Douglas S. Bell

Depression symptomatology and diagnosis: discordance between patients and physicians in primary care settings

BackgroundTo examine the agreement between depression symptoms using an assessment tool (PHQ-9), and physician documentation of the same symptoms during a clinic visit, and then to examine how the presence of these symptoms affects depression diagnosis in primary care settings.MethodsInterviewer administered surveys and medical record reviews. A total of 304 participants were recruited from 2321 participants screened for depression at two large urban primary care community settings.ResultsOf the 2321 participants screened for depression 304 were positive for depression and of these 75.3% (n = 229) were significantly depressed (PHQ-9 score ≥ 10). Of these, 31.0% were diagnosed by a physician with a depressive disorder. A total of 57.6% (n = 175) of study participants had both significant depression symptoms and functional impairment. Of these 37.7% were diagnosed by physicians as depressed. Cohens Kappa analysis, used to determine the agreement between depression symptoms elicited using the PHQ-9 and physician documentation of these symptoms showed only slight agreement (0.001–0.101) for all depression symptoms using standard agreement rating scales. Further analysis showed that only suicidal ideation and hypersomnia or insomnia were associated with an increased likelihood of physician depression diagnosis (OR 5.41 P sig < .01 and (OR 2.02 P sig < .05 respectively). Other depression symptoms and chronic medical conditions had no affect on physician depression diagnosis.ConclusionTwo-thirds of individuals with depression are undiagnosed in primary care settings. While functional impairment increases the rate of physician diagnosis of depression, the agreement between a structured assessment and physician elicited and or documented symptoms during a clinical encounter is very low. Suicidality, hypersomnia and insomnia are associated with an increase in the rate of depression diagnosis even when physician and self report of the symptom differ. Interventions that emphasize the use of routine structured screening of primary care patients might also improve the rate of diagnosis of depression in these settings. Further studies are needed to explore depression symptom assessment during physician patient encounter in primary care settings.

Interface design principles for usable decision support

Developing effective clinical decision support (CDS) systems for the highly complex and dynamic domain of clinical medicine is a serious challenge for designers. Poor usability is one of the core barriers to adoption and a deterrent to its routine use. We reviewed reports describing system implementation efforts and collected best available design conventions, procedures, practices and lessons learned in order to provide developers a short compendium of design goals and recommended principles. This targeted review is focused on CDS related to medication prescribing. Published reports suggest that important principles include consistency of design concepts across networked systems, use of appropriate visual representation of clinical data, use of controlled terminology, presenting advice at the time and place of decision making and matching the most appropriate CDS interventions to clinical goals. Specificity and contextual relevance can be increased by periodic review of trigger rules, analysis of performance logs and maintenance of accurate allergy, problem and medication lists in health records in order to help avoid excessive alerting. Developers need to adopt design practices that include user-centered, iterative design and common standards based on human-computer interaction (HCI) research methods rooted in ethnography and cognitive science. Suggestions outlined in this report may help clarify the goals of optimal CDS design but larger national initiatives are needed for systematic application of human factors in health information technology (HIT) development. Appropriate design strategies are essential for developing meaningful decision support systems that meet the grand challenges of high-quality healthcare.

Evaluating the teaching of evidence based medicine: conceptual framework

Although evidence for the effectiveness of evidence based medicine has accumulated, there is still little evidence on what are the most effective methods of teaching it.

Toward a Medical-concept Representation Language

The Canon Group is an informal organization of medical informatics researchers who are working on the problem of developing a “deeper” representation formalism for use in exchanging data and developing applications. Individuals in the group represent experts in such areas as knowledge representation and computational linguistics, as well as in a variety of medical subdisciplines. All share the view that current mechanisms for the characterization of medical phenomena are either inadequate (limited or rigid) or idiosyncratic (useful for a specific application but incapable of being generalized or extended). The Group proposes to focus on the design of a general schema for medical-language representation including the specification of the resources and associated procedures required to map language (including standard terminologies) into representations that make all implicit relations “visible,” reveal “hidden attributes,” and generally resolve ambiguous or vague references. The Group is proceeding by examining large numbers of texts (records) in medical sub-domains to identify candidate “concepts” and by attempting to develop general rules and representations for elements such as attributes and values so that all concepts may be expressed uniformly.

