Information needs of generalists and specialists using online best-practice algorithms to answer clinical questions

Modified Needleman-Wunsch algorithm for clinical pathway clustering

Clinical pathways are used to guide clinicians to provide a standardised delivery of care. Because of their standardisation, the aim of clinical pathways is to reduce variation in both care process and patient outcomes. When learning clinical pathways from data through data mining, it is common practice to represent each patient pathway as a string corresponding to their movements through activities. Clustering techniques are popular methods for pathway mining, and therefore this paper focuses on distance metrics applied to string data for k-medoids clustering. The two main aims are to firstly, develop a technique that seamlessly integrates expert information with data and secondly, to develop a string distance metric for the purpose of process data. The overall goal was to allow for more meaningful clustering results to be found by adding context into the string similarity calculation. Eight common distance metrics and their applicability are discussed. These distance metrics prove to give an arbitrary distance, without consideration for context, and each produce different results. As a result, this paper describes the development of a new distance metric, the modified Needleman-Wunsch algorithm, that allows for expert interaction with the calculation by assigning groupings and rankings to activities, which provide context to the strings. This algorithm has been developed in partnership with UK's National Health Service (NHS) with the focus on a lung cancer pathway, however the handling of the data and algorithm allows for application to any disease type. This method is contained within Sim.Pro.Flow, a publicly available decision support tool.

A scoping review of clinical decision support tools that generate new knowledge to support decision making in real time

OBJECTIVE

A growing body of observational data enabled its secondary use to facilitate clinical care for complex cases not covered by the existing evidence. We conducted a scoping review to characterize clinical decision support systems (CDSSs) that generate new knowledge to provide guidance for such cases in real time.

MATERIALS AND METHODS

PubMed, Embase, ProQuest, and IEEE Xplore were searched up to May 2020. The abstracts were screened by 2 reviewers. Full texts of the relevant articles were reviewed by the first author and approved by the second reviewer, accompanied by the screening of articles' references. The details of design, implementation and evaluation of included CDSSs were extracted.

RESULTS

Our search returned 3427 articles, 53 of which describing 25 CDSSs were selected. We identified 8 expert-based and 17 data-driven tools. Sixteen (64%) tools were developed in the United States, with the others mostly in Europe. Most of the tools (n = 16, 64%) were implemented in 1 site, with only 5 being actively used in clinical practice. Patient or quality outcomes were assessed for 3 (18%) CDSSs, 4 (16%) underwent user acceptance or usage testing and 7 (28%) functional testing.

CONCLUSIONS

We found a number of CDSSs that generate new knowledge, although only 1 addressed confounding and bias. Overall, the tools lacked demonstration of their utility. Improvement in clinical and quality outcomes were shown only for a few CDSSs, while the benefits of the others remain unclear. This review suggests a need for a further testing of such CDSSs and, if appropriate, their dissemination.

