Special Article
Protocol-Based Computer Reminders, the Quality of Care and the Non-Perfectibility of Man
N Engl J Med 1976; 295:1351-1355December 9, 1976DOI: 10.1056/NEJM197612092952405
- Abstract
- Abstract
To determine whether clinical errors can be reduced by prospective computer suggestions about the management of simple clinical events, I studied the responses of nine physicians to computer suggestions generated by 390 protocols in a controlled crossover design. These protocols dealt primarily with conditions managed (e.g., elevated blood pressure) or caused (e.g., liver toxicity) by drugs. Physicians responded to 51 per cent of 327 events when given, and 22 per cent of 385 events when not given computer suggestions. Neither level of postgraduate training (first-year postgraduate or third-year postgraduate) nor the order in which physicians served as study and control subjects had statistically significant overall effect on the results. It appears that the prospective reminders do reduce errors, and that many of these errors are probably due to man's limitations as a data processor rather than to correctable human deficiencies. (N Engl J Med 295:1351–1355, 1976)
Media in This Article
Figure 1
Copy of a Surveillance Report Produced during the Study with the Patient Identifying Information Removed.Table 1
Physician Response to Type I Events.*Article Activity
- Article
Implicit in currently available remedies for medical errors is the belief that man is perfectable and his errors can be eliminated by training or coercion. Postgraduate training and recertification can eliminate errors caused by ignorance. Professional Standards Review Organization (PSRO) length-of-stay reviews, malpractice suits and tissue committees can eliminate errors caused by wrong intent. However, man is not perfectable. There are limits to man's capabilities as an information processor that assure the occurrence of random errors in his activities. In studies using flight simulators, Drinkwater1 showed that sensory overload consistently caused pilot errors, often with "fatal" consequences. This study has obvious implications for the performance of physicians under the peak informational loads of busy practice settings. When keeping watch for random and infrequent events under experimental conditions, man predictably overlooks some target events.2 The physician in his watch for pathologic events is no exception. Physicians have overlooked radiologic3 and bacteriologic4 evidence of active tuberculosis. They have failed to react to adverse drug effects,5 unexplained anemia6 and a host of isolated laboratory findings.7 Given this background, it seems reasonable to hypothesize that many medical errors are due to the physician's intrinsic limits rather than to remediable flaws in his fund of knowledge.
Information theory states that to eliminate such errors, one must commit more time to the processing of the relevant data.8 , 9 Since many of the physician's informational tasks are rote and repetitive, a computer, given the necessary decision logic (protocols), could perform them and thereby provide the necessary processing time. We at the Regenstrief Institute have developed a computerized medical-record system that performs simple informational tasks by protocol.
In a previous study10 protocol-generated recommendations provided by this system significantly reduced the physician's error rate. However, that study did not prove the introductory hypothesis because it did not include a post-recommendation control period (a period of observation of the physician without computer assistance after a period with computer assistance). A fall in the physician's performance in such a control period would support the introductory hypothesis. A continuation of the physician's performance at the improved level would imply that the computer had "trained" the physician and would support the current opinion that the physician can be "perfected." In addition, the earlier study10 was performed in a clinic with little continuity of care, which raised doubt about the generality of its results. Finally, the protocols used were simple and imprecise (discussed elsewhere10). As a result, the recommendations generated by one type of protocol were inapplicable 72 per cent of the time. To test the introductory hypothesis, to determine whether computer-generated recommendations would have a similar influence on physicians who were familiar with their patients and to learn whether the protocols can be made more applicable (in terms of the per cent compliance with the protocol-generated recommendations), the present study using a crossover design and more precise protocols in a clinic with continuity of care was performed.
Materials and Methods
The Regenstrief Medical Record system, which has been described in detail elsewhere, consists of an evolving set of computer programs that serve the outpatient medical record, the pharmacy, the radiology department and the clinical laboratory.11 The system uses paper documents rather than electronic terminals for communication between the clinic and computer and provides a mechanism for executing protocols of care. For the purpose of this discussion, the term protocol is an English-like statement (written according to prescribed rules) defining a specific clinical event and the course of action necessary to "correct" that event. The Regenstrief Medical Record system accepts protocols written by physicians and uses these protocols to evaluate the management of the clinical events in its files. Following the mandates of these protocols, the computer systematically searches its records for the events specified and makes recommendations about the management of those that it finds. Each recommendation consists of a reminder to the physician that a particular event has occurred and a suggested course of "action" for correcting that event. The computer prints its recommendation for each patient on two reports provided at that patient's clinic visit.
