Healthcare provider education to support integration of pharmacogenomics in practice: the eMERGE Network experience

Ten organizations within the Electronic Medical Records and Genomics Network developed programs to implement pharmacogenomic sequencing and clinical decision support into clinical settings. Recognizing the importance of informed prescribers, a variety of strategies were used to incorporate provider education to support implementation. Education experiences with pharmacogenomics are described within the context of each organization's prior involvement, including the scope and scale of implementation specific to their Electronic Medical Records and Genomics projects. We describe common and distinct education strategies, provide exemplars and share challenges. Lessons learned inform future perspectives. Future pharmacogenomics clinical implementation initiatives need to include funding toward implementing provider education and evaluating outcomes.

Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system

BACKGROUND

Although alert fatigue is blamed for high override rates in contemporary clinical decision support systems, the concept of alert fatigue is poorly defined. We tested hypotheses arising from two possible alert fatigue mechanisms: (A) cognitive overload associated with amount of work, complexity of work, and effort distinguishing informative from uninformative alerts, and (B) desensitization from repeated exposure to the same alert over time.

METHODS

Retrospective cohort study using electronic health record data (both drug alerts and clinical practice reminders) from January 2010 through June 2013 from 112 ambulatory primary care clinicians. The cognitive overload hypotheses were that alert acceptance would be lower with higher workload (number of encounters, number of patients), higher work complexity (patient comorbidity, alerts per encounter), and more alerts low in informational value (repeated alerts for the same patient in the same year). The desensitization hypothesis was that, for newly deployed alerts, acceptance rates would decline after an initial peak.

RESULTS

On average, one-quarter of drug alerts received by a primary care clinician, and one-third of clinical reminders, were repeats for the same patient within the same year. Alert acceptance was associated with work complexity and repeated alerts, but not with the amount of work. Likelihood of reminder acceptance dropped by 30% for each additional reminder received per encounter, and by 10% for each five percentage point increase in proportion of repeated reminders. The newly deployed reminders did not show a pattern of declining response rates over time, which would have been consistent with desensitization. Interestingly, nurse practitioners were 4 times as likely to accept drug alerts as physicians.

CONCLUSIONS

Clinicians became less likely to accept alerts as they received more of them, particularly more repeated alerts. There was no evidence of an effect of workload per se, or of desensitization over time for a newly deployed alert. Reducing within-patient repeats may be a promising target for reducing alert overrides and alert fatigue.

The effect of a clinical decision-making mHealth support system on maternal and neonatal mortality and morbidity in Ghana: study protocol for a cluster randomized controlled trial

BACKGROUND

Mobile health (mHealth) presents one of the potential solutions to maximize health worker impact and efficiency in an effort to reach the Sustainable Development Goals 3.1 and 3.2, particularly in sub-Saharan African countries. Poor-quality clinical decision-making is known to be associated with poor pregnancy and birth outcomes. This study aims to assess the effect of a clinical decision-making support system (CDMSS) directed at frontline health care providers on neonatal and maternal health outcomes.

METHODS/DESIGN

A cluster randomized controlled trial will be conducted in 16 eligible districts (clusters) in the Eastern Region of Ghana to assess the effect of an mHealth CDMSS for maternal and neonatal health care services on maternal and neonatal outcomes. The CDMSS intervention consists of an Unstructured Supplementary Service Data (USSD)-based text messaging of standard emergency obstetric and neonatal protocols to providers on their request. The primary outcome of the intervention is the incidence of institutional neonatal mortality. Outcomes will be assessed through an analysis of data on maternal and neonatal morbidity and mortality extracted from the District Health Information Management System-2 (DHIMS-2) and health facility-based records. The quality of maternal and neonatal health care will be assessed in two purposively selected clusters from each study arm.

DISCUSSION

In this trial the effect of a mobile CDMSS on institutional maternal and neonatal health outcomes will be evaluated to generate evidence-based recommendations for the use of mobile CDMSS in Ghana and other West African countries.

TRIAL REGISTRATION

ClinicalTrials.gov, identifier: NCT02468310 . Registered on 7 September 2015; Pan African Clinical Trials Registry, identifier: PACTR20151200109073 . Registered on 9 December 2015 retrospectively from trial start date.

