Clayton, Paul D.

Pryor, T. Allan

English

The University of Utah: J. Willard Marriott Digital Library

Test Ordering and Medical Decision Making: A Synergistic Relationship By T. Allan Pryor, Paul D. Clayton ann Keith G. Larsen H lstorically, the development of hospital information systems has tended to emphasize either adminis-trative or clinical applications. Ad· mlnlstrative systems have concen-trated on implementing the admit-discharge-transfer function, auto-mating test ordering, capturing patlant ho::Jpital charges and compiling finan-cial requirements. Clinical systems have concentrated on capturing and re<;ordlng clinical data on patients served by the system. In the develop-ment of both system&, however, little has been done to Implement auto-mated decision processing. The Health Evaluation through Logi-cal Processing (HELP) system de-veloped over the last 15 years at the LOS Hospital in Salt Lake City, Utah, has combined the administrative and clinical attributes of traditional hospi-tal information systems with a general-Ized decision structure available to users for making on-line medical decisions. The majority of the medical decisions defined in the HELP system are data drive. That is, the criteria Is automatically processed as the data associated with the decision are captured and stored by the system. This feature ensures that the recipient of the information may review the most current decisions with minimal interaction with the system. While many of the decisions written for HELP concern the medical evalu-ation of patients, decision processing can be added to any function Incor-porated In the system. In particular, ordering drugs and procedures, such as laboratory tests and x-rays, provide opportunities to create decision logic that controls the order arid suggests clinical decisions. In the data driven mode, the order is the data that trig-gers the appropriate decision criteria. Figure 1 illustrates the ge11eral mechanism followed in the HELP sys-tem for the processing of an order. When the order is completed, it is stored in the patient data base and that input triggers the decision pro-cessing. The information processed MEDICAL KNOWLEDGE DATA BASE PROCESS DECISION REPORT ORDER ~ AND CRITERIA DECISION RESULT ENTER ORDER ~ PATIENT DATA B4SE Figure 1. 36 Includes the order, other patient data base Items, and medical knowledge pertinent to the specific \Uder. Fol-lowing the processing, the order-together with any decisions-Is for-warded to the appropriate locations. Interrelation of Medical Logic One example of the Interrelation of medical logic In the HELP system used to enhance test ordering and re-porting occurs in our radiology sub system. In developing the sub system, we wanted to automate the order, and ensure that clinically pertinent radi· ology findings from the patient's x-ray could be easily entered into the computer data base. The goal resulted in two major constraints being placed on the subsystem design. The first constraint was that the radiological findings had to be coded so that the stored data could be reviewec.' by clini-cians and analyzed by medical deci-sion logic. The second constraint was that the system had to be unobtrusive to radiologists so that they Wt)uld readily cooperate in entering findings into the computer. We found that the salient Infor-mation in a large majority of x-ray examinations can be repc.,rted with a limited list of possible findings. We further realized that for an Individual patient with a specific ordered pro-cedure, we could narrow the list of possible findings to the five most likely by using 9atlent-specific data that was already part of the compu-terized data base at the time the ex-amination was ordered. These reall· zatlons led us to conclude that t'" 3 radiologists could conveniently report pertinent clinical results by selecting from an individualized list of likely findings for each patient. To imple-ment this convenient coding scheme, . we used HELP-based medical decision logic as part of the ordering process, so that the list of likely findings was appropriate for each Individual patient. Requests for radiological examinations are processed In the November/December 1982 following way. At-er the ward clerk has entered the patient number, or-dering clerk and requesting physician, the five most recent x-ray procedures ordered for that patient are displayed. This Information reduces duplicate or unnecessary examinations. If this re-view does not alter the request, the clerk selects from a menu that lists 13 general categor.ies of examinations. These categories range from chest x-rays to ultrasound, computerized tomography or nucle~r medicine. Choosing the general category of chest examination causes the next menu to display the common specific procedures for that category. The menus are used to obtain the reason for ordering the examination. The first question describes the general cate-gory such as postoperative, pre-operative, follow-up known disease, symptoms only, and