Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain

Do veterans with risky substance use (RSU) use distinct pain treatment modalities?

BACKGROUND AND OBJECTIVES

Risky substance use (RSU) is common among people with chronic pain and is associated with worse pain treatment outcomes. Nonopioid treatment is recommended, but it is unknown whether people with RSU use different or fewer pain treatment modalities. This study describes use of different pain treatments by veterans with and without RSU and those receiving versus not receiving opioid medication.

METHODS

Veterans (N = 924) who filed service-connected disability claims related to musculoskeletal conditions and rated their pain four or higher on the Numeric Rating Scale, reported on 25 different pain services in the preceding 90 days. Recent RSU was identified via Alcohol, Smoking, and Substance Involvement Test (ASSIST) cutoffs and/or nail sample toxicology.

RESULTS

Overall, RSU was not associated with number of provider-delivered or self-delivered pain modalities. Over-the-counter medications (71%), self-structured exercise (69%), and nonopioid prescription medications (38%) were the most used modalities. Veterans receiving prescribed opioids (8.4%) were more likely to see primary care, receive injections, and attend exercise and/or meditation classes, compared to those without opioid prescriptions.

DISCUSSION AND CONCLUSIONS

Opioid and nonopioid pain treatment utilization did not differ based on RSU, and those prescribed opioids were more likely to engage in other nonopioid pain treatments. Regardless of RSU, veterans appear willing to try provider-delivered (58%) and self-delivered (79%) pain treatment.

SCIENTIFIC SIGNIFICANCE

In this first-ever evaluation of 25 different pain treatment modalities among veterans with and without RSU, people with RSU did not use less treatment modalities.

Cluster-Randomized Comparative Effectiveness Trial of Physician-Directed Clinical Decision Support Versus Patient-Directed Education to Promote Appropriate Use of Opioids for Chronic Pain

We compared the effectiveness of physician-directed clinical decision support (CDS) administered via electronic health record versus patient-directed education to promote the appropriate use of opioids by conducting a cluster-randomized trial involving 82 primary care physicians and 951 of their patients with chronic pain. Primary outcomes were satisfaction with patient-physician communication consumer assessment of health care providers and system clinician and group survey (CG-CAHPS) and pain interference patient-reported outcomes measurement information system. Secondary outcomes included physical function (patient-reported outcomes measurement information system), depression (PHQ-9), high-risk opioid prescribing (>90 morphine milligram equivalents per day [≥90 mg morphine equivalent/day]), and co-prescription of opioids and benzodiazepines. We used multi-level regression to compare longitudinal difference-in-difference scores between arms. The odds of achieving the maximum CG-CAHPS score were 2.65 times higher in the patient education versus the CDS arm (P = .044; 95% confidence interval [CI] 1.03-6.80). However, baseline CG-CAHPS scores were dissimilar between arms, making these results challenging to interpret definitively. No difference in pain interference was found between groups (Coef = -0.64, 95% CI -2.66 to 1.38). The patient education arm experienced higher odds of Rx ≥ 90 milligrams morphine equivalent/day (odds ratio = 1.63; P = .010; 95% CI 1.13, 2.36). There were no differences between groups in physical function, depression, or co-prescription of opioids and benzodiazepines. These results suggest that patient-directed education may have the potential to improve satisfaction with patient-physician communication, whereas physician-directed CDS via electronic health records may have greater potential to reduce high-risk opioid dosing. More evidence is needed to ascertain the relative cost-effectiveness between strategies. PERSPECTIVE: This article presents the results of a comparative-effectiveness study of 2 broadly used communication strategies to catalyze dialog between patients and primary care physicians around chronic pain. The results add to the decision-making literature and offer insights about the relative benefits of physician-directed versus patient-directed interventions to promote the appropriate use of opioids.