Drug—drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records

OBJECTIVE Alert fatigue represents a common problem associated with the use of clinical decision support systems in electronic health records (EHR). This problem is particularly profound with drug-drug interaction (DDI) alerts for which studies have reported override rates of approximately 90%. The objective of this study is to report consensus-based recommendations of an expert panel on DDI that can be safely made non-interruptive to the providers workflow, in EHR, in an attempt to reduce alert fatigue. METHODS We utilized an expert panel process to rate the interactions. Panelists had expertise in medicine, pharmacy, pharmacology and clinical informatics, and represented both academic institutions and vendors of medication knowledge bases and EHR. In addition, representatives from the US Food and Drug Administration and the American Society of Health-System Pharmacy contributed to the discussions. RESULTS Recommendations and considerations of the panel resulted in the creation of a list of 33 class-based low-priority DDI that do not warrant being interruptive alerts in EHR. In one institution, these accounted for 36% of the interactions displayed. DISCUSSION Development and customization of the content of medication knowledge bases that drive DDI alerting represents a resource-intensive task. Creation of a standardized list of low-priority DDI may help reduce alert fatigue across EHR. CONCLUSIONS Future efforts might include the development of a consortium to maintain this list over time. Such a list could also be used in conjunction with financial incentives tied to its adoption in EHR.

Methodological Review: Interface design principles for usable decision support: A targeted review of best practices for clinical prescribing interventions

Developing effective clinical decision support (CDS) systems for the highly complex and dynamic domain of clinical medicine is a serious challenge for designers. Poor usability is one of the core barriers to adoption and a deterrent to its routine use. We reviewed reports describing system implementation efforts and collected best available design conventions, procedures, practices and lessons learned in order to provide developers a short compendium of design goals and recommended principles. This targeted review is focused on CDS related to medication prescribing. Published reports suggest that important principles include consistency of design concepts across networked systems, use of appropriate visual representation of clinical data, use of controlled terminology, presenting advice at the time and place of decision making and matching the most appropriate CDS interventions to clinical goals. Specificity and contextual relevance can be increased by periodic review of trigger rules, analysis of performance logs and maintenance of accurate allergy, problem and medication lists in health records in order to help avoid excessive alerting. Developers need to adopt design practices that include user-centered, iterative design and common standards based on human-computer interaction (HCI) research methods rooted in ethnography and cognitive science. Suggestions outlined in this report may help clarify the goals of optimal CDS design but larger national initiatives are needed for systematic application of human factors in health information technology (HIT) development. Appropriate design strategies are essential for developing meaningful decision support systems that meet the grand challenges of high-quality healthcare.

Evaluating Electronic Referrals for Specialty Care at a Public Hospital

BACKGROUNDPoor communication between referring clinicians and specialists may lead to inefficient use of specialist services. San Francisco General Hospital implemented an electronic referral system (eReferral) that facilitates iterative pre-visit communication between referring and specialty clinicians to improve the referral process.OBJECTIVEThe purpose of the study was to determine the impact of eReferral (compared with paper-based referrals) on specialty referrals.DESIGNThe study was based on a visit-based questionnaire appended to new patient charts at randomly selected specialist clinic sessions before and after the implementation of eReferral.PARTICIPANTSSpecialty clinicians.MAIN MEASURESThe questionnaire focused on the self-reported difficulty in identifying referral question, referral appropriateness, need for and avoidability of follow-up visits.KEY RESULTSWe collected 505 questionnaires from speciality clinicians. It was difficult to identify the reason for referral in 19.8% of medical and 38.0% of surgical visits using paper-based methods vs. 11.0% and 9.5% of those using eReferral (p-value 0.03 and <0.001). Of those using eReferral, 6.4% and 9.8% of medical and surgical referrals using paper methods vs. 2.6% and 2.1% were deemed not completely appropriate (p-value 0.21 and 0.03). Follow-up was requested for 82.4% and 76.2% of medical and surgical patients with paper-based referrals vs. 90.1% and 58.1% of eReferrals (p-value 0.06 and 0.01). Follow-up was considered avoidable for 32.4% and 44.7% of medical and surgical follow-ups with paper-based methods vs. 27.5% and 13.5% with eReferral (0.41 and <0.001).CONCLUSIONUse of technology to promote standardized referral processes and iterative communication between referring clinicians and specialists has the potential to improve communication between primary care providers and specialists and to increase the effectiveness of specialty referrals.