Effects of eHealth on hospital practice: synthesis of the current literature

Objective The transition to digital hospitals is fast-moving. Although US hospitals are further ahead than some others in implementing eHealth technologies, their early experiences are not necessarily generalisable to contemporary healthcare because both the systems and technologies have been rapidly evolving. It is important to provide up-to-date assessments of the evidence available. The aim of this paper is to provide an assessment of the current literature on the effects to be expected from hospital implementations of eHealth technologies. Methods A narrative review was conducted of systematic reviews investigating the effects of eHealth technologies (clinical decision support systems (CDSS), computerised provider order entry (CPOE), ePrescribing, electronic medical records (EMRs)) published between November 2015 and August 2017 and compared the findings with those of a previous narrative review that examined studies published between January 2010 and October 2015. The same search strategy and selection criteria were used in both studies. Results Of the seven relevant articles, three (42.9%) examined the effects of more than one eHealth system: only two (28.6%) studies were high quality, three (42.9%) were of intermediate quality and two (28.6%) were of low quality. We identified that EMRs are largely associated with conflicting findings. Previous reviews suggested that CPOE are associated with significant positive results of cost savings, organisational efficiency gains, less resource utilisation and improved individual performance. However, these effects were not investigated in the more recent reviews, and only mixed findings for communication between clinicians were reported. Similarly, for ePrescribing, later reviews reported limited evidence of benefits, although when coupled with CDSS, more consistent positive findings were reported. Conclusion This overview can help inform other hospitals in Australia and elsewhere of the likely effects resulting from eHealth technologies. The findings suggest that the effects of these systems are largely mixed, but there are positive findings, which encourage ongoing digital transformation of hospital practice. What is known about the topic? Governments are increasingly devoting substantial resources towards implementing eHealth technologies in hospital practice with the goals of improving clinical and financial outcomes. Yet, these outcomes are yet to be fully realised in practice and conflicting findings are often reported in the literature. What does this paper add? This paper extends a previous narrative review of systematic reviews and categorises the effects of eHealth technologies into a typology of outcomes to enable overall findings to be reported and comparisons to be made. In doings so, we synthesise 7 years of eHealth effects. Mixed results are largely reported for EMRs, with many benefits being compromised by practices stemming from resistance to EMRs. Limited evidence of effectiveness exists for CPOE and ePrescribing. CDSS are associated with the most consistent positive findings for clinician- and hospital-level effects. We observed renewed interest in the literature for the effect of eHealth technologies on communication both between clinicians and with patients. Other new insights have emerged relating to effects on clinical judgement, changing practice and staff retention. What are the implications for practitioners? eHealth technologies have the potential to positively affect clinical and financial outcomes. However, these benefits are not guaranteed, and mixed results are often reported. This highlights the need for hospitals and decision makers to clearly identify and act on the drivers of successful implementations if eHealth technologies are to facilitate the creation of new, more effective models of patient care in an increasingly complex healthcare environment.

Clinical Information Needs: A Concept Analysis

Health care organizations have invested resources to implement and upgrade information systems capable of collecting large quantities of data. Recent technology developments have created a renewed interest in studying clinician information needs. However, a common definition and analysis of the concept clinical information needs have not been provided. Walker and Avant's method was used to perform a concept analysis. Following a review of relevant literature, a clinical information need was defined as a conscious expression, which can be verbal or nonverbal, of a desire for knowledge to answer clinical questions in the course of decision making to deliver patient care.

Associations Among Practice Variation, Clinician Characteristics, and Care Algorithm Usage: A Multispecialty Vignette Study

The objective was to quantitatively evaluate clinician characteristics associated with unwarranted practice variation, and how clinical care algorithms influence this variation. Participants (142 physicians, 53 nurse practitioners, and 9 physician assistants in family medicine, internal medicine, and cardiology) described their management of 4 clinical vignettes, first based on their own practice (unguided), then using care algorithms (guided). The authors quantitatively estimated variation in management. Cardiologists demonstrated 17% lower variation in unguided responses than generalists (fold-change 0.83 [95% confidence interval (CI) 0.68, 0.97]), and those who agreed that practice variation can realistically be reduced had 16% lower variation than those who did not (fold-change 0.84 [CI, 0.71, 0.99]). A 17% reduction in variation was observed for guided responses compared with baseline (unguided) responses (fold-change 0.83 [CI, 0.76, 0.90]). Differences were otherwise similar across clinician subgroups and attitudes. Unwarranted practice variation was similar across most clinician subgroups. The authors conclude that care algorithms can reduce variation in management.

Electronic Knowledge Resources and Point-of-Care Learning: A Scoping Review

PURPOSE

The authors sought to summarize quantitative and qualitative research addressing electronic knowledge resources and point-of-care learning in a scoping review.

METHOD

The authors searched MEDLINE, Embase, PsycINFO, and the Cochrane Database for studies addressing electronic knowledge resources and point-of-care learning. They iteratively revised inclusion criteria and operational definitions of study features and research themes of interest. Two reviewers independently performed each phase of study selection and data extraction.

