry. The user-in this case a factbuilder-sees displays at a touch-screen terminal. Byanswering questions and selecting the next step in aseries of actions, the builder is guided through anevaluation of the new fact against information al-ready in the data base and any further informationneeded to know how to connect the new data with theold. The display sequence is controlled by frames.A frame is a data structure which contains text and

control data specifying what to display, how todisplay it, what other associated data to display (e.g.,the name of the problem being worked on), and whatactions to take when a choice (i.e., a selectable text

),g

Fact 135C00303 cause fact table

A transferri. saturation cf less than l16. "uit" an average of 7.) ita anifestation of iron deficien-g that is aluagt present

FormPr ct- Su

Su: transferrin aturation, <167Codes:

Pr iron defi,i.ncy C127 00700DSc: tran ferrin saturation C121.048143

Flagt not audited; pIS.IPr3 = liRef t

Clinical HenatologtWintrobe M11 et al, Edt.Philadelphi:dLea and Febig-r 7th ed. 1974

Ch 16: Anemias charac terized b6 deficient hemoglobin tgnthnsi. andimpaired iron metabolism

pp 6Z1-634Process 134.1 Dependence fact fron uthority sourceBR: RR Holland MD-SP 1/17/79Dcc_mentation nut sodit-d.Ref_

Clinical HematologyWlintr-be MM, et al Eds.Philadelphia: Lea and Febijer 7th d 1974

Cb 17: Iron defieioncg and iron-ddficcienc aneniapp 635-670Laboratory findingspp 656-665

Process: 134.1 Dependence f-ct from authority sourceBy: tW Gilr RPb-SP1/-2/79Documentation not acditmd.

November 1979

a

I

23

field) is touched by the user. Frames are non-proce-dural programs interpreted by a routine called theDISPLAY ENGINE. They are modified and partial-ly compiled by FRED, a data structure editor (seebelow).The frames form a control structure which se-

quences the displays and encodes the choices made,accumulating them over many selections and thenpassing them as inputs to programs that manipulatethe data base. These programs implement thestorage and retrieval functions necessary to supportfact entry. Some programs run interactively, takingan action which is the next step in fact entry (e.g.,assigning the next free table to contain the fact andthen loading it with the appropriate fact template).Other programs are scheduled to run independentlyof the user (e.g., building a frame defining an entityfrom the facts entered about it).Entry of one fact may require entry of additional

facts before that fact can be completed. For example,the facts defining iron deficiency and transferrinsaturation must be in the system for the fact inFigure 4 to be complete. The frames support thisrecursive requirement, indicating that the currentprocessing context should be saved as part of re-entering the fact entry sequence. Popping out of thesequence automatically restores the previously ac-tive processing context, and the original fact's entrycan continue. The mechanics of saving and restoringthe user's frame state are transparent to the factenterer, who, having found that a needed piece of in-formation is not available, adds it to the data base.Facts have a fixed format with variable length

fields. Each fact has a type and associated flags speci-fying the kind of information contained in the fact.These attributes indicate how the contents of the factshould be interpreted. Fact contents include stringsof text and pointers to related entities. In addition,documentation for each fact indicates who entered,audited or updated it, when he did so, which controlstructures he used, andwhat sources ofknowledge herelied on.Facts are one ofmany data structures manipulated

by PPL routines. PPL data are organized into ahierarchy of structural elements..Bits, numerics, andstrings are combined into variable-length logicalunits called sentences. A variable number ofsentences form a paragraph. Paragraphs are in turnorganized into sections (files). (Faor extensive discus-sions of PPL and its data structures, see Schultz andDavis, and Davis and PROMISStaf 6,7 Paragraphsare classified both as to their structural organization(the kinds of sentences contained, for example a facttable) and their content (how the data are used, suchas fact type).A data structure editor, FRED (originally

specialized for frame editing), manipulates facts andother types of paragraphs. FRED is table driven: thetype and form of data element, from-bit to paragraph,and how to display and update it, are-specified in a setof tables accessed by FRED. FRED also initiatesother actions for maintaining the data base ofparagraphs, particularly backpointing and indexing.

COMPUTER24

Paragraphs reference other paragraphs in order todefine display branching or use of an entity (e.g., anorder for transferrin saturation). To rationallymanage the paragraph data base, it is necessary toknow how and where each paragraph is referenced.This backpointing, or backlinking, is performed by aroutine scheduled by FRED.FRED also triggers the management of a set of

associative data structures, or indices, about theparagraphs. Indices are used to maintain and displayalphabetic lists of entities with various attributes(e.g, they are all drugs or alllaboratory tests). Indicesare used for many other purposes, including organiz-ing facts. Each pair of predecessor-successorpointers is used as a primary key for an index entry.Secondary keys include fact type and associatedflags. This index, together with its inverse keyed bysuccessor-predecessor, allows traversal of the factdata base, enabling efficient implementation ofretrievals defined in terms ofrelationships among en-tities within the data base (see Figure 5).

Guidance arrays

Given the knowledge base of facts, a guidancecapability can now be built into the system. But the

resultingguidance mustac odate individual pa-tient values and uniqueness. The approach toguidance implemented in PROMIS is illustrated bythe guidance array for investigating iron deficiencyas apossible cause ofanemia (see Figure 6). Itmustbekept in mindthat this example ofguidance deals withone of many possible causes, in one step of a struc-tured approach toproblem management fora patientwho may have more than just one problem. This onearray does not represent a complete approach todiagnosis.The guidance array in Figure 6 presents datawhich

could be used for deciding whether iron deficiency ispresent or absent. Each set ofdata has listed the typeof guidance available for it and the possible conclu-sions that can be reached. Not every approachrepresents a possible course of action. A high-riskprocedure like a liver biopsy has no place in the work-up for iron deficiency. (As much, ifnot more, informa-tion about iron deficiency can be obtained from a lessrisky bone marrow examination.) But ifa liver biopsyhas been done for other reasons, every effort shouldbe made to take advantage of its results in the con-text of anemia. Consequently, a liver biopsy is notdirectly orderable from this array, but reports ofprevious biopsies may be retrieved from the record.Other procedures, such as transferin saturation, do

.....e0. 1. Anemia, chronic. hypochroic, icrocytic.ecbrsey, eicrocYtic. I-vestig.te forc- e Ine deficiency R/O casse.------i--c-------------I-Ir d.fileic¶ investigati--------------------------

D.et UtiLity -E.p Pri-e Tine Side eff-cts-------------------1I----- R.I., for: ------lI--------lI------------I---------------

-HM -likely present 1 - - --h.coglobin or -li l absen t $4.00 10 minutos minimal

bematocrit II-rb.s m.r -absenc. $4.01) ID .cinctec miimal

I ~ ~ ~ ~~III-rbh indices -.bsence, likely $4.00 1 I day oeiml

present-stOol o.s.lt -likely present $3.00n 10 inutes none

bleed l l.iron -absent..itell $5.00 Z-S days mninil

-trasferrrie.- -ebett likely $18.00 2-S days minimalsatoraslom I present

-FlIosheet -Def-

-Neet F-ram M-or nhsP.ter L wl1ten---.-----------------------------.--------------------70. l6O-Ers S.n. Reviec Erase I -Opts-- -Fred- Orient R t

Ce. .. O 1. Anemi., chronic hpchromic, ni rocticchromin, eiesytic. Investiget. P0 sass Iron dePicien_ R/O case

------------------Iron deficienc: inestig ------------------D ta Utility Price Tim BiSde eff.ets

---------------1--- _Rules P ----------I.------------I-------.-------iron th.r-py -tlI sIenIt $IS. B 10 days I miniml

present l lI-cobelt -bsece Not -vailable at MCHV

-bon. marrow I -absence presence :$40.00 2 days i moderate

-liver biopsy -absence presence Not orderble. Pr this -ort-.p

. II

Pet:r L.Ers Sen R-i.. Erase I -Opts-

-Prev-chc- -Next Frame-----------------------.70.1680-2-Fred- Orient R.ti-

C.., n.. S 1. emia, chrmnic, hypshr-mic. micro.ytic, chroni, hyp-chroic. nicrcytic. nvstiga.t for cease I Iron deficim..y---------Ive-tigte for Iron deficiency: trans.frrinsatur-tion-----------

1. Obtain a -serum iron nd -total iron binding cpacity.

tr.nsferrin at-ratiis I. -oser-. iron X 100/TIBC

-2. Interpret rsesIts

Peer LEras gem

)(

-Nect Frame-WtI.1 - ---06. 1309--

Re-ies Erase 1 -Opts- -Fred- Orient

Interpretation 135.0035SIF tranp.errin ta..ration Pt > 16 X THEN

Iron dofiol.ncy is ab.ent.OTHERWISE IF < S X THEN

Th. litlihed of i-n deficien.y is very high.OTHERWISE

Thb.findinns. re o.nsistent !ith bat eot diagnostic of iron deficiscy.

November 1979 25

have a potential place in the work-up and so areorderable as well as retrievable. For those approachesthat are orderable, the array lists approximate prices,turnaround times, and potential side effects. This in-formation is needed in decidihg what action to takenext.The array offers guidance for how to investigate

the presence or absence of iron deficiency, but is notdesigned to guide in the decision of whether to in-vestigate for iron deficiency. The decision of whetherand when to look for iron deficiency is made prior togetting to this array, and is dependent upon the en-tire context, including aims, other problems, prog-nosis of cause, etc. But the array can be used in theway outlined below.

First, before deciding whether to investigate foriron deficiency, the user can retrieve a flowsheet-atabular presentation organized by time-of the datarelevant to iron deficiency already in the patient'srecord. He can do this by selecting the "choice"flowsheet on the array (Figure 6a). Using these dataand the guidance offered in the array, he tries toestablish the presence or absence ofiron deficiency. Ifthis cannot be done, then he tries to establish whetheriron deficiency is likely tobe present or likely tobe ab-sent, again using data already available.At this point, either iron deficiency has been ruled

in or out, or its presence or absence is still uncertain.In the former case, the only thing left to decide iswhether to ascribe the anemia solely to iron deficien-cy or to keep looking for other possible causes. The ar-ray does not help with this decision. If the diagnosisis uncertain, a decision must be made about whetherto take any action to determine the presence orabsence of iron deficiency. This decision is based on anumber of factors: goals, speed with which diagnosismust be made, prognosis ifcause is present, andwhatdegree of definiteness is needed and what degree hasalready been attained from existing data.

26

The array's helpfulness depends upon a clearlydefined purpose for proceeding. One can rationallydecide to do costly and risky tests to rule out adiagnosis only if one has already decided that it is im-portant to rule the diagnosis in or out. With a clearlyunderstood purpose in mind, one can use the array toguide the decision on the next step. Information ontime, risks, and so forth is also available for this deci-sion. If, for example, there is no time constraint onfinding a cause, then one could decide to follow thecourse over time to see what happens. In other con-texts such a decisionmay be inappropriate, for exam-ple where the cause being investigated is leukemia.Here speed and definiteness can be very important.Other causes may fall in between: time is a factor, yetthe cost and risk of definiteness are not acceptable atthis point. The decision might then be to collect datafor which the cost and risk are less but which willallow discrimination only at the "likely present" (or"absent") level. Any decision about definitely know-ing presence or absence would be postponed untilthese data are in.

The ingredients needed to offer computer-basedhealth-care guidance are now all in place. Knowledgeis captured in atomic form in the facts. Facts areorganized using a directed network. The networkfacilitates understanding and use of the facts to offerguidance. Building of this guidance capability is donein an explicit and rigorous fashion. Each step in theprocess of transforming knowledge to facts toguidance is documented and connected in electronicform to make both guidance and knowledge main-tainable and adaptable. And everyday health careguidance for an individual occurs within a structured,stepwise approach to decision making and problemsolving. Structured guidance is designed to accom-modate individual values and uniqueness, puttingthe patient as the focus of the system. Audit ofperfor-mancewithin the system has also been approached ina rigorous, documented fashion by building a gui-dance structure to audit for the important behavioralcharacteristics of thoroughness, accuracy, analyticsense, and efficiency.8 Audit of the system itself (seeFigure 7) is possible by applying specially developedsoftware on a large population ofpatient records.11 Inthis way, the knowledge used in building theguidance capability can be validated.10 Thus, asknowledge and the understanding of how to act uponknowledge evolve, so too can PROMIS evolve. U

Acknowledgments

We are indebted to the entire staff of the PROMISLaboratory, past and present, without whomdevelopment and implementation of the ideaspresented here would have been impossible. Ourmost directacknowledgment is to the people workingwith us daily to solve the problems described in thisarticle. Our daily interactions with Carolyn Barnes,Ann Buller, Brian Ellinoy, Genevieve Gilroy, and

COMPUTER

Pati Naratomi have led to the development, expres-sion, and refinement of the ideas concerning buildinga system for health care guidance. The contributionof Stuart Graves has been especialy significant inthis regard.This work has been funded primarily by the Na-

tional Center for Health Services Research, Depart-ment of Health, Education, and Welfare, under Con-tract Nos. 230-76-0099 and 233-78-3011.

References

1. L. L. Weed, Medical Records, Medical Education andPatient Care, Cleveland, Case Western ReserveUniversity, 1971. (Distributed by Yearbook MedicalPublishers, 35 Wacker Drive, Chicago, IL 60606.) Anintroduction to the problem-oriented system, especial-ly the problem-oriented medical record.

2. L. L. Weed, Your Health Care and How to Manage It,Essex Junction, Vt., Essex Publishing Co., 1978.(Distributed by PROMIS Laboratory, MCHV-MFU,Adams Residence, Burlington, VT 05401.) Describesthe use of the problem-oriented system to enable pa-tients to manage their own health care. Introductiondeals with why patients must be involved. Chapter 20presents a set ofpremises for a health care system. Ap-pendices 4 and 4a illustrate many more of the guidancedisplays and explanations. Appendix 7 discusses therole, of trustees in the management of the health careof others.

3. G. Gilroy and B. J. Ellinoy, "Integration of Pharmacyinto Computerized Problem Oriented Medical Infor-mation System (PROMIS)-A Demonstration Proj-ect," AmericanJournal ofHospitalPharmacy, Vol. 34,1977, pp. 155-162.

4. J. F. Wanner, PROMIS Terminal Specification, PRO-MIS Laboratory Technical Report (available fromPROMIS Laboratory). Specifications for the high-speed, touch-sensitive CRT termninal used to interfaceusers with the system.

5. J. F. Wanner, "WidebandCommunication System Im-proves Response Time," ComputerDesign, Dec. 1978,pp. 85-91. Discusses the communication system com-ponents, performance theory, and operation.

6. J. R. Schultz and L. Davis, "The Technology of PRO-MIS," Proc. of IEEE, Vol. 67, No. 9, Sept. 1979.Discusses hardware, system architecture, PROMISProgramming Language (PPL), run-time environ-ment, data structures, and system performance.

7. L. Davis and PROMIS Laboratory Staff, FunctionalSpecification of a PROMIS Instance System, PRO-MIS Laboratory Technical Report. Tobe publishedbyNational Technical Information Service (NTIS).Detailed specifications of the display network, tables,patient record structure, programming and framelanguages, application software, file system, nodemanagement system, and network communication.

8. L. L. Weed, "A New Paradigm for MedicalEducation," in Recent Trends in Medical Education,Report ofa Macy Conference, Josiah Macy, Jr., Foun-dation, New York, 1976. Discusses educational im-plications of PROMIS.

9. PROMIS Laboratory Staff, " 'Representation ofMedicalKnowledge' and PROMIS,"Proc. SecondAn-nual Symposium on Computer Applications inMedical Care, Washington, DC, Nov. 1978, pp.

November 1979

368-4Q0. Includes display sequences, discussion ofsome tools used by builders, frame data structures, ex-amples of flowsheets, and responses to criticisms ofcomputerized guidance systems for medical care.

10. R. E. Esterhay, Jr., and P. L. Walton, "ClinicalResearch and PROMIS," Proc. Third Annual Sym-posium on ComputerApplications in Medical Care (inpress). Discussion of application ofPROMIS guidanceto treatment protocols for clinical research by theBaltimore Cancer Research Program, NationalCancerInstitute.

11. S. R. Reynolds, PROMIS Laboratory, unpublisheddocuments.

Peter L. Walton has been with PROMISLaboratory since 1974. After a rotatinginternship at Pennsylvania Hospital, heserved for twoyears in the Public HealthService, assigned to the National Centerfor Health Services Research. WaltonreceivedanAB from Dartmouth Collegeand anMD from the University of Penn-sylvania.

Robert R. Holland is employed as anemergency room physician at NorthCountry Hospital, Newport, Vermont.After a rotating internship at St. Luke'sHospital, Fargo, North Dakota, heworked with the PROMIS Laboratoryfrom 1973 to July 1979. Holland re-ceivedaBAand anMD fromthe Univer-sity of Vermont.

Lawrence I. Wolf joined PROMISLaboratory in 1976. Since then, he has'been the software speialist within thePROMIS medical content group, creat-ingand untangling the networks used tocapture medical knowledge. Wolf re-ceived a BA in language and commu-nication from Hampshire College and anMS in computer andinformation sciencefrom the University of Massachusetts.

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