Information needs for making clinical recommendations about potential drug-drug interactions: a synthesis of literature review and interviews

BACKGROUND

Drug information compendia and drug-drug interaction information databases are critical resources for clinicians and pharmacists working to avoid adverse events due to exposure to potential drug-drug interactions (PDDIs). Our goal is to develop information models, annotated data, and search tools that will facilitate the interpretation of PDDI information. To better understand the information needs and work practices of specialists who search and synthesize PDDI evidence for drug information resources, we conducted an inquiry that combined a thematic analysis of published literature with unstructured interviews.

METHODS

Starting from an initial set of relevant articles, we developed search terms and conducted a literature search. Two reviewers conducted a thematic analysis of included articles. Unstructured interviews with drug information experts were conducted and similarly coded. Information needs, work processes, and indicators of potential strengths and weaknesses of information systems were identified.

RESULTS

Review of 92 papers and 10 interviews identified 56 categories of information needs related to the interpretation of PDDI information including drug and interaction information; study design; evidence including clinical details, quality and content of reports, and consequences; and potential recommendations. We also identified strengths/weaknesses of PDDI information systems.

CONCLUSIONS

We identified the kinds of information that might be most effective for summarizing PDDIs. The drug information experts we interviewed had differing goals, suggesting a need for detailed information models and flexible presentations. Several information needs not discussed in previous work were identified, including temporal overlaps in drug administration, biological plausibility of interactions, and assessment of the quality and content of reports. Richly structured depictions of PDDI information may help drug information experts more effectively interpret data and develop recommendations. Effective information models and system designs will be needed to maximize the utility of this information.

Automating Performance Measures and Clinical Practice Guidelines: Differences and Complementarities

Through close analysis of two pairs of systems that implement the automated evaluation of performance measures (PMs) and guideline-based clinical decision support (CDS), we contrast differences in their knowledge encoding and necessary changes to a CDS system that provides management recommendations for patients failing performance measures. We trace the sources of differences to the implementation environments and goals of PMs and CDS.

How can GPs drive software changes to improve healthcare for Aboriginal and Torres Strait Islanders peoples?

BACKGROUND

Changes to the software used in general practice could improve the collection of the Aboriginal and Torres Strait Islander status of all patients, and boost access to healthcare measures specifically for Aboriginal and Torres Strait Islander peoples provided directly or indirectly by general practitioners (GPs).

OBJECTIVE

Despite longstanding calls for improvements to general practice software to better support Aboriginal and Torres Strait Islander health, little change has been made. The aim of this article is to promote software improvements by identifying desirable software attributes and encouraging GPs to promote their adoption.

DISCUSSION

Establishing strong links between collecting Aboriginal and Torres Strait Islander status, clinical decision supports, and uptake of GP-mediated health measures specifically for Aboriginal and Torres Strait Islander peoples - and embedding these links in GP software - is a long overdue reform. In the absence of government initiatives in this area, GPs are best placed to advocate for software changes, using the model described here as a starting point for action.

The Use of Automated SNOMED CT Clinical Coding in Clinical Decision Support Systems for Preventive Care

OBJECTIVE

The objective of this study is to discuss and analyze the use of automated SNOMED CT clinical coding in clinical decision support systems (CDSSs) for preventive care. The central question that this study seeks to answer is whether the utilization of SNOMED CT in CDSSs can improve preventive care.

METHOD

PubMed, Google Scholar, and Cochrane Library were searched for articles published in English between 2001 and 2012 on SNOMED CT, CDSS, and preventive care.

OUTCOME MEASURES

Outcome measures were the sensitivity or specificity of SNOMED CT coded data and the positive predictive value or negative predictive value of SNOMED CT coded data. Additionally, we documented the publication year, research question, study design, results, and conclusions of these studies.

RESULTS

The reviewed studies suggested that SNOMED CT successfully represents clinical terms and negated clinical terms.

CONCLUSION

The use of SNOMED CT in CDSS can be considered to provide an answer to the problem of medical errors as well as for preventive care in general. Enhancement of the modifiers and synonyms found in SNOMED CT will be necessary to improve the expected outcome of the integration of SNOMED CT with CDSS. Moreover, the application of the tree-augmented naïve (TAN) Bayesian network method can be considered the best technique to search SNOMED CT data and, consequently, to help improve preventive health services.

A collaborative outcome resource network (ACORN): Tools for increasing the value of psychotherapy

The authors describe a collaborative outcomes resource network (ACORN) and the suite of measurement and decision support tools (ACORN Toolkit) that have emerged from this collaboration for the purpose of providing clinical feedback to therapists. The ACORN Toolkit is most accurately described as a comprehensive clinical information system designed to increase the value of mental health services across large systems of care. It was built to integrate large datasets from multiple sources including outcome data, client demographics and diagnostic data, therapist credentialing information, pharmacy data, and service claims data. For the limited purposes of this article, the authors focus on the ACORN Toolkit for measuring and how it has contributed to improving outcomes in psychotherapy. Implications to current practice and future training are provided.