RESULTS

Of 10,811 studies identified, 305 were included and reviewed. Most studies (225; 74%) included physicians or medical students. The most frequently mentioned electronic resources were UpToDate (88; 29%), Micromedex (59; 19%), Epocrates (50; 16%), WebMD (46; 15%), MD Consult (32; 10%), and LexiComp (31; 10%). Eight studies (3%) evaluated electronic resources or point-of-care learning using outcomes of patient effects, and 36 studies (12%) reported objectively measured clinician behaviors. Twenty-five studies (8%) examined the clinical or educational impact of electronic knowledge resource use on patient care or clinician knowledge, 124 (41%) compared use rates of various knowledge resources, 69 (23%) examined the quality of knowledge resource content, and 115 (38%) explored the process of point-of-care learning. Two conceptual clarifications were identified, distinguishing the impact on clinical or educational outcomes versus the impact on test setting decision support, and the quality of information content versus the correctness of information obtained by a clinician-user.

CONCLUSIONS

Research on electronic knowledge resources is dominated by studies involving physicians and evaluating use rates. Studies involving nonphysician users, and evaluating resource impact and implementation, are needed.

Computer-Based Driving in Dementia Decision Tool With Mail Support: Cluster Randomized Controlled Trial

Background

Physicians often find significant challenges in assessing automobile driving in persons with mild cognitive impairment and mild dementia and deciding when to report to transportation administrators. Care must be taken to balance the safety of patients and other road users with potential negative effects of issuing such reports.

Objective

The aim of this study was to assess whether a computer-based Driving in Dementia Decision Tool (DD-DT) increased appropriate reporting of patients with mild dementia or mild cognitive impairment to transportation administrators.

Methods

The study used a parallel-group cluster nonblinded randomized controlled trial design to test a multifaceted knowledge translation intervention. The intervention included a computer-based decision support system activated by the physician-user, which provides a recommendation about whether to report patients with mild dementia or mild cognitive impairment to transportation administrators, based on an algorithm derived from earlier work. The intervention also included a mailed educational package and Web-based specialized reporting forms. Specialists and family physicians with expertise in dementia or care of the elderly were stratified by sex and randomized to either use the DD-DT or a control version of the tool that required identical data input as the intervention group, but instead generated a generic reminder about the reporting legislation in Ontario, Canada. The trial ran from September 9, 2014 to January 29, 2016, and the primary outcome was the number of reports made to the transportation administrators concordant with the algorithm.

Results

A total of 69 participating physicians were randomized, and 36 of these used the DD-DT; 20 of the 35 randomized to the intervention group used DD-DT with 114 patients, and 16 of the 34 randomized to the control group used it with 103 patients. The proportion of all assessed patients reported to the transportation administrators concordant with recommendation did not differ between the intervention and the control groups (50% vs 49%; Z=−0.19, P=.85). Two variables predicted algorithm-based reporting—caregiver concern (odds ratio [OR]=5.8, 95% CI 2.5-13.6, P<.001) and abnormal clock drawing (OR 6.1, 95% CI 3.1-11.8, P<.001).

Conclusions

On the basis of this quantitative analysis, in-office abnormal clock drawing and expressions of concern about driving from caregivers substantially influenced physicians to report patients with mild dementia or mild cognitive impairment to transportation administrators, but the DD-DT tool itself did not increase such reports among these expert physicians.

Trial Registration

ClinicalTrials.gov NCT02036099; https://clinicaltrials.gov/ct2/show/NCT02036099 (Archived by WebCite at http://www.webcitation.org/6zGMF1ky8)

Emergency Department Clinician Perspectives on the Data Availability to Implement Clinical Decision Support Tools for Five Clinical Practice Guidelines

Clinical practice guidelines (CPGs) often serve as the knowledge base for clinical decision support (CDS). While CPGs are rigorously created by medical professional societies, the concepts in each guideline may not be sufficient for translation into CDS applications. In addition, clinicians' perceptions of these concepts may differ greatly, affecting the implementation and impact of CDS within an organization. Five guidelines developed by the American College of Emergency Physicians were systematically explored, generating fifty-one unique clinical concepts. These concepts were presented to two nurses and two physicians, whom were asked to assess and comment on the capture of each clinical concept in the electronic health record (EHR) and the subsequent availability of the data for CDS. Nurses and physicians showed differing perceptions of data availability. These differing perceptions may influence an organizational approach to developing and implementing CDS, potentially informing our understanding of why CDS may not achieve the intended impact.