ACAD EMERG MED       d   November 2004, Vol. 11, No. 11   d                                                ...
1238                                                                    Husk and Waxman   d   HOSPITAL INFORMATION SYSTEMS...
ACAD EMERG MED     d   November 2004, Vol. 11, No. 11   d                                                  ...
1240                                                                   Husk and Waxman   d   HOSPITAL INFORMATION SYSTEMS ...
ACAD EMERG MED     d   November 2004, Vol. 11, No. 11   d                                                  ...
1242                                                                Husk and Waxman   d   HOSPITAL INFORMATION SYSTEMSTABL...
ACAD EMERG MED     d   November 2004, Vol. 11, No. 11   d                                                  ...
1244                                                                    Husk and Waxman     d   HOSPITAL INFORMATION SYSTE...
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Using data from hospital information systems to improve emergency department care

  1. 1. ACAD EMERG MED d November 2004, Vol. 11, No. 11 d 1237 Using Data from Hospital Information Systems to Improve Emergency Department Care Gregg Husk, MD, Daniel A. Waxman, MDAbstractThe ubiquity of computerized hospital information systems, of detail that can be used to educate staff and improve theand of inexpensive computing power, has led to an un- quality of care emergency physicians offer their patients.precedented opportunity to use electronic data for quality In this article, the authors describe five such projects thatimprovement projects and for research. Although hospitals they have performed and use these examples as a basis forand emergency departments vary widely in their degree of discussion of some of the methods and logistical chal-integration of information technology into clinical opera- lenges of undertaking such projects. Key words: hospital in-tions, most have computer systems that manage emergency formation systems; quality improvement; emergency healthdepartment registration, admission–discharge–transfer in- services. ACADEMIC EMERGENCY MEDICINE 2004;formation, billing, and laboratory and radiology data. These 11:1237– are designed for specific tasks, but contain a wealthOver the last two decades, the microcomputer revo- FOLLOW-UP INFORMATION FOR EDlution has brought processing power and mass storage ADMISSIONSto the desktop of inexpensive personal computers.E-mail and Internet access, ensuring scheduled back- Although follow-up of patients admitted from the EDups of data, and the need to move data between is a requirement for residents training in emergencydifferent hospital information systems have catalyzed medicine,1 the logistics of doing so are often cumber-the networking of hospital computers. Standardiza- some, and, in our experience, residents and attendingtion of formatting of medical information using the physicians do not always follow-up on cases of educa-Health Level 7 (HL7) standard has facilitated sharing tional value. We have designed a quarterly report thatof information between different hospital information provides follow-up information for each resident andsystems. Middleware formats data and routes the data attending physician on every patient whom they haveto different applications and network destinations. admitted. This report includes the admitting diagnosis, Data from these ubiquitous information systems are the length of hospital stay, the principal dischargea rich source for study of a broad range of emergency diagnosis, the principal inpatient procedure, and thedepartment (ED) issues, both clinical and administra- patient’s discharge status (Figure 1).tive, for research and quality improvement (QI) For this project, the ED uses two monthly down-purposes. In this article, we give examples of quality loads. One file contains each ED patient’s demo-improvement and research projects we have per- graphic and financial information, date and time offormed using data commonly available in hospital ED arrival and discharge, up to two ED providers, EDadministrative databases. We discuss some of the diagnosis, and the patient’s disposition. The ED alsomethods and logistical challenges of implementing receives a monthly file containing all inpatient dis-such projects. Our facility is a multihospital network; charges, including