The Impact of an Online Evidence System on Confidence in Decision Making in a Controlled Setting

Objective. To examine the impact of online evidence retrieval on clinicians’ decision-making confidence and to determine if this differs for experienced doctors and nurses. Methods. A sample of 44 doctors and 31 clinical nurse consultants (CNCs) answered 8 clinical scenarios (600 scenario answers) before and after the use of online evidence resources. Clinicians rated their confidence in scenario answers and in the evidence they found using the information system. Results. Prior to using online evidence, 37% of doctors and 18% of CNCs answered the scenarios correctly. These clinicians were more confident (56% very confident or confident) in their answers than those with incorrect (34%) answers. Doctors with incorrect answers prior to searching rated their confidence significantly higher than did nurses who were incorrect. After searching, both groups answered 50% of scenarios correctly. Clinicians with correct answers had greater confidence in the evidence found compared to those with incorrect answers. Doctors were more confident in evidence found confirming an initially correct answer than were nurses. More than 50% of clinicians who persisted with an incorrect answer after searching reported that they were confident or very confident in the evidence found. Clinicians who did not know scenario answers before searching placed equal confidence in evidence that led them to a correct or incorrect answer. Conclusions. The information obtained from an online evidence system influenced clinicians’ confidence in their answers to the clinical scenarios. The relationship between confidence in answers and correctness is complex. Both existing knowledge and professional role were mediating factors. The finding that many clinicians placed confidence in information that led them to incorrect answers warrants further investigation.

Prescription reporting with immediate medication utilization mapping (PRIMUM): development of an alert to improve narcotic prescribing

BACKGROUND

Prescription narcotic overdoses and abuse have reached alarming numbers. To address this epidemic, integrated clinical decision support within the electronic medical record (EMR) to impact prescribing behavior was developed and tested.

METHODS

A multidisciplinary Expert Panel identified risk factors for misuse, abuse, or diversion of opioids or benzodiazepines through literature reviews and consensus building for inclusion in a rule within the EMR. We ran the rule "silently" to test the rule and collect baseline data.

RESULTS

Five criteria were programmed to trigger the alert; based on data collected during a "silent" phase, thresholds for triggers were modified. The alert would have fired in 21.75 % of prescribing encounters (1.30 % of all encounters; n = 9998), suggesting the alert will have a low prescriber burden yet capture a significant number of at-risk patients.

CONCLUSIONS

While the use of the EMR to provide clinical decision support is not new, utilizing it to develop and test an intervention is novel. We successfully built an alert system to address narcotic prescribing by providing critical, objective information at the point of care. The silent phase data were useful to appropriately tune the alert and obtain support for widespread implementation. Future healthcare initiatives can utilize similar methodology to collect data prospectively via the electronic medical record to inform the development, delivery, and evaluation of interventions.

Reducing prognostic errors: a new imperative in quality healthcare

Dhruv Khullar and Anupam Jena argue that we need to pay more attention to prognosis if we are to ensure that patients get appropriate and safe treatment

Optimizing Digital Health Informatics Interventions Through Unobtrusive Quantitative Process Evaluations

Health informatics interventions such as clinical decision support (CDS) and audit and feedback (A&F) are variably effective at improving care because the underlying mechanisms through which these interventions bring about change are poorly understood. This limits our possibilities to design better interventions. Process evaluations can be used to improve this understanding by assessing fidelity and quality of implementation, clarifying causal mechanisms, and identifying contextual factors associated with variation in outcomes. Coiera describes the intervention process as a series of stages extending from interactions to outcomes: the "information value chain". However, past process evaluations often did not assess the relationships between those stages. In this paper we argue that the chain can be measured quantitatively and unobtrusively in digital interventions thanks to the availability of electronic data that are a by-product of their use. This provides novel possibilities to study the mechanisms of informatics interventions in detail and inform essential design choices to optimize their efficacy.