High-priority drug–drug interactions for use in electronic health records

OBJECTIVE To develop a set of high-severity, clinically significant drug-drug interactions (DDIs) for use in electronic health records (EHRs). METHODS A panel of experts was convened with the goal of identifying critical DDIs that should be used for generating medication-related decision support alerts in all EHRs. Panelists included medication knowledge base vendors, EHR vendors, in-house knowledge base developers from academic medical centers, and both federal and private agencies involved in the regulation of medication use. Candidate DDIs were assessed by the panel based on the consequence of the interaction, severity levels assigned to them across various medication knowledge bases, availability of therapeutic alternatives, monitoring/management options, predisposing factors, and the probability of the interaction based on the strength of evidence available in the literature. RESULTS Of 31 DDIs considered to be high risk, the panel approved a final list of 15 interactions. Panelists agreed that this list represented drugs that are contraindicated for concurrent use, though it does not necessarily represent a complete list of all such interacting drug pairs. For other drug interactions, severity may depend on additional factors, such as patient conditions or timing of co-administration. DISCUSSION The panel provided recommendations on the creation, maintenance, and implementation of a central repository of high severity interactions. CONCLUSIONS A set of highly clinically significant drug-drug interactions was identified, for which warnings should be generated in all EHRs. The panel highlighted the complexity of issues surrounding development and implementation of such a list.

Rates and types of events reported to established incident reporting systems in two US hospitals

Background: US hospitals have had voluntary incident reporting systems for many years, but the effectiveness of these systems is unknown. To facilitate substantial improvements in patient safety, the systems should capture incidents reflecting the spectrum of adverse events that are known to occur in hospitals. Objective: To characterise the incidents from established voluntary hospital reporting systems. Design: Observational study examining about 1000 reports of hospitalised patients at each of two hospitals. Patients and setting: 16 575 randomly selected patients from an academic and a community hospital in the US in 2001. Main outcome measures: Rates of incidents reported per hospitalised patient and characteristics of reported incidents. Results: 9% of patients had at least one reported incident; 17 incidents were reported per 1000 patient-days in hospital. Nurses filed 89% of reports, physicians 1.9% and other providers 8.9%. The most common types were medication incidents (29%), falls (14%), operative incidents (15%) and miscellaneous incidents (16%); 59% seemed preventable and preventability was not clear for 32%. Among the potentially preventable incidents, 43% involved nurses, 16% physicians and 19% other types of providers. Qualitative examination of reports indicated that very few involved prescribing errors or high-risk procedures. Conclusions: Hospital reporting systems receive many reports, but capture a spectrum of incidents that differs from the adverse events known to occur in hospitals, thereby substantially underdetecting physician incidents, particularly those involving operations, high-risk procedures and prescribing errors. Increasing the reporting of physician incidents will be essential to enhance the effectiveness of hospital reporting systems; therefore, barriers to reporting such incidents must be minimised.

Mentorship in academic general internal medicine. Results of a survey of mentors.

BACKGROUND: Effective mentorship is crucial to career development. Strategies to improve the availability of mentors include mentoring multiple mentees at once, compensating mentors, comentoring, and long-distance mentoring.OBJECTIVE: To describe current trends in mentorship in general Internal Medicine (GIM).METHODS: We conducted a national cross-sectional web-based survey of GIM mentors, GIM fellowship directors, and GIM National Institutes of Health K24 grant awardees to capture their experiences with mentoring, including compensation for mentorship, multiple mentees, comentorship, and long-distance mentorship. We compared experiences by mentorship funding status, faculty type, academic rank, and sex.RESULTS: We collected data from 111 mentors (77% male, 54% full professors, and 68% clinician-investigators). Fifty-two (47%) received funding for mentorship. Mentors supervised a median (25th percentile, 75th percentile) of 5 (3, 8) mentees each, and would be willing to supervise a maximum of 6 (4, 10) mentees at once. Compared with mentors without funding, mentors with funding had more current mentees (mean of 8.3 vs 5.1, respectively; P<.001). Full professors had more current mentees than associate or assistant professors (8.0 vs 5.9 vs 2.4, respectively; P=.005). Ninety-four (85%) mentors had experience comentoring, and two-thirds of mentors had experience mentoring from a distance. Although most mentors found long-distance mentoring to be less demanding, most also said it is less effective for the mentee and is personally less fulfilling.CONCLUSIONS: Mentors in GIM appear to be close to their mentorship capacity, and the majority lack funding for mentorship. Comentoring and long-distance mentoring are common.