coded diagnoses, procedures, andmost of the projects described use data from our the patient’s discharge status. These downloads areprimary site, an 894-bed teaching hospital with 65,000 imported into Stata (StataCorp, College Station, TX),ED visits annually. a data-management language, to manipulate the data. To combine information from the ED and inpatient monthly file, the medical record number and admis- sion date are used to match records. This approach has limitations. This system is passive and succeeds only if the individual cliniciansFrom the Department of Emergency Medicine, Beth Israel Medical read their reports and identify cases of interest. ACenter (GH, DAW), New York, NY. given patient may have more than one ED attendingAddress for correspondence and reprints: Gregg Husk, MD, physician or resident involved in his or her care but,Emergency Medicine, Beth Israel Medical Center, First Avenueand 16th Street, New York, NY 10003. Fax: 212-420-2863; e-mail: until recently, we captured only one attending cian and one resident in the data. Data entry person-doi:10.1197/j.aem.2004.08.019 nel did not always accurately enter the involved ED
  2. 2. 1238 Husk and Waxman d HOSPITAL INFORMATION SYSTEMS Figure 1. Selected cases from one resident’s quarterly report (identifiers redacted).resident. Most recently, we have changed the ED data for a number of stat ED laboratory tests (tracked tests)source to our ED information system, EmSTAT (A4 with the laboratory leadership. We drafted a dailyHealth Systems, Cary, NC), and each attending report for the laboratory manager, and modified thephysician and resident involved in the patient’s care report in response to her requests. The daily manage-is now reliably identified. Textual descriptions of ment report highlighted outliers for each of the studyInternational Classification of Diseases (ICD-9) codes tests, in a single high-acuity ED that cares for 30,000are not always meaningful to clinicians, e.g., ‘‘Chest patients a year and admits 9,000 patients.Pain NEC.’’ Nevertheless, we believe that providing We studied intralaboratory TAT from December 1,some follow-up information on every admitted pa- 2002, to March 1, 2003, the first half being a controltient seen by emergency physicians is of value—it period preceding the daily report. In the first half ofallows ED clinicians to review the patient’s medical the study, 2,097 ED patients had at least one trackedrecord if they find a mismatch between the ED and test performed, and during the intervention period,inpatient diagnoses. When our medical center imple- 2,049 ED patients had at least one tracked testments its clinical information system for its outpatient performed. The percentage of ED study tests thatpractices, we will be able to use a similar approach to met the 60-minute TAT standard increased from 88.8%provide follow-up for patients discharged from the in the preintervention period to 95.3% postinterven-ED. tion (p , 0.0001, Pearson’s x2 test). The overall median TAT improved by 14% (5 minutes) and the 90th percentile TAT improved from 59 minutes to 46LABORATORY TURNAROUND QI PROJECT minutes (21%). During the study, there were noIn an effort to improve the efficiency of ED care, we changes in laboratory staff, procedures, or equipmentundertook a QI project to improve laboratory turn- that would otherwise explain the improvements. Onearound time (TAT).2 We requested a daily download individual was responsible for supervising all labora-of all ED laboratory tests for which results were tory personnel, and she used this report to raiseavailable the preceding day. We hypothesized that general awareness of time standards and to provideby providing data to the laboratory leadership for feedback to technologists when particular tests werepatients with prolonged TATs, we could reduce completed later than the time standard.variability in TAT and improve the average perfor- During the preintervention period, no specificmance. We negotiated a TAT standard of 60 minutes feedback was provided other than routine supervision
  3. 3. ACAD EMERG MED d November 2004, Vol. 11, No. 11 d 1239of the technologists. The hospital’s laboratory per- A NEW MEASURE OF ED CROWDINGformed an average of 337 stat tracked tests per day(for the ED and inpatient units). This report high- Our walkout rate is higher than the national average,lighted certain problems that had previously gone and the physical space of our ED is small for ourundetected—delays during shift change and when volume. ‘‘ED overcrowding’’ has been much dis-laboratory staff were busy distributing reports to the cussed, but it remains imprecisely defined. We in-inpatient units. The ED continues to provide these troduced the hourly ED census as a new measure ofdaily management reports to the laboratory, and the crowding3 and demonstrated that the hour-by-hourimprovements in TAT are sustained. ED census could be recreated over a prolonged period This project was challenging. Initially, our informa- (one year), using patient arrival and departure timestion technology (IT) services were unable to provide captured in our hospital registration system. A pro-the necessary data. We obtained laboratory results gram was written in the Stata language to calculateusing the laboratory information system (LIS) directly, the number of patients in the ED at any given hourand there was no outbound interface that contained over the one-year study period. Hourly censustest results. When the laboratory began performing changed dramatically over the course of the day andtests for other hospitals and physicians’ offices, an between our busiest and least busy days (Figure 3).outbound interface that contained test results was To validate hourly census as a measure of crowding,developed, and we began receiving daily files con- we used logistic regression to show a correlationtaining all laboratory data that originated from the between the hourly census at the time of a patient’sED. Stata does not support importing of files where registration and the likelihood of the patient’s leavingeach field is separated by the HL7 field delimiter (a without being seen (LWBS) (Figure 4) and the ED’spipe (|) character), so we needed to translate each being on ambulance diversion. The logistic regressionpipe character into a comma, which Stata can use to odds ratio for LWBS was 1.05 (95% confidence intervalidentify separate fields. We initially tried this trans- [95% CI] = 1.04 to 1.06), and the odds ratio for ambu-lation with two different editors (Brief [Solution lance diversion was 1.10 (95% CI = 1.07 to 1.12). WeSystems, Wellesley, MA] and Microsoft Word for believe that our quantitative measure of crowding willWindows [Microsoft Corp., Redmond, WA]), and prove robust across a wide range of clinical settings,found that these translations (750,000 to 1,000,000 and will provide a common standard for researchper day) took approximately 10 minutes. We identi- purposes. We are also using our findings to supportfied a stream editor (SED from Microsoft Windows our request for improved access to inpatient beds,Services for Unix) that performs the substitutions in physical plant modifications, and a facilitated redesignapproximately 15 seconds. In addition, the report for effort of ED work processes to improve the timelinessa single laboratory test is spread across several seg- of emergency care.ments (Figure 2), and it took us some time to learn tocombine these data into a single record of interest. Initially, daily downloads included only testing EFFECT OF AMBIGUOUS TROPONIN Iperformed on ED patients, but we soon discovered CUTOFF ON THE RATE OF POSITIVE TESTthat ED tests performed on patients who remained in RESULTSthe ED after the admission decision were classified by The guidelines of the American College of Cardiologythe LIS as inpatient tests and, therefore, we included (ACC) and European Society of Cardiology (ESC)4 con-all inpatient testing as well. tain a significant ambiguity in their recommendation Figure 2. Format of Health Level 7 (HL7) laboratory data for a single test (identifiers redacted).
  4. 4. 1240 Husk and Waxman d HOSPITAL INFORMATION SYSTEMS offs in clinical laboratories; our laboratory uses the 10% CV cutoff. We undertook a study9 to determine the effect of using one level for the diagnostic cutoff (the 99th percentile) versus another (the 10% CV) in our ED population. We studied two different assays (one manufactured by Abbott Diagnostics [Abbott Park, IL] the other by Ortho-Clinical Diagnostics [Raritan, NJ]) used by our clinical laboratory during sequential study periods. The daily laboratory download was used to identify all unique patient encounters in which troponin level was measured in an ED patient during each period. InFigure 3. The distribution of the census of nonpsychiatric accordance with ACC/ESC guidelines, the highestemergency department (ED) patients who received care in troponin level within 24 hours of the ED visit wasthe main ED. The horizontal line identifies the number of used for analysis. The two cutoffs defined threepatient-treatment spaces in the main ED. The census isreflected in two ways: the number of patients and the percent patient groups (low, medium, and high) based onoccupancy of the number of main ED treatment spaces. their highest troponin levels: less than 99th percentile, between the 99th percentile and 10% CV, and greater than 10% CV. The primary endpoint was the numberfor where to place the diagnostic cutoff for the troponin of patients in each group, or the number of positiveI test. They stipulate that the cutoff should be placed at cases at each cutoff. To further characterize patients inthe 99th percentile of a reference population, but also each group, we evaluated the following data from thesay that the coefficient of variation should be less than hospital’s inpatient database: inpatient mortality, re-10% at that cutoff. Coefficient of variation (CV) is vascularization procedures, and discharge diagnoses.defined as the standard deviation divided by the Analysis of inpatient procedures and discharge di-sample mean. In laboratory medicine, it refers to the agnoses was challenging. Each admission had up tovariability of replicate measurements of a single sam- 20 coded procedures and 20 secondary diagnoses.ple and is a measure of the precision of the assay. Text string searches were developed by manual re-Most assays have better precision at higher concen- view of all coded procedures and diagnoses, andtrations.5 The 10% CV cutoff is the minimum concen- validation of the algorithm by two physicians. Eachtration at which the CV (of replicate samples at that patient with a cardiac revascularization procedureconcentration) is less than 10%. At present, most had a coded procedural descriptor that included thecommercial troponin assays have a CV significantly text string ‘‘aortcor bypass,’’ ‘‘cor art bypass,’’ ‘‘coro-greater than 10% at the 99th percentile of the reference nary artery stent,’’ ‘‘aortcor,’’ or ‘‘aortcor,’’ and thispopulation, and several investigators have advocated algorithm did not erroneously include any noncardiacthe use of the 10% CV cutoff rather than the 99th procedures. Patients with a coronary artery diseasepercentile.6–8 There is considerable variability in cut- (CAD) diagnosis had a coded diagnosis descriptor ofFigure 4. The relationship between the emergency department (ED) census and walkout rate. For each ten additional patients inthe main ED, the walkout rate rises 1.8% (95% confidence interval, 1.8%–1.8%).
  5. 5. ACAD EMERG MED d November 2004, Vol. 11, No. 11 d 1241‘‘AMI’’ or one that included the text string ‘‘cornry,’’ and foreign bodies represent a significant fraction of‘‘infrct,’’ ‘‘coronary,’’ ‘‘posterior infarct,’’ ‘‘angina malpractice closed claims. We have observed that thepec,’’ ‘‘post MI,’’ ‘‘othr acute/subacute,’’ ‘‘ischemic ED and inpatient clinical staff do not always reportheart,’’ or ‘‘myocardial infarct.’’ diagnostic failures to ED leadership on those occa- During the two five-month study periods (from sions when the patient returns to our hospital, andJune 2002 to April 2003), there were a total of 5,570 this has been reported elsewhere.11patients (3,149 and 2,421 patients, respectively). We In a chart review of 5,000 inpatient and ED recordsfound that lowering the diagnostic cutoff from the from a single community hospital, Chellis et al.1210% CV level to the 99th percentile increased the sought cases with discrepancies between the ED’srelative number of positive tests by 58% (n = 461 vs. admission diagnosis and the principal discharge di-728) for the Abbott assay and 133% (n = 352 vs. 832) agnosis. Cases with discordant diagnoses were thenfor the Ortho assay. For each assay, inpatient mortality reviewed by two levels of physicians. They identifiedwas significantly greater for high-troponin cases than a very low rate of diagnostic errors (0.6%), andfor indeterminate- (p , 0.009, Pearson’s x2) or low- concluded that this two-tiered chart audit is a valuabletroponin cases. For the Abbott assay, inpatient mor- instrument for ED quality assurance. We createdtality for the indeterminate group was significantly a merged data file of all patients discharged fromhigher than for the low group (1.7% vs. 5%, p , the ED in 2003 who were admitted to the hospital0.003). For the Ortho assay, this difference was not within 96 hours after ED discharge. We then gener-significant. For each assay, there were significantly (p ated a report showing the ICD-9-coded ED diagnosis,, 0.0001) more revascularization procedures for together with a textual inpatient discharge diagnosis.patients with high troponin levels than indeterminate A total of 1,400 cases met the aforementioned criteria.or low ones. For the Abbott assay, the difference in This report was then manually reviewed for cases thatrevascularization procedures in the indeterminate- appeared to represent failure to diagnose during theand low-troponin groups was not significant, whereas initial ED visit. A sample of these cases is shown init was significant for the Ortho assay (p = 0.017). Table 1. Our approach is consistent with Schenkel’sPrincipal or secondary discharge diagnoses in pa- suggestion13 of a two-stage process to identify andtients with high troponin levels were more likely to quantitate errors using an automated review toreflect CAD than in patients with indeterminate or efficiently identify cases of potential interest, followedlow troponin levels (p , 0.0001), but there was no by a chart review.significant difference between indeterminate- and In a small study, the Joint Commission on Accred-low-troponin groups. itation on Healthcare Organizations found that more Thus we showed that the ambiguity in the di- than half of reported hospital sentinel events resultingagnostic cutoff translates into a significant effect on in patient death or permanent injuries resulting fromthe number of patients potentially diagnosed as delays in treatment originated in the ED.14 A singlehaving myocardial infarction. Our evaluation of in- error can identify work processes likely to fail again.patient mortality, revascularization procedures, and A root-cause analysis of a bad result, or a failure-discharge diagnoses suggests that patients in the mode effects analysis of high-risk work processes,indeterminate-troponin group probably fall some- may identify opportunities to improve the quality ofwhere between the low- and high-troponin groups care we provide. The Institute of Medicine’s bell-in terms of their likelihood of CAD. An understand- wether report, ‘‘To Err Is Human, Building a Safering of the magnitude of this effect led our laboratory Health System,’’15 emphasized the importance ofto reintroduce an indeterminate range (between the identifying, reporting, and analyzing errors.99th percentile and the 10% CV), rather than to leavejust a single cutoff at the higher 10% CV level. DISCUSSION IDENTIFY INDIVIDUAL CASES THAT MAY Over the last 20 years, the personal computer’s clock REPRESENT A ‘‘FAILURE TO DIAGNOSE’’ speed, maximal memory, and local hard drive storage have each increased by more than 300-fold at the same A HIGH-RISK CLINICAL CONDITION time that its price has dropped. Software that har-Many of the critical decisions in emergency medicine nesses this processing power has evolved in lockstepinvolve diagnostic strategies, and many costly errors with hardware improvements, allowing these typesinvolve failing to diagnose certain dangerous condi- of projects to be efficiently performed on personaltions. In a study of nine EDs using trained observers, computers. For example, the computer program forPerry et al.10 found that diagnostic errors were among the laboratory TAT report processes 750,000 tothe most common ED errors. Missed acute myocardial 1,000,000 separate data elements in less than 1 minute.infarction, appendicitis, ectopic pregnancy, subarach- Hospitals16 and EDs17 vary widely in their degree ofnoid hemorrhage, and aortic dissection can threaten integration of IT into clinical operations. Pallin et al.18the health or life of a patient, and missed fractures surveyed the primary training sites for emergency
  6. 6. 1242 Husk and Waxman d HOSPITAL INFORMATION SYSTEMSTABLE 1. Selected Cases of ED and Inpatient Final Diagnoses for Patients Who Were Dischargedfrom the ED and Admitted within Four Days in 2003 (Identifiers Redacted) ED to ED admit Disposition MR # Adm gap (days) ED Diagnosis Principal Inpatient Diagnosis (1st visit)Pt MR # Adm date 2 Cellulitis of foot Gangrene T&RPt MR # Adm date 3 Acute uri nos Parox ventric tachycard T&RPt MR # Adm date 2 Abdmnal pain oth spcf st Abscess of appendix T&RPt MR # Adm date 1 Alcohol abuse-unspec Diab ketoacidosis adult nsau T&RPt MR # Adm date 1 Abdmnal pain oth spcf st Torsion of ovary or tube T&RPt MR # Adm date 1 CVA Cerebral occ unspec w infarct AMAPt MR # Adm date 1 Appendicitis nos Acute appendicitis nos AMAPt MR # Adm date 2 Nausea alone Gastrointest hemorr nos T&RPt MR # Adm date 2 Asthma Othr pulmonary emb/infarction T&RPt MR # Adm date 2 Headache Pseudotumor cerebri T&RPt MR # Adm date 1 Pyrexia unknown origin Salmonella septicemia T&RPt MR # Adm date 1 Dyspnea Diab ketoacidosis juven nsau T&RPt MR # Adm date 3 Postsurgical states nec Orbital cellulitis T&RPt MR # Adm date 3 Abdmnal pain unspcf site Acute cholecystitis AMAPt MR # Adm date 4 Headache Meningitis nos T&RPt MR # Adm date 1 Atten to cystostomy Atherosclerosis w/ gangrene T&RPt MR # Adm date 2 Abdmnal pain rt upr quad Cornry atheroscelersis native T&RPt MR # Adm date 1 Heartburn Intestinl/perteal adhes w/obst T&RPt MR # Adm date 1 Gastritis/duodenitis nos Ac append w peritonitis T&RPt MR # Adm date 3 Malfunc vasc device/graf Staphylococc meningitis T&RPt MR # Adm date 1 Posttraum wnd infec nec Cardiac device/implant/graft T&RPt MR # Adm date 2 Constipation Acute appendicitis nos T&RPt MR # Adm date 2 Skin sensation disturb Cerebral occ unspec w infarct T&RPt MR # Adm date 4 Backache nos Pulm embol nos-antepart T&RPt MR # Adm date 2 Viral infections nos Ac append w peritonitis T&RPt MR # Adm date 3 Postop oth specfd aftrcr Osteomyelitis nos-ankle T&RPt MR # Adm date 1 No proc/patient decision Viral meningitis nos T&RPt MR # Adm date 3 Popliteal synovial cyst Othr pulmonary emb/infarction T&RPt MR # Adm date 2 Abdmnal pain unspcf site Duodenitis w/ hemorrhage T&RPt MR # Adm date 3 Abdmnal pain rt upr quad Acute cholecystitis T&RPt MR # Adm date 2 Headache Pituitary disorder nec AMAmedicine residencies regarding availability of IT tools. veillance to detect bioterrorism,19 discharge informa-Order entry, clinical documentation, and medication tion to state authorities, or claims data to payers.error checking were each found in fewer than 25% of Combining information from disparate systemsthese teaching EDs. Old electrocardiogram retrieval, generally requires that the applications are capablelaboratory, and radiology results reporting, cardiology of sending and receiving information over a networkreports, pathology reports, and electronic reference using standardized protocols and a means of match-materials were each found in more than 50% of ing appropriate records from the two systems. Forteaching EDs. Most hospitals have computer systems example, when the ADT system processes a transfer ofthat manage ED registration, admission–discharge– a patient from one inpatient unit to another, thistransfer (ADT) information, billing, laboratory, and updates the ‘‘current location’’ data element in theradiology data. If an inpatient is transferred to a dif- pharmacy, laboratory, patient-tracking, and radiologyferent unit, information about his or her visitors, information systems records for that patient. Middle-meals, and medications and the final reports of his or ware, computers, and software to format and routeher electrocardiograms, laboratory, and radiographic information between different systems is playing anresults need to be sent to the correct inpatient location. increasingly important role in moving informationThe hospital’s ADT system commonly provides up- between different systems. If the hospital replaces itsdates to the pharmacy, laboratory, patient tracking, ADT system, it need not design and test separateand radiology information systems, supporting the interfaces to the laboratory information system, theproper routing of visitors, medications, and reports. radiology information system, the pharmacy system,The use of supplies or pharmaceuticals for individual the bed-tracking software, etc. Rather, it ensures thatpatients may be sent to billing and inventory manage- the new ADT system’s interface with its middlewarement systems. In many hospitals, some information is functions as did the old ADT system’s, and middle-sent to external destinations, providing ED patient ware continues to properly route and format infor-data to public health authorities for syndromic sur- mation to those systems that require this information.
  7. 7. ACAD EMERG MED d November 2004, Vol. 11, No. 11 d 1243 Health Level 7 (HL7)20 is the most widely adopted errors by more than half, and to reduce unnecessarystandard for many types of clinical and administrative laboratory test ordering.26,27 As hospitals move to-medical information, including data involving patient ward additional computerization, we will have op-registration, admission, discharge and transfers, in- portunities for additional quality initiatives tosurance, charges and payers, laboratory tests, imaging improve quality and education by implementingstudies, nursing and physician observations, and phar- these systems and using the data that originate frommacy orders.21 A sample of a laboratory transaction the ED, inpatient units, and outpatient practices.reflecting one test result (obscuring data that identifythe patients and providers) is shown in Figure 2. Eachsegment of a record contains a three-character prefix, LIMITATIONSand ends with a carriage return character (ASCII This article is derived from the authors’ experiences atcharacter 13). For example, a given laboratory test two urban medical centers with five EDs in oneresult will begin with a Medical Subject Heading geographical area. It is designed to demonstrate the(MeSH) prefix, and the patient’s medical record num- ease and limitations of using existing databases tober will be found in the fourth field of the ‘‘PID’’ address a variety of quality issues. The particularsegment, and the normal range for a particular labora- projects that we undertook were selected based on ourtory test will be found in the eighth field of the ‘‘OBX’’ ability to obtain data from the specific hospital in-segment. Note that additional information (in the NTE formation systems available in our hospitals. Thesegment) can be incorporated into the test result. This ability to carry out these projects may not generalizestandard is evolving and is available from http:// to other Data designed to meet one need (to capture in-formation on inpatient admissions) may not meet all CONCLUSIONpotential needs. Coding of inpatient discharges driveshospital financials, and the accuracy of these data Data from existing hospital systems can be used toshould not be assumed.22 Inpatient coding of second- measure and manage the quality of emergency care.ary diagnoses may not always distinguish comorbid- As clinical information systems mature and more dataities from complications. In our troponin study, we sources become available, there will be additionalfound that coronary revascularization is consistently opportunities to analyze and improve the quality ofcoded, but other procedures that are less likely to care that we provide.influence reimbursement, e.g., an exercise stress test,are not always coded. Using data from hospital in- Referencesformation systems to identify cases that may benefit 1. Anonymous. Program requirements for emergency medicine.from a medical record review has been highlighted23 Available at: a strategy to address the limitations of quality rrc_progReq/110pr101.pdf. Accessed Mar 14, 2004. 2. Husk G, Waxman DA. Improving laboratory turnaroundreviews using medical record reviews and encounter time with a QI project focusing on outliers [abstract].data. Acad Emerg Med. 2004; 11:453. For many quality initiatives, e.g., the timeliness of 3. Husk G, Akhtar S, Krishnamurthy C, Waxman DA. Hourlylaboratory TAT, the existing databases support efficient emergency department census: a simple measure of crowding.measures of quality. But the examples of quality Ann Emerg Med [abstract], 2004; in press. 4. Myocardial infarction redefined: A consensus document of theinitiatives we have described highlight many of the Joint European Society of Cardiology/American College ofchallenges of trying to use data from hospital informa- Cardiology Committee for the redefinition of myocardialtion systems for other purposes. Database experts infarction. J Am Coll Cardiol. 2000; 36:959–69.advise users to start with the information they want 5. Ravel R. Clinical Laboratory Medicine. 6th ed. St. Louis, MO:to get out of the database (the reports) in order to Mosby–Year Book, 1995. 6. Apple FS, Wu AHB, Jaffe AS. European Society of Cardiologyproperly design the database or information system. and American College of Cardiology guidelines for Clinical leadership can contribute to design deci- redefinition of myocardial infarction: how to use existingsions of hospital information systems by advocating assays clinically and for clinical trials. Am Heart J.designs that will promote quality. By engaging in 2002; 144:981–6.these QI projects, we found we were better prepared 7. Sheehan P, Blennerhassett J, Vasikaran SD. Decision limit for troponin I and assay performance. Ann Clin select and implement an ED information system 2002; 39:231–6.and to work with inpatient colleagues on the inpatient 8. Panteghini M. Acute coronary syndrome: biochemicalcomputerized physician order entry system that is strategies in the troponin era. Chest. 2002; 122:1428–35.being implemented over the next several years. 9. Waxman DA, Buchwald JM, Schappert J, Hecht S, Husk G. In 1998, fewer than 2% of hospitals in the United Troponin I: clinical effect of an ambiguous diagnostic cutoff [abstract]. Acad Emerg Med. 2004; 11:505.States24 required computerized entry of orders by 10. Perry SJ, Risser D, Salisbury M, Wears R, Simon R.physicians. Computerized physician order entry has Classification of error in the emergency department.been shown25 to decrease nonintercepted medication Acad Emerg Med. 2000; 7:523–c.
  8. 8. 1244 Husk and Waxman d HOSPITAL INFORMATION SYSTEMS11. Schenkel SM, Khare RK, Rosenthal MM, et al. Resident 19. Irvin CB, Nouhan PP, Rice K. Syndromic analysis of perceptions of medical errors in the emergency department. computerized emergency department patients’ chief Acad Emerg Med. 2003; 10:1318–24. complaints: an opportunity for bioterrorism and influenza12. Chellis M, Olson JE, Augustine J, Hamilton GC. Evaluation surveillance. Ann Emerg Med. 2003; 41:447–52. of missed diagnoses for patients admitted from the 20. Anonymous. Available at: emergency department. Acad Emerg Med. 2001; about/glossary.htm. Accessed Mar 7, 2004. 8:125–30. 21. Anonymous. Available at: Schenkel S. Promoting patient safety and preventing medical standard/health/HL7.htm. Accessed Mar 7, 2004. error in emergency departments. Acad Emerg Med. 22. Iezzoni LI. Assessing quality using administrative data. 2000; 7:1204–22. Ann Intern Med. 1997; 127:666–74.14. Anonymous. Sentinel event alert. Available at: http:// 23. Steele-Friedlob E. The value of encounter data compared to medical record data for studies of Medicaid managed care. event1alert/sea_26.htm. Accessed Mar 12, 2004. Available at: Kohn LT, Corrigan JM, Donaldson MS (eds). To Err Is Human, managedcare/app-k.pdf. Accessed Mar 14, 2004. Building a Safer Health System. Washington, DC: National 24. Ash JS, Gorman PN, Hersh WR. Physician order entry in US Academy Press, 1999. hospitals. Proc AMIA Annu Symp. 1998; 235–9.16. Burke DE, Wang BB, Wan TT, Diana ML. Exploring 25. Bates DW, Leape LL, Cullen DJ, et al. Effect of computerized hospitals’ adoption of information technology. J Med Syst. physician order entry and a team intervention on prevention of 2002; 26:349–55. serious medication errors. JAMA. 1998; 280:1311–6.17. Birkinshaw R, O’Donnell J, Sammy I. Information technology 26. Bates DW, Kuperman GH, Rittenberg E, et al. A randomized in accident and emergency departments. Eur J Emerg Med. trial of computer-based intervention to reduce utilization of 1998; 5:245–8. redundant laboratory tests. Am J Med. 1999; 106:144–50.18. Pallin D, Lahman M, Baumlin K. Information technology in 27. Solomon DH, Shmerling RH, Schur PH, Lew R, Fiskio J, emergency medicine residency-affiliated emergency Bates DW. A computer based intervention to reduce departments. Acad Emerg Med. 2003; 10:848–52. unnecessary serologic testing. J Rheumatol. 1999; 26:2578–84.