A Randomized Trial of "Corollary Orders" to Prevent Errors of Omission

Regenstrief Institute for Health Care, Indianapolis, Indiana 46202-2859, USA.
Journal of the American Medical Informatics Association (Impact Factor: 3.5). 09/1997; 4(5):364-75. DOI: 10.1136/jamia.1997.0040364
Source: PubMed


Errors of omission are a common cause of systems failures. Physicians often fail to order tests or treatments needed to monitor/ameliorate the effects of other tests or treatments. The authors hypothesized that automated, guideline-based reminders to physicians, provided as they wrote orders, could reduce these omissions.
The study was performed on the inpatient general medicine ward of a public teaching hospital. Faculty and housestaff from the Indiana University School of Medicine, who used computer workstations to write orders, were randomized to intervention and control groups. As intervention physicians wrote orders for 1 of 87 selected tests or treatments, the computer suggested corollary orders needed to detect or ameliorate adverse reactions to the trigger orders. The physicians could accept or reject these suggestions.
During the 6-month trial, reminders about corollary orders were presented to 48 intervention physicians and withheld from 41 control physicians. Intervention physicians ordered the suggested corollary orders in 46.3% of instances when they received a reminder, compared with 21.9% compliance by control physicians (p < 0.0001). Physicians discriminated in their acceptance of suggested orders, readily accepting some while rejecting others. There were one third fewer interventions initiated by pharmacists with physicians in the intervention than control groups.
This study demonstrates that physician workstations, linked to a comprehensive electronic medical record, can be an efficient means for decreasing errors of omissions and improving adherence to practice guidelines.

Download full-text


Available from: J. Marc Overhage,
1 Follower
32 Reads
  • Source
    • "An example can be found from medical informatics , where physicians often make mistakes of omitting certain tests or treatments. These errors were reduced by developing an automated reminder for certain treatments and tests based on medical practice guidelines [9]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This study reports on the requirements for developing computer-interpretable rules for checking the compliance of a building design in a request for proposal (RFP), especially in the building information modeling (BIM) environment. It focuses on RFPs for large public buildings (over 5 million dollars) in South Korea, which generally entail complex designs. A total of 27 RFPs for housing, office, exhibition, hospital, sports center, and courthouse projects were analyzed to develop computer-interpreted RFP rules. Each RFP was composed of over 1800 sentences. Of these, only three to 366 sentences could be translated into a computer-interpretable sentence. For further analysis, this study deployed context-free grammar (CFG) in natural language processing, and classified morphemes into four categories: i.e., object (noun), method (verb), strictness (modal), and others. The subcategorized morphemes included three types of objects, twenty-nine types of methods, and five levels of strictness. The coverage applicability of the derived objects and methods was checked and validated against three additional RFP cases and then through a test case using a newly developed model checker system. The findings are expected to be useful as a guideline and basic data for system developers in the development of a generalized automated design checking system for South Korea.
    Advanced Engineering Informatics 06/2015; 29(3). DOI:10.1016/j.aei.2015.05.006 · 1.63 Impact Factor
  • Source
    • "Implementation of order sets and similar clinical decision support systems (CDSS) in an electronic medical record (EMR) with computerized physician order entry (CPOE) helps reinforce consistency among practitioners and support compliance with best-practice guidelines[ 1 ] [ 2 ]. While order sets and corollary orders can greatly benefit clinical practice, a top-down distribution model limits their benefit. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Physician orders, the concrete manifestation of clinical decision making, are enhanced by the distribution of clinical expertise in the form of order sets and corollary orders. Conventional order sets are top-down distributed by committees of experts, limited by the cost of manual development, maintenance, and limited end-user awareness. An alternative explored here applies statistical data-mining to physician order data (>330K order instances from >1.4K inpatient encounters) to extract clinical expertise from the bottom-up. This powers a corollary order suggestion engine using techniques analogous to commercial product recommendation systems (e.g.,'s "Customers who bought this…" feature). Compared to a simple benchmark, the item-based association method illustrated here improves order prediction precision from 13% to 18% and further to 28% by incorporating information on the temporal relationship between orders. Incorporating statistics on conditional order frequency ratios further refines recommendations beyond just "common" orders to those relevant to a specific clinical context.
    03/2013; 2013:34-38.
  • Source
    • "Given the suggestion that up to 50% of requests may be inappropriate, introducing strategies to manage duplicate testing can be a useful first step in initiating demand management without challenging the autonomy of clinical decision makers [97]. Information technology, such as electronic medical records, clinician order entry, expert systems, electronic handbooks and embedded hyperlinks in reports, is probably the easiest way to both provide solutions and monitor performance in these phases [98-100]. Clinical audits and clinician satisfaction surveys can also be useful measures of overall laboratory effectiveness [43, 101]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: For many years, the clinical laboratory's focus on analytical quality has resulted in an error rate of 4-5 sigma, which surpasses most other areas in healthcare. However, greater appreciation of the prevalence of errors in the pre- and post-analytical phases and their potential for patient harm has led to increasing requirements for laboratories to take greater responsibility for activities outside their immediate control. Accreditation bodies such as the Joint Commission International (JCI) and the College of American Pathologists (CAP) now require clear and effective procedures for patient/sample identification and communication of critical results. There are a variety of free on-line resources available to aid in managing the extra-analytical phase and the recent publication of quality indicators and proposed performance levels by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) working group on laboratory errors and patient safety provides particularly useful benchmarking data. Managing the extra-laboratory phase of the total testing cycle is the next challenge for laboratory medicine. By building on its existing quality management expertise, quantitative scientific background and familiarity with information technology, the clinical laboratory is well suited to play a greater role in reducing errors and improving patient safety outside the confines of the laboratory.
    Annals of Laboratory Medicine 01/2012; 32(1):5-16. DOI:10.3343/alm.2012.32.1.5 · 1.48 Impact Factor
Show more