W. Scott Richardson

Evidence in the learning organization

BackgroundOrganizational leaders in business and medicine have been experiencing a similar dilemma: how to ensure that their organizational members are adopting work innovations in a timely fashion. Organizational leaders in healthcare have attempted to resolve this dilemma by offering specific solutions, such as evidence-based medicine (EBM), but organizations are still not systematically adopting evidence-based practice innovations as rapidly as expected by policy-makers (the knowing-doing gap problem). Some business leaders have adopted a systems-based perspective, called the learning organization (LO), to address a similar dilemma. Three years ago, the Society of General Internal Medicines Evidence-based Medicine Task Force began an inquiry to integrate the EBM and LO concepts into one model to address the knowing-doing gap problem.MethodsDuring the model development process, the authors searched several databases for relevant LO frameworks and their related concepts by using a broad search strategy. To identify the key LO frameworks and consolidate them into one model, the authors used consensus-based decision-making and a narrative thematic synthesis guided by several qualitative criteria. The authors subjected the model to external, independent review and improved upon its design with this feedback.ResultsThe authors found seven LO frameworks particularly relevant to evidence-based practice innovations in organizations. The authors describe their interpretations of these frameworks for healthcare organizations, the process they used to integrate the LO frameworks with EBM principles, and the resulting Evidence in the Learning Organization (ELO) model. They also provide a health organization scenario to illustrate ELO concepts in application.ConclusionThe authors intend, by sharing the LO frameworks and the ELO model, to help organizations identify their capacities to learn and share knowledge about evidence-based practice innovations. The ELO model will need further validation and improvement through its use in organizational settings and applied health services research.

Could our pretest probabilities become evidence based?: A prospective survey of hospital practice

OBJECTIVE: We sought to measure the proportion of patients on our clinical service who presented with clinical problems for which research evidence was available to inform estimates of pretest probability. We also aimed to discern whether any of this evidence was of sufficient quality that we would want to use it for clinical decision making.DESIGN: Prospective, consecutive case series and literature survey.SETTING: Inpatient medical service of a university-affiliated Veterans’ Affairs hospital in south Texas.PATIENTS: Patients admitted during the 3 study months for diagnostic evaluation.MEASUREMENTS: Patients’ active clinical problems were identified prospectively and recorded at the time of discharge, transfer, or death. We electronically searched MEDLINE and hand-searched bibliographies to find citations that reported research evidence about the frequency of underlying diseases that cause these clinical problems. We critically appraised selected citations and ranked them on a hierarchy of evidence.RESULTS: We admitted 122 patients for diagnostic evaluation, in whom we identified 45 different principal clinical problems. For 35 of the 45 problems (78%; 95% confidence interval [95% CI], 66% to 90%), we found citations that qualified as disease probability evidence. Thus, 111 of our 122 patients (91%; 95% CI, 86% to 96%) had clinical problems for which evidence was available in the medical literature.CONCLUSIONS: During 3 months on our hospital medicine service, almost all of the patients admitted for diagnostic evaluation had clinical problems for which evidence is available to guide our estimates of pretest probability. If confirmed by others, these data suggest that clinicians’ pretest probabilities could become evidence based.

Teaching evidence-based practice on foot

Come along to watch some clinical teachers in action.(1) A nurse practitioner and 2 nursing students working in a community health centre see a 51 year old woman with symptoms of menopause. The woman has had a total hysterectomy and, other than her menopausal symptoms, is healthy, with no family history of breast cancer or cardiovascular disease. She states that the symptoms are severely affecting her quality of life but seems to be reluctant to take hormone replacement therapy (HRT) because she has heard that it may affect her risk of breast cancer or heart disease. The nurse practitioner tells the woman they will do some research on the risks and benefits of HRT for her next consultation. After the consultation, she invites the students to help find and appraise evidence on this topic and “thinks aloud” about how the evidence will be used to discuss options with the woman at her next appointment.(2) A family nurse is running a vaccination clinic with a group of nursing students. After teaching the students how to give a vaccination using a larger bore, longer needle (23 gauge, 25 mm), the nurse emphasises the importance of using this type of needle because it reduces the likelihood of reactions to the vaccination. She provides the students with the reference of a study that provides evidence to support this claim.1(3) After initiating a multilayered, high compression regimen for a patient with a venous ulcer, a specialist nurse discusses the results of quantitative studies of the effectiveness of this intervention with a group of students. The group then explores how they can use this evidence to discuss the treatment with the patient.2 These teaching moments share 4 important features. Firstly, the teaching is actually happening, despite the barriers and disincentives that clinical teachers …

Killing Bugs at the Bedside: A prospective hospital survey of how frequently personal digital assistants provide expert recommendations in the treatment of infectious diseases