A Standard for Nursing Process - Clinical Decision Support Systems (NP-CDSS)

A Nursing Process-Clinical Decision Support System (NP-CDSS) Standard with 25 criteria to guide future developments of Nursing Process-Clinical Decision Support Systems was developed. The NP-CDSS Standards' content validity was established in qualitative interviews yielding fourteen categories that demonstrate international expert consensus. All experts judged the Advanced Nursing Process being the centerpiece for Nursing Process-Clinical Decision Support System that should suggest research-based, pre-defined nursing diagnoses and correct linkages between diagnoses, evidence-based interventions and patient outcomes.

Ontological Foundations for Tracking Data Quality through the Internet of Things

Amongst the positive outcomes expected from the Internet of Things for Health are longitudinal patient records that are more complete and less erroneous by complementing manual data entry with automatic data feeds from sensors. Unfortunately, devices are fallible too. Quality control procedures such as inspection, testing and maintenance can prevent devices from producing errors. The additional approach envisioned here is to establish constant data quality monitoring through analytics procedures on patient data that exploit not only the ontological principles ascribed to patients and their bodily features, but also to observation and measurement processes in which devices and patients participate, including the, perhaps erroneous, representations that are generated. Using existing realism-based ontologies, we propose a set of categories that analytics procedures should be able to reason with and highlight the importance of unique identification of not only patients, caregivers and devices, but of everything involved in those measurements. This approach supports the thesis that the majority of what tends to be viewed as 'metadata' are actually data about first-order entities.

Developing Evidence-Based Care Standards and a Decision-Making Support System for Pain Management

Pain is a crucial sign and symptom in hospitalised patients. This paper describes how a medical centre created a knowledge-based, computerised pain management decision-making process to support nurses in personalising preventive interventions based on patient requirements.

A Quantitative Study Investigating the Effects of Computerised Clinical Decision Support in the Emergency Department

This paper describes the evaluation of a computerized clinical decision support system (CCDSS) for Emergency Department (ED) triage. The CCDSS for triage was developed as a means to improve ED quality and safety. Whilst there is significant research on the role of CCDSS in health care, their role in EDs remains under-investigated. In this study, a CCDSS for ED triage was developed and evaluated using a quasi-experimental interrupted time-series design. Data was collected at four time points before and after the introduction of the CCDSS to assess key aspects of quality and safety within the ED. The results demonstrated a statistically significant improvement in triage prioritization (p < 0.001), pain scoring (p < 0.001) and pain management (p < 0.001). This study clearly identifies the positive clinical impact that a CCDSS can have on quality and safety for ED patients and provides a unique contribution to the current knowledge base.

Improving Utilization of Clinical Decision Support Systems by Reducing Alert Fatigue: Strategies and Recommendations

Clinical decision support systems (CDSS) are designed to help making clinical decisions regarding the management of patients. CDS alerts can save lives but frequent insignificant ones might cause alert fatigue. Studies discuss that 33% to 96% of clinical alerts are ignored. We categorized best evidence based strategies, to reduce alert fatigue and improve CDSS utilization, into five major areas. Classify alerts in to three main levels; severe, moderate and minor then develop a core set of critical drug to drug interactions. Classify alerts into active and passive groups, where only critical alerts should be interruptive actively while less critical alerts should be non-interruptive to the user. Conduct regular user training on new improvements. Keep monitoring alert response rates and keep ongoing research and improvement efforts. Provide systems with automated feedback and learning mechanisms where frequently ignored and justified alerts could be moved automatically from the active interruptive to the passive non-interruptive model.

Mobile Clinical Decision Support Systems in Our Hands - Great Potential but also a Concern

Due to the powerful computer resources as well as the availability of today's mobile devices, a special field of mobile systems for clinical decision support in medicine has been developed. The benefits of these applications (systems) are: availability of necessary hardware (mobile phones, tablets and phablets are widespread, and can be purchased at a relatively affordable price), availability of mobile applications (free or for a "small" amount of money) and also mobile applications are tailored for easy use and save time of clinicians in their daily work. In these systems lies a huge potential, and certainly a great economic benefit, so this issue must be approached multidisciplinary.

Characterization of the Context of Drug Concepts in Research Protocols: An Empiric Study to Guide Ontology Development

We examined a large body of research study documents (protocols) to identify mentions of drug concepts and established base concepts and roles needed to characterize the semantics of these instances. We found these concepts in three general situations: background knowledge about the drug, study procedures involving the drug, and other roles of the drug in the study. We identified 18 more specific contexts (e.g., adverse event information, administration and dosing of the drug, and interactions between the study drug and other drugs). The ontology was validated against a test set of protocol documents from NIH and ClinicalTrial.gov. The goal is to support the automated extraction of drug information from protocol documents to support functions such as study retrieval, determination of subject eligibility, generation of order sets, and creation of logic for decision support alerts and reminders. Further work is needed to formally extend existing ontologies of clinical research.

Design, Development, and Initial Evaluation of a Terminology for Clinical Decision Support and Electronic Clinical Quality Measurement

When coupled with a common information model, a common terminology for clinical decision support (CDS) and electronic clinical quality measurement (eCQM) could greatly facilitate the distributed development and sharing of CDS and eCQM knowledge resources. To enable such scalable knowledge authoring and sharing, we systematically developed an extensible and standards-based terminology for CDS and eCQM in the context of the HL7 Virtual Medical Record (vMR) information model. The development of this terminology entailed three steps: (1) systematic, physician-curated concept identification from sources such as the Health Information Technology Standards Panel (HITSP) and the SNOMED-CT CORE problem list; (2) concept de-duplication leveraging the Unified Medical Language System (UMLS) MetaMap and Metathesaurus; and (3) systematic concept naming using standard terminologies and heuristic algorithms. This process generated 3,046 concepts spanning 68 domains. Evaluation against representative CDS and eCQM resources revealed approximately 50-70% concept coverage, indicating the need for continued expansion of the terminology.

Clinical Decision Support Systems and Prevention: A Community Guide Cardiovascular Disease Systematic Review

CONTEXT

Clinical decision support systems (CDSSs) can help clinicians assess cardiovascular disease (CVD) risk and manage CVD risk factors by providing tailored assessments and treatment recommendations based on individual patient data. The goal of this systematic review was to examine the effectiveness of CDSSs in improving screening for CVD risk factors, practices for CVD-related preventive care services such as clinical tests and prescribed treatments, and management of CVD risk factors.

EVIDENCE ACQUISITION

An existing systematic review (search period, January 1975-January 2011) of CDSSs for any condition was initially identified. Studies of CDSSs that focused on CVD prevention in that review were combined with studies identified through an updated search (January 2011-October 2012). Data analysis was conducted in 2013.

EVIDENCE SYNTHESIS

A total of 45 studies qualified for inclusion in the review. Improvements were seen for recommended screening and other preventive care services completed by clinicians, recommended clinical tests completed by clinicians, and recommended treatments prescribed by clinicians (median increases of 3.8, 4.0, and 2.0 percentage points, respectively). Results were inconsistent for changes in CVD risk factors such as systolic and diastolic blood pressure, total and low-density lipoprotein cholesterol, and hemoglobin A1C levels.

CONCLUSIONS

CDSSs are effective in improving clinician practices related to screening and other preventive care services, clinical tests, and treatments. However, more evidence is needed from implementation of CDSSs within the broad context of comprehensive service delivery aimed at reducing CVD risk and CVD-related morbidity and mortality.

Qualitative analysis of vendor discussions on the procurement of Computerised Physician Order Entry and Clinical Decision Support systems in hospitals

OBJECTIVES

We studied vendor perspectives about potentially transferable lessons for implementing organisations and national strategies surrounding the procurement of Computerised Physician Order Entry (CPOE)/Clinical Decision Support (CDS) systems in English hospitals.

SETTING

Data were collected from digitally audio-recorded discussions from a series of CPOE/CDS vendor round-table discussions held in September 2014 in the UK.

PARTICIPANTS

Nine participants, representing 6 key vendors operating in the UK, attended. The discussions were transcribed verbatim and thematically analysed.

RESULTS

Vendors reported a range of challenges surrounding the procurement and contracting processes of CPOE/CDS systems, including hospitals' inability to adequately assess their own needs and then select a suitable product, rushed procurement and implementation processes that resulted in difficulties in meaningfully engaging with vendors, as well as challenges relating to contracting leading to ambiguities in implementation roles. Consequently, relationships between system vendors and hospitals were often strained, the vendors attributing this to a lack of hospital management's appreciation of the complexities associated with implementation efforts. Future anticipated challenges included issues surrounding the standardisation of data to enable their aggregation across systems for effective secondary uses, and implementation of data exchange with providers outside the hospital.

CONCLUSIONS

Our results indicate that there are significant issues surrounding capacity to procure and optimise CPOE/CDS systems among UK hospitals. There is an urgent need to encourage more synergistic and collaborative working between providers and vendors and for a more centralised support for National Health Service hospitals, which draws on a wider body of experience, including a formalised procurement framework with value-based product specifications.