The present study involved 390 protocols developed on the basis of management strategies reported in the medical literature. Most of the protocols dealt with conditions managed by drugs (e.g., hypertension) or induced by drugs (e.g., hyperkalemia). There were also a few protocols concerned with the work-up of individual laboratory abnormalities. The events that triggered these protocols were defined in terms of the patient's laboratory, use of medication or vital-signs data (or all three). Events were classified as Type I, II or III according to the suggestion that they triggered. Three different kinds of suggestions were made by the protocols: Type I, observe a physical finding or inquire about a symptom (e.g., the frequency of angina); Type II, order a diagnostic study; and Type III, change or initiate a therapeutic regimen.
I use the following protocol as an example to show, in concrete terms, how a protocol generates a recommendation:
On "cardiac glycosid" and followed by last "PVC's/MIN" greater than 2 then consider "cardiac glycosid" as cause of arrhythmia.
This protocol triggered the recommendation about cardiac glycosides shown in Figure 1Figure 1
Copy of a Surveillance Report Produced during the Study with the Patient Identifying Information Removed. (the fifth recommendation). The first part of the above protocol (the part before the "then") contains the event of interest. The second part contains the computer suggestion for the event. Given this protocol, when the computer finds a digitalized patient with more than 2 premature ventricular contractions (PVC's) at last observation, it suggests (obliquely) that the patient needs a review of his digitalis regimen (consider "cardiac glycosides" as cause of cardiac arrhythmia) and presents the critical facts for review (13-Feb-75, PVC's/MIN = 8). The wording of the recommendation is defined by the wording of the protocol as chosen by its author. As can be seen in Figure 1, the recommendation is nothing more than a rearrangement of the text of the protocol.The following are representative protocols from among the 390 used in the study. They are presented exactly as entered into the computer. The first is a Type I protocol; the second and third are Type II protocols, and the fourth and fifth are Type III protocols.
On "cardiac glycosid" then observe "PVC's/MIN"
If last "PPD INT" greater than 10 mm and no "Chest PA&LAT" since 1 year ago then order "Chest PA&LAT" to follow-up positive PPD*
On "K+ wasters" and no "uric" since 1 year ago then order "uric"
"Preg test" = "positive" since 9 months and on "sulfonamides" then stop "sulfonamides" if near term because of possible hyperbilirubinemia*
First "chloroquine" and followed by last "visual acuity" changed less than 20% then consider "chloroquine" as cause of optic damage*
For each patient's clinic visit, the Regenstrief Medical Record system produces three different reports. The first is the surveillance report (Fig. 1), which contains all the computer recommendations for a given patient. The second is a computer-tailored encounter form on which the computer displays the patient's active prescriptions as written at the last visit. The encounter form also provides space for recording of clinical findings. In this space the computer requests the observations required by Type I protocols by printing the names of these observations — e.g., "PVC's/MIN." All orders (prescriptions, requests for diagnostic services, referrals, diets, etc.) are written on the encounter form, a portion of which becomes the poly-drug prescription carried to the pharmacy. The third report, the summary report, is the only one that is not influenced by the computer protocols. It is a flow-sheet summary of the patient's clinical course that includes his symptoms, clinical findings, diagnostic studies and medication history.
The study was performed in one of the nine half-day sessions of the general-medicine clinic at Wishard Memorial Hospital. Continuity of care is provided in these clinics by medical residents, each of whom sees a fixed population of patients during "office hours" a half a day per week for the duration of his training. The physicians were acclimatized to the automated medical record (without computer recommendations) during a nine-month period before the study. The study was initiated on March 1, 1975. It dealt only with scheduled patient visits (approximately 60 per cent of the total). Each physician served as his own control in a crossover design. Four physicians served as study subjects first and control subjects second. Five physicians played these roles in reverse order. Study subjects received computer recommendations about their patients; control subjects did not. (This procedure meant that study subjects received surveillance reports and Type I recommendations on their encounter forms; control subjects received encounter forms without specific recommendations, and both study and control subjects received summary reports.) To assure that the study subjects actually read the computer recommendations, they were asked to notate each recommendation with an A, D or M according to whether they agreed (A) with it, disagreed (D) with it, or noticed that it was a consequence of missing (M) data. (The computer record was not as complete as the manual records because of incomplete capture of laboratory results ordered from the emergency room and hospital.)
Whether the physician responded appropriately to a given event was determined by analysis of his encounter form. For a Type Ievent his response was appropriate if he recorded the requested information, for a Type II event if he ordered the recommended tests, and for a Type III event if he either changed the treatment as recommended or repeated the observation which triggered the recommendation. I distinguish between events that occurred at study encounters and those occurring at control encounters by calling them study and control events, respectively. The effect of the computer recommendations was measured by the difference between the percentage of events to which the physician responded when fortified by these recommendations (study events) and the case when he was left to rely on his own perceptional resources (control events).
*Event specified was sought but not encountered during the study.
Results
It should be noted that the protocols were approximate, rather than precise, specifications of ideal care and that the computerized medical record had gaps in its laboratory information. Therefore, the physicians had legitimate reasons for ignoring some of the computer recommendations, and their absolute response rate should not be interpreted as a measure of the overall quality of care. The study provided meaningful information only about the difference between response rates at study and control encounters. In the analysis that follows, each event is treated as an independent entity.
A total of seven clinic sessions were studied before, and nine sessions after, crossover. The study extended over 17 weeks, during which the computer detected 712 events needing attention in 256 patient visits by 189 different patients. Physicians reacted to 51 per cent of 327 study events and 22 per cent of the 385 control events (P<0.00001, by Fisher's two-tailed exact test). Each of the nine physicians responded to a greater percentage of events when given computer recommendations than when not. The computer recommendation had the least effect on an intern who responded to 53 per cent of study events and 35 per cent of control events. It had the greatest effect on a highly regarded second-year resident who responded to 71 per cent of study events and 25 per cent of control events. Interestingly enough, at the onset of this study the latter physician insisted that what was needed was education of bad physicians about what they did not know, not reminders to good physicians about what they knew.
A number of different factors — the type of the event, the level of physician training and the order in which physicians served as study and control subjects — might have influenced the outcomes of this study. To measure the relative effect of these factors, the results were analyzed by the technic of Grizzle et al.12 for categorical data. This technic provides the analytical power for nominal and ordinal data that the analysis of variance provides for interval data.
Looking at the physician's response within event type, one finds very significant differences between those receiving and those not receiving computer suggestions: P<0.0016 for Type I events, P<0.00001 for Type II events, and P<0.0076 for Type III events. The fact that the suggestions affected the response to all three types of events suggests a pervasive and consistent influence on the physician's decision process. The breakdown of study results by individual event types is shown in Tables 1Table 1
Physician Response to Type I Events.*, 2Table 2
Physician Response to Type II Events.* and 3Table 3
Physician Response to Type III Events.*, respectively.There were four physicians in their first postgraduate year (interns), one in his second year (first-year resident) and four in their third postgraduate year (second-year residents). The level of training had no significant overall effect on the results. However, significant interactions were detected between some of the factors. Within study events interns responded more frequently than residents to Type II events (P<0.0001). The opposite was true for Type I events, to which residents responded more frequently than interns (P<0.04). Within control events, residents and interns showed no significant difference between their response rates to any type of event.
Because the physicians were exposed to the computer suggestions once a week for almost two months during their period as study subjects, we had expected a training effect on those whose control periods followed their study periods. However, the analysis showed that there was no overall difference between the results for physicians whose control period was first and those whose control period was second.
As mentioned above, physicians were asked to notate the computer suggestions received at study encounters. They did so in 86 per cent of the suggestions. Agreement was indicated in 65 per cent, disagreement in 20 per cent and missing data in 15 per cent of their comments. Excluding the suggestion with missing data, there is a 77 per cent physician agreement with the protocol logic. Interestingly enough, the physician's words and actions were not always congruent. In some cases they agreed with a suggestion by their word and disagreed with it by action, and in some cases, vice versa.
Discussion
Physicians detected and responded to twice as many events when given computer recommendations as when not. This effect was seen in every physician and was of similar magnitude for physicians in their early as compared to those in their late years of training. It was seen in all event types, influencing actions with direct effects on patient outcomes (medication changes) as well as those with indirect effects (test orders). Komaroff13 and Grimm14 and their colleagues have demonstrated similar effects on the respective performance of diabetologists and staff physicians who were reminded of abnormal findings or process standards (or both) by manual methods.
These results show that the influence of computer-generated suggestions extends to at least some continuous-care settings and that physicians comply with a higher proportion of computer recommendations when protocols are more precise (51 per cent in this study versus 35 per cent in the previous study). To the extent that the study protocols defined good medical process, the computer suggestions improved care. In addition, they revealed a persistent deficit in the conventional care process as typified by the control encounters. (I assume that the physicians would have responded to the same proportion of control events as study events if given equivalent information and that the difference in these two proportions represents a deficit.) Medical ignorance is widely assumed to be the cause of errors in medical practice. Ignorance of medical fact contributed little to the deficit measured in this study. If it had been a substantial factor, the base-line (control) response rate should have been higher for physicians with more years of training and for those whose control periods followed their study periods because of the "training effect" of the antecedent study period, and neither of these effects occurred.
Selfish motives are another possible explanation for medical "errors." However, the most common selfish motive, greed, could not have been a factor because financial incentives are not operative in our clinics. I believe that the results are most consistent with the initial hypothesis: that the amount of data presented to the physician per unit time is more than he can process without error. The computer augments the physician's capabilities and thereby reduces his error rate. The hypothesis is also relevant to the substantial rates of physician noncompliance with standards observed by Brook and Appell,15 Payne et al.,17 and Sibley et al.16 (98 per cent, 60 per cent and 30 per cent noncompliance respectively). It is very likely that the physicians in these studies were simply unable to detect all the multitudinous conditions specified by the standards used.
Computerized protocols will only be useful to the extent that they reflect the physician's actual decision logic. This limitation means that the system described in this paper can only perform informational tasks that can be represented by short, deterministic protocols. I believe that the physician's day is occupied by a great number of such tasks and that the 390 tasks represented by protocol in this study constitute only a fraction of the total. Since this study 76 protocols have been added to guide the work-up of a few common laboratory abnormalities and the scope of the computer record has been expanded to include the problem list and diagnostic impressions from x-ray, electrocardiographic and nuclear-medicine reports. This diagnostic information provides the basis for a great many more protocols (e.g., "if the chest x-ray shows active tuberculosis and the patient is not on antituberculous medicine, then culture for acid-fast bacteria and start treatment"). It is likely that a large proportion of the physician's informational work can be assisted by protocols of the kind reported here.
I have shown that the computer can improve physician compliance with predefined care protocols, and have presented evidence in support of the introductory hypothesis. If this hypothesis is correct, all practice errors cannot be attributed to the human causes of ignorance and avarice. Some are intrinsic to the limitations of the human mind. Their amelioration requires the commitment of more time to the processing of patient data. The physician in primary care can not realistically supply the needed time because his day is already saturated. More time for some patients would only mean less time for others — a reduction in the citizenry's access to care. Physician extenders could possibly supply the needed time, at least for some informational tasks. The use of physician extenders to review charts has improved the process of care.13 But machines are better suited than men to the mindless and repetitive tasks described above, and for such work, computer power will soon become cheaper than manpower because of the cost revolution being wrought by large-scale integrated circuits. Thus, I conclude that though the individual physician is not perfectable, the system of care is, and that the computer will play a major part in the perfection of future care systems.
Supported in part by a contract (HRA 106–74–181) with the Bureau of Health Services Research, Division of Health Care Information Systems and Technology.
I am indebted to Kerry Lee, Ph.D., of Duke University, for assistance in the design and analysis of this experiment, to Dr. Charles Kelley, Ms. Betty Dinius and the nurses, physicians and staff of the medicine clinic for co-operation, to Dr. Greg Wilson for assistance and encouragement, to David Jeris, Jay Seeger, Lonnie Blevins, Jim McKee, Tom Streeter, and Kevin Marshall for technical support and to Ms. Beth Bridge and Ms. Pearl Bertrum for their assistance.
Source Information
From the Department of Medicine and Community Health Sciences, Indiana University School of Medicine and Regenstrief Institute for Health Care (address reprint requests to Dr. McDonald at the Regenstrief Institute, Wishard Memorial Hospital, 1001 W. 10th St., Indianapolis, IN 46202).
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