BackgroundPersonal Digital Assistants (PDAS) are rapidly becoming popular tools in the assistance of managing hospitalized patients, but little is known about how often expert recommendations are available for the treatment of infectious diseases in hospitalized patients.ObjectiveTo determine how often PDAs could provide expert recommendations for the management of infectious diseases in patients admitted to a general medicine teaching service.DesignProspective observational cohort studySettingInternal medicine resident teaching service at an urban hospital in Dayton, OhioPatients212 patients (out of 883 patients screened) were identified with possible infectious etiologies as the cause for admission to the hospital.MeasurementsPatients were screened prospectively from July 2002 until October 2002 for infectious conditions as the cause of their admissions. 5 PDA programs were assessed in October 2002 to see if treatment recommendations were available for managing these patients. The programs were then reassessed in January 2004 to evaluate how the latest editions of the software would perform under the same context as the previous year.ResultsPDAs provided treatment recommendations in at least one of the programs for 100% of the patients admitted over the 4 month period in the 2004 evaluation. Each of the programs reviewed improved from 2002 to 2004, with five of the six programs offering treatment recommendations for over 90% of patients in the study.ConclusionCurrent PDA software provides expert recommendations for a great majority of general internal medicine patients presenting to the hospital with infectious conditions.

Tips for Teachers of Evidence-based Medicine: Making Sense of Diagnostic Test Results Using Likelihood Ratios

Now is an exciting time to be or become a diagnostician. More diagnostic tests, including portions of the medical interview and physical examination, are being studied rigorously for their accuracy, precision, and usefulness in practice,1,2 and this research is increasingly being systematically reviewed and synthesized.3,4 Diagnosticians are gaining increasing access to this research evidence, raising hope that this knowledge will inform their diagnostic decisions and improve their patients’ clinical outcomes.5 For patients to benefit fully from this accumulating knowledge, the diagnosticians serving them must be able to reason probabilistically, to understand how test results can revise disease probability to confirm or exclude disorders, and to integrate this reasoning with other types of knowledge and diagnostic thinking.6–8 Yet, clinicians encounter several barriers when trying to integrate research evidence into clinical diagnosis.9 Some barriers involve difficulties in understanding and using the quantitative measures of tests’ accuracy and discriminatory power, including sensitivity, specificity, and likelihood ratios (LRs).9,10 We have noticed that LRs are particularly troubling to many learners at first, and we have wondered if this is because of the way they have been taught. Stumbling blocks can arise in several places when learning LRs: the names and formulae themselves can be intimidating; the arithmetic functions can be mystifying when attempted all at once; if two levels of test results are taught first, learners can have difficulty ‘stretching’ to multiple levels; and if disease probability is framed in odds terms (to directly multiply the odds by the likelihood ratio), learners can misunderstand why and how this conversion is done. Other stumbling blocks may occur as well. Other authors have described various approaches to helping clinicians understand LRs.11–16 In this article, we describe two additional approaches to help clinical learners understand how LRs describe the discriminatory power of test results. Whereas we mention other concepts such as pretest and posttest probability, full treatment of those subjects is beyond the scope of this article. These approaches were developed by experienced teachers of evidence-based medicine (EBM) and were refined over years of teaching practice. These tips have also been field-tested to double-check the clarity and practicality of these descriptions, as explained in the introductory article of this series.17 To help the reader envision these teaching approaches, we present sequenced advice for teachers in plain text, coupled with sample words to speak, in italics. These scripts are meant to be interactive, which means that teachers should periodically check in with the learners for their understanding and that teachers should try other ways to explain the ideas if the words we have suggested do not “click.” We present them in order from shorter to longer; however, because these 2 scripts cover the same general content, we encourage teachers to use either or both in an order that best fits their setting and learners.

Integrating Evidence Into Clinical Diagnosis

In busy clinical practice, diagnosis is our daily bread. All day, every day, we confront patients’ predicaments in which both of us want to know what is the matter. Many diagnostic tests are already available to help us, with more being developed all the time. Increasingly, diagnostic tests are undergoing rigorous evaluations of their accuracy and usefulness, with the results published as clinical care research (1). How can the results of this clinical care research be incorporated into our diagnostic decision making? This chapter addresses this topic, by starting with some illustrative cases, considering different modes of diagnostic thought, and then examining how evidence can be integrated into clinical diagnosis.

The practice of evidence-based medicine involves the care of whole persons

In this issue of the Journal, Dr. Fava posits that evidence-based medicine (EBM) was bound to fail. I share some of the concerns he expresses, yet I see more reasons for optimism. Having been on rounds with both Drs. Engel and Sackett, I reckon they would have agreed more than they disagreed. Their central teaching was the compassionate and well-informed care of sick persons. The model that emerged from these rounds was that patient care could be both person-centered and evidence-based, that clinical judgment was essential to both, and the decisions could and should be shared. Both clinicians and patients can bring knowledge from several sources into the shared decision making process in the clinical encounter, including evidence from clinical care research. I thank Dr. Fava for expressing legitimate doubts and providing useful criticism, yet I am cautiously optimistic that the model of EBM described here is robust enough to meet the challenges and is not doomed to fail.