so on. The follow-up menu displays a list of common reasons for ordering the specific, previously ordered procedure. The selected reasons are coded and stored as part of the order string and are extremely important in the d$Cision logic used to predict the most likely findings of the requested procedures. Free text may be entered to describe unusual examinat!ons or reasons for ordering certain procedures. Requisition Report · When the ward clerk has answered additional questions concerning time and patient transportation, the com-puter prints the paper work necessary to perform the examination and the requisition report form (Figure 2) that contains the Individualized list of most likely findings for the requested procedure. In addition to the list of likely findings, the requisition report form gives other clinical data, such as admitting diagnosis and surgical pro-cedures that may be of interest to the radiologist. The system includes prewritten medical logic to predict the likeli-hood for 357 separate radiological findings. Whenever a chest exami-nation Is ordered, the logic sectors for 45 pertinent findings are evalu-ated and the five findings with the highest likelihood for that patient are printed on the requisition rej.lort fo• m. The logic for the likely findings Is based upon Indicants that are found to be slgnifit:antly correlated to a specific finding. Such Items include age, sex and other clinical data In the Comput4nln Healthcare I DIP All.,...,. Of IIADIOLOGY 4 S 1 1 1944 - ~~H2~~! lAohB•I43 - (101) 350-1791 I Patient: Age 38F CHEST 1 I Ooto To Bo Dono: 3Q 1168 B2 I RESPIRATORY O!STRESS Room 4530 Ha.pltatlll157574 Rocliolog~ I 13-35-601 =WEIST, JOHN • HARRIS, ALVIN E. Colnp<dor Entry: 29 MAR 82 11:28 B~ DWX Aclmlnlng DIOV-10: TRAI.MAS Radiology Report Summary•' ,.,,TI .. .. •~c•uJt<l UUMCI ...... ·-··· s.. ••• ~ ...... """ ... :-.::~~ ~.~.: Olio"'"' ·-, ...... -·· CHEST1 s .... u NO SIGNIF,CANT ABNORIIAALITIES A 1 2 3 4 5 7 i 10 11 HYPOAERATIONIATELECTASIS LIR B 1 2 3 4 5 7 10 11 PNEUMOTHORAX c 1 2 3 4 5 7 10 11 PULMONARY EDEMA D 1 2 3 4 5 7 10 11 PLEURAL EFFUSION UR E 1 2 3 4 5 7 10 11 OTHER F 1 2 3 4 5 7 10 11 G 1 2 3 4 5 7 tO 11 H 1 2 3 4 5 7 10 11 I 1 2 3 4 5 7 10 t1 J 1 2 3 4 5 7 10 11 K 1 2 3 4. 5 7 to t1 L 1 2 3 4 5 7 10 11 M 1 2 3 4 5 7 10 11 •• T .. oondlt._ priniOd obovo "'" olmpl~ c:omputar colculotod _.ltlllltloo dotormlnod pnor 10 t"" •omlftltlon. Tttoy - rodlologlc lnterpr.,.tlono ONLY II tllolr ca-dlng box• .,. chocked. S. text - "" _,., _.. Figure 2. computer. The most important item is usally the physician's reason tor or· dering the examination. The actual use of these indicants to predict the pre· sence of a specific finding is based upon sequential applications of Bayes' formula. The requisition report form listing the likely findings, goes to the radl· ologist's office where the film is read and the appropriate boxes of the likely findings are checked. Traditional de· tailed dictation is typed on the bottom of the st.eet and selected findings are entered into the computer. Once the information is entered into the com-puter, it is available ior review and use in furii1er decision-making efforts. Billing Information and management statistics are derived when the radi-ology technician confirms with a bar code reader that the examination was performed as ordered. By providing the radiologist with a limited patient-specific list of coded findings, clinical data is captured from this major source of ancillary tests. The radlologls ~s use a check-the-box format to report coded findings for more than 90 percent of the pro-cedures performed in our hospital. With our present level of decision logic, we are able to include all of the appropriate findings on the predicted list 73 percent of the time. An addl· tlonal25 percent of the examinations can be reported using key words In combination with the findings that appear on the list. Ordering Medications Another example of the Interaction between medical decision processing and computerized order entry Is illus-trated In the ordering of medications at the LOS Hospital. This action Is initiated by the physician with a written order on the patient's medical chart. A direct copy of the medication order Is given to a pharmacist, who Is then responsible for entering the order Into the patient's computer record. The order may be entered at a terminal located on the nursing station or at a terminal located In the pharmacy department. The order Information entered Into the medication profile includes the name of the medication, prescribed dosage, route of administration and scheduled times the medication should be given. As with most tradl· tlonal order entry systems, this Infor-mation is used for generating medl· cation labels, capturing billing Infor-mation, updating the patient's medl· cation profile and updating a narcotics utilization file. In addition, medical decision pro-cessing results are reported back to the pharmacist before the order Is 37 Flgure3. completed (F~gure 3). The computer de· cision-maklng criteria screens for al· lergy contraindications, drug inter· actions and drug contraindications as indicated by laboratory and medical diagnoses. The decision logic inte· grates the medication information with oth9r patient clinical data such as medical diagnosis, laboratory results, bacteriological data and so on. The output of the computer medi· cation screening is a series of recom-ll)endations concerning drug therapy alteration if indicated. These recom-mendations are displayed to the pharmacist immediately following the STATEMENT OF OWNERSHIP. MANAGEMENT AND CIRCULATION (Act of Aug·Jsl 12. 1970: Section 368~. Tille 39. United Slates Code). 1. Tille of Publication: COMPUTERS IN HEALTHCARE. 2. Date of Filing: September 30, 1982. 3. Frequency of Issue: 12 issues per year. (plus one extra.) 4. Location of Known Office Of Publication: 6430 S. Yosemite St .. EnglewOOd, Colorado 80111. s. Location oft he Headquarters or General Business· Office oft he Publishers: 6430 S. Yosemite St .. Eng1e0100d. COlOrado 80111. 6. Names and Addresses of Publisher and Editor. Publisher: Bill Childs, 6430 S. Yosemite St., Englewood, Coklrlldo 80111. Editor: Don Peterson, 6430 S. Yosemite St .. EnglewOOd, Colorado 80111. 7. Owner (II owned by a corporatton, its name and address must be stater! and also immediately there under the names Pnd addresses of the stockholders owning or holding 1 percent or more of total amount of sloe~. II not owned by a corporatiOn, the names and addresses of the individual owners must be given. II owned by a partner· Ship or othe-: unincorporated linn, Its name anti address, as well as that of each individual must be given.): Cardiff Communications, Inc. (owner), 6430 S. Yosemite St., EnglewOOd, Colorado 80111. Stanley M. Seatle (stockholder), 6430 s. Yosemite St., Englewood, Colorado 80111. Lorna K. Searle (stockholder), 6430 S. Yosemite St., Englewood, COlOrado 80111. Robert A. Searle (stockholder), 6430 S. Yosemite St., Englewood, Colorado 80111. V.L. Patterson,6430S. Yosemite, Englew00d,C080111. J. Millmore, Rt.1, Strove, OK 74079. 8. Known bondholders, mortgages, and other security holders owning or holding 1 percent or more :>f total amount of boft.:ls. mortgages or other securities: American National Bank. P.O. Box 900, Bristow, OK 74010. 9. For optional completion by publishers mailing at the regular rates (Section 132.12;, Postal SeMce Manual)· 39 l!.S.C. provides in pertin<orol part: "No persons who would have been entitled to mail mailers under former Section 4359 of this Iitle shall mail such mailers at the rates provided under this subsection unless he Iiles annually wilillhe Postal Service a wrilllin request for permission to mail maller at such rates ... In accordance with the provisions of this statute. I hereby request pe•mission to mail the publication named in Item 1 at the reduced P?Siage rates presenlly authorized by 39 U.S.C. 3826. (Signed) Bill W. Childs, PubliSher Average No. Copies Each la~ue During Preceedlng 12 Months A. Total No. Copies Printed (Nat Press Run) B. Paid Circulation 1. Sales Through Dealers and Carriers, Street Vendors and Counter Sates 2. Mllil SubecriptiOns C. Total Paid Circulation D. Free Dletrlbutlon by Man, Carrier or Othe-r ~s- Samptn, Complimentary and Other Free Copies E. Total Otllrlbutlon (Sum of C and D) F. Copies Not Distributed 1. Office UM, Left-Over Unaccounted, Spoiled Alter Printing 2. Ratuma From News Agents G. Total (Sum of E. F1 and 2 Should Equal 11,500 None 2.700 2,700 7,300 10.000 1,!500 None Actual No. Copies ol Single lssuv Published Nearest to Filing Dale 14,842 None 3,349 3,349 8,085 11,434 3,400 None Net Press Run 811own In A) 11,1500 14,842 I certify that the statement• mede by me above are correct and complete. (Signed) Bill W. Childs, Publisher 38 scheduling of the patient's medication orders. The pharmacist Is responsible for follow-up cf the recommendation with the appropriate member of the patient's health team. If the severity of the anticipated reaction warrants, the pharmacist will withhold the medi· cation until the patient's physician has been contacted. While this inci:fence is low, the potential cost to tt.e patients of un-recognized medication problems Is greatly reduced. Between three and five percent of hospital patients have an alert generated on a medication or-der during their hospital stay. In each instance the patient realizes a four to one cost benefit from the medication ordering system. To accomplish the same screening for medication compll· cations in a hospital the size of LOS pharmacists would have to review about 500 hospital charts per day. With the computer system, the phar-macists at LOS Hospital have been able to concentrate on the 15 to 20 patients a day for whom medication complications were ind:cated during the ordering process. Developln~ ileclslon Logic The unique ability of the HELP sys-tem to interact with traditional hospi-tal information functions and decision processing has been acco'Tlplished by providing a common integrated data and medical knowledge base, as well as by developing generalized user programs that provide an easy interface for creation and manage-ment of new data base Items, entry systems, decision criteria and for· matted screen displays. The availability of these user tools ensures that the system will remain dynamic, respond· ing Io changes In both the hospital data base and decision criteria neces-sitated by new medical knowledge. Figure· 4 illustrates the general model of implementing both the or-dering function and the creation of the decision criteria using the HELP system. The first step Is to define any new Items to be added to the existing data base. The format and units of measure of the new data Item, the key words to access the data, the cost accounting procedure number and the charge value are Included with the data description. Once defined in the data base, an Item may be used In the generation of entry questionnaires ana decision modules. November/December 1982 :, Worry-Free UPS Power ~rotection ~~ lt~s ridicul~us to design . · a fully-redundant, total pattent information system . ~ . and ignore .the power supply!'' Alex1an Brothers hospttals' total Palient Information System pro-, v1des doctors and nurses ' with immediate access to cltnical data. As sys-tem reliability is cnttcal. a 30 kW.LorTec UPS in'sures system opera-tion. 24 hour~ a day. Our's Works! LorTec UPS- 16 years of documented. worry"-free, UPS dependability But don't take our word • for it. Ask any of our cus-tomers. You' II find all their names, addresses and phone numbers inour hew "UPS Reliability" Discovery Kit. Or, call Tom()' Neill, Vice Presi-dent at 216/327-5050. Wayne West Director ol F 1etd Opera(lons . Alex1an Brothers oi:Amenca, Inc Elk Grove V1llage, IllinOIS , LorT~c UPS unit.s are available from 12.5 kW to 125 kW; 50 Hz and 60Hz. LorTec Power Systems, Inc. 5214 Mills Industrial Parkway North ~idgeville, Ohio 44039 report. In some instances, the system may be used In the laboratory to order tests, or it may be used on the ward when the user Is requesting review of such information. Range of Applications The ordering examples given above illustrates only some of the current in· teractlon between the medical deci-sion-making and order entry programs in the HELP system. Similar inter-action and decision logic is available for ordering blood gas, clinical labora-tory tests and so on. Research is cur· rently under way to make the system useful in determining the most likely set of test orders that should be re-quested in order to further the diag-nostic needs and management of a particular patient. As these appiications grow, the indi· cation is that hospital information systems of the future must be medi-cal decision systems wherein items of data originate from numerous medi· cal and administrative sources. 0 About the Au,hors Pryor Clayton T. Allan Pryor Ph.D., is a profes· sor In the Department of Medical Biophysics anci Computing at the University of Utah and is responsi· ble for the development of the HELP system at the LOS Hospital. He has been involved in the creation of medical computer systems for over 15 years at the University of Utah and LOS Hospital. Paul D. Clayton, Ph.D., is an associate professor in the Depart-Larsen ment of Medical Biophysics and Computing at the University of Utah and LOS Hospital. Clayton has been involved in the develop-ment of hospital computing and artificial intelligence. Keith G. larsen is a research Pharmacist on the staff of the LOS Hospital. For the past five years he has been involved in the develop-ment of computerized pharmacy systems at the LOS Hospital. ~ HOSRITAL INFORMATION SYSTEMS 40 SHARING GROUP a Announces its Winter Seminar on "HOSPITAL INFORMATION SYSTEMS: SUPPORTING SUCCESSFUL CORPORATE & MARKET STRATEGIES IN THE 80's" January 12-14, 1983, Scottsd~le Hilton, AZ For hospital administrators, marketing & planning directors, financial directors and information systems directors. • The Voluntary Hospital's Position In the 80s • Management Information & Decision-Support Systems for Healthcare Providers In the 80s • Corporate Hospital Structure: How & Why, Legal Aspects, Financial Considerations, MIS Needs 8 Marketing: How, Market Research Directed Toward Physician vs. ;>atient For further Information, contact John A. Tvedtnes, HISSG. 2415 South 2300 West, Salt Lake City, Utah (801) 972·6099. HISSG's principal goal is to promote the sharing of ideas, system des;gns, software implementation and management techn1ques. Membership is open to hospitals, vendors, consultants, and others having an interest in hospital information systems. INFO/CARD 18 November/December 1982 

Test Ordering and Medical Decision Making: A Synergistic Relationship

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Test Ordering and Medical Decision Making: A Synergistic Relationship

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The University of Utah: J. Willard Marriott Digital Library