An Online Acceptance and Mindfulness Intervention for Chronic Pain in Veterans: Development and Protocol for a Pilot Feasibility Randomized Controlled Trial

BACKGROUND

In the veteran community, chronic pain is particularly prevalent and often debilitating. Until recently, veterans with chronic pain were offered primarily pharmacological intervention options, which rarely suffice and can also have negative health consequences. To better address chronic pain in veterans, the Veterans Health Administration has invested in novel, nonpharmacological behavior interventions that target both pain management and chronic pain-related functional issues. One approach, acceptance and commitment therapy (ACT) for chronic pain, is supported by decades of efficacy evidence for improving pain outcomes; however, ACT can be difficult to obtain owing to issues such as a lack of trained therapists or veterans having difficulty committing to the time and resources needed for the full clinician-led ACT protocol. Given the strong ACT evidence base combined with access limitations, we set out to develop and evaluate Veteran ACT for Chronic Pain (VACT-CP), an online program guided by an embodied conversational agent to improve pain management and functioning.

OBJECTIVE

The aims of this study are to develop, iteratively refine, and then conduct a pilot feasibility randomized controlled trial (RCT) of a VACT-CP group (n=20) versus a waitlist and treatment-as-usual control group (n=20).

METHODS

This research project includes 3 phases. In phase 1, our research team consulted with pain and virtual care experts, developed the preliminary VACT-CP online program, and conducted interviews with providers to obtain their feedback on the intervention. In phase 2, we incorporated feedback from phase 1 into the VACT-CP program and completed initial usability testing with veterans with chronic pain. In phase 3, we are conducting a small pilot feasibility RCT, with the primary outcome being assessment of usability of the VACT-CP system.

RESULTS

This study is currently in phase 3; recruitment for the RCT began in April 2022 and is expected to continue through April 2023. Data collection is expected to be completed by October 2023, with full data analysis completed by late 2023.

CONCLUSIONS

The findings from this research project will provide information on the usability of the VACT-CP intervention, as well as secondary outcomes related to treatment satisfaction, pain outcomes (pain-related daily functioning and pain severity), ACT processes (pain acceptance, behavioral avoidance, and valued living), and mental and physical functioning.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03655132; https://clinicaltrials.gov/ct2/show/NCT03655132.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/45887.

Assessing the use of a clinical decision support tool for pain management in primary care

Objective

Given time constraints, poorly organized information, and complex patients, primary care providers (PCPs) can benefit from clinical decision support (CDS) tools that aggregate and synthesize problem-specific patient information. First, this article describes the design and functionality of a CDS tool for chronic noncancer pain in primary care. Second, we report on the retrospective analysis of real-world usage of the tool in the context of a pragmatic trial.

Materials and methods

The tool known as OneSheet was developed using user-centered principles and built in the Epic electronic health record (EHR) of 2 health systems. For each relevant patient, OneSheet presents pertinent information in a single EHR view to assist PCPs in completing guideline-recommended opioid risk mitigation tasks, review previous and current patient treatments, view patient-reported pain, physical function, and pain-related goals.

Results

Overall, 69 PCPs accessed OneSheet 2411 times (since November 2020). PCP use of OneSheet varied significantly by provider and was highly skewed (site 1: median accesses per provider: 17 [interquartile range (IQR) 9-32]; site 2: median: 8 [IQR 5-16]). Seven "power users" accounted for 70% of the overall access instances across both sites. OneSheet has been accessed an average of 20 times weekly between the 2 sites.

Discussion

Modest OneSheet use was observed relative to the number of eligible patients seen with chronic pain.

Conclusions

Organizations implementing CDS tools are likely to see considerable provider-level variation in usage, suggesting that CDS tools may vary in their utility across PCPs, even for the same condition, because of differences in provider and care team workflows.

Study protocol for a type III hybrid effectiveness-implementation trial to evaluate scaling interoperable clinical decision support for patient-centered chronic pain management in primary care

Background

The US continues to face public health crises related to both chronic pain and opioid overdoses. Thirty percent of Americans suffer from chronic noncancer pain at an estimated yearly cost of over $600 billion. Most patients with chronic pain turn to primary care clinicians who must choose from myriad treatment options based on relative risks and benefits, patient history, available resources, symptoms, and goals. Recently, with attention to opioid-related risks, prescribing has declined. However, clinical experts have countered with concerns that some patients for whom opioid-related benefits outweigh risks may be inappropriately discontinued from opioids. Unfortunately, primary care clinicians lack usable tools to help them partner with their patients in choosing pain treatment options that best balance risks and benefits in the context of patient history, resources, symptoms, and goals. Thus, primary care clinicians and patients would benefit from patient-centered clinical decision support (CDS) for this shared decision-making process.

Methods

The objective of this 3-year project is to study the adaptation and implementation of an existing interoperable CDS tool for pain treatment shared decision making, with tailored implementation support, in new clinical settings in the OneFlorida Clinical Research Consortium. Our central hypothesis is that tailored implementation support will increase CDS adoption and shared decision making. We further hypothesize that increases in shared decision making will lead to improved patient outcomes, specifically pain and physical function. The CDS implementation will be guided by the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. The evaluation will be organized by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. We will adapt and tailor PainManager, an open source interoperable CDS tool, for implementation in primary care clinics affiliated with the OneFlorida Clinical Research Consortium. We will evaluate the effect of tailored implementation support on PainManager’s adoption for pain treatment shared decision making. This evaluation will establish the feasibility and obtain preliminary data in preparation for a multi-site pragmatic trial targeting the effectiveness of PainManager and tailored implementation support on shared decision making and patient-reported pain and physical function.

Discussion

This research will generate evidence on strategies for implementing interoperable CDS in new clinical settings across different types of electronic health records (EHRs). The study will also inform tailored implementation strategies to be further tested in a subsequent hybrid effectiveness-implementation trial. Together, these efforts will lead to important new technology and evidence that patients, clinicians, and health systems can use to improve care for millions of Americans who suffer from pain and other chronic conditions.

Trial registration

ClinicalTrials.gov, NCT05256394, Registered 25 February 2022.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13012-022-01217-4.

The Role of Informatics in Implementing Guidelines for Chronic Opioid Therapy Risk Assessment in Primary Care: A Narrative Review Informed by the Socio-Technical Model

Approximately 2 million Americans live with opioid use disorder (OUD), most of whom also have chronic pain. The economic burden of chronic pain and prescription opioid misuse runs into billions of dollars. Patients on prescription opioids for chronic non-cancer pain (CNCP) are at increased risk for OUD and overdose. By adhering to the Center for Disease Control and Prevention (CDC) opioid prescribing guidelines, primary care providers (PCPs) have the potential to improve patient outcomes. But numerous provider, patient, and practice-specific factors challenge adherence to guidelines in primary care. Many of the barriers may be mediated by informatics interventions, but gaps in knowledge and unmet needs exist. This narrative review examines the risk assessment and harm reduction process in a socio-technical context to highlight the gaps in knowledge and unmet needs that can be mediated through informatics intervention.

Evaluating a Prototype Clinical Decision Support Tool for Chronic Pain Treatment in Primary Care

OBJECTIVES

 The Chronic Pain Treatment Tracker (Tx Tracker) is a prototype decision support tool to aid primary care clinicians when caring for patients with chronic noncancer pain. This study evaluated clinicians' perceived utility of Tx Tracker in meeting information needs and identifying treatment options, and preferences for visual design.

METHODS

 We conducted 12 semi-structured interviews with primary care clinicians from four health systems in Indiana. The interviews were conducted in two waves, with prototype and interview guide revisions after the first six interviews. The interviews included exploration of Tx Tracker using a think-aloud approach and a clinical scenario. Clinicians were presented with a patient scenario and asked to use Tx Tracker to make a treatment recommendation. Last, participants answered several evaluation questions. Detailed field notes were collected, coded, and thematically analyzed by four analysts.

RESULTS

 We identified several themes: the need for clinicians to be presented with a comprehensive patient history, the usefulness of Tx Tracker in patient discussions about treatment planning, potential usefulness of Tx Tracker for patients with high uncertainty or risk, potential usefulness of Tx Tracker in aggregating scattered information, variability in expectations about workflows, skepticism about underlying electronic health record data quality, interest in using Tx Tracker to annotate or update information, interest in using Tx Tracker to translate information to clinical action, desire for interface with visual cues for risks, warnings, or treatment options, and desire for interactive functionality.

CONCLUSION

 Tools like Tx Tracker, by aggregating key information about past, current, and potential future treatments, may help clinicians collaborate with their patients in choosing the best pain treatments. Still, the use and usefulness of Tx Tracker likely relies on continued improvement of its functionality, accurate and complete underlying data, and tailored integration with varying workflows, care team roles, and user preferences.

The effect of decision support systems on pain in patients with cancer: A systematic review and meta-analysis of randomized controlled trials

PURPOSE

This study was conducted to systematically examine the effect of decision support systems (DSSs) applied to patients with cancer on pain severity.

REVIEW METHODS

Systematic review and meta-analysis. A search was done on Web of Science, Science Direct, PubMed, ProQuest, EBSCOhost/CINAHL Complete, Scopus, Springer Link, Cochrane Library, and Ovid databases, which covered a period until September 2021. Meta-analysis of the data was conducted on the CMA 3 software package. Comprehensive reviews were conducted by two independent researchers in line with the PICOS criteria. The study was conducted according to the PRISMA checklist.

FINDINGS

Five randomized controlled trials with 1.880 participants were included in this systematic review and meta-analysis. In the study, visits, consultations, simulation of patient outcomes, telephone support, and email applications were employed for periods ranging from 6 weeks to 6 months. The evaluation of the meta-analysis results indicated that DSSs had positive effects on pain severity in patients with cancer (Hedge's g = 0.22; p < 0.001).

CONCLUSION

The findings of this systematic review and meta-analysis show that DSSs can be used as an effective and comfortable technological application in reducing the severity of pain in patients with cancer.

CLINICAL RELEVANCE

The use of DSSs for pain severity in patients with cancer is an effective method. In line with the findings of this systematic review and meta-analysis, awareness and knowledge levels of all health disciplines about DSSs will increase. It is believed that the use of DSSs to improve patient-centered care will be guiding.

Clinical decision support systems-based interventions to improve medication outcomes: A systematic literature review on features and effects

Background: Clinical decision support systems (CDSSs) interventions were used to improve the life quality and safety in patients and also to improve practitioner performance, especially in the field of medication. Therefore, the aim of the paper was to summarize the available evidence on the impact, outcomes and significant factors on the implementation of CDSS in the field of medicine. Methods: This study is a systematic literature review. PubMed, Cochrane Library, Web of Science, Scopus, EMBASE, and ProQuest were investigated by 15 February 2017. The inclusion requirements were met by 98 papers, from which 13 had described important factors in the implementation of CDSS, and 86 were medicated-related. We categorized the system in terms of its correlation with medication in which a system was implemented, and our intended results were examined. In this study, the process outcomes (such as; prescription, drug-drug interaction, drug adherence, etc.), patient outcomes, and significant factors affecting the implementation of CDSS were reviewed. Results: We found evidence that the use of medication-related CDSS improves clinical outcomes. Also, significant results were obtained regarding the reduction of prescription errors, and the improvement in quality and safety of medication prescribed. Conclusion: The results of this study show that, although computer systems such as CDSS may cause errors, in most cases, it has helped to improve prescribing, reduce side effects and drug interactions, and improve patient safety. Although these systems have improved the performance of practitioners and processes, there has not been much research on the impact of these systems on patient outcomes.

Opioids, Polypharmacy, and Drug Interactions: A Technological Paradigm Shift Is Needed to Ameliorate the Ongoing Opioid Epidemic

Polypharmacy is a common phenomenon among adults using opioids, which may influence the frequency, severity, and complexity of drug–drug interactions (DDIs) experienced. Clinicians must be able to easily identify and resolve DDIs since opioid-related DDIs are common and can be life-threatening. Given that clinicians often rely on technological aids—such as clinical decision support systems (CDSS) and drug interaction software—to identify and resolve DDIs in patients with complex drug regimens, this narrative review provides an appraisal of the performance of existing technologies. Opioid-specific CDSS have several system- and content-related limitations that need to be overcome. Specifically, we found that these CDSS often analyze DDIs in a pairwise manner, do not account for relevant pharmacogenomic results, and do not integrate well with electronic health records. In the context of polypharmacy, existing systems may encourage inadvertent serious alert dismissal due to the generation of multiple incoherent alerts. Future technological systems should minimize alert fatigue, limit manual input, allow for simultaneous multidrug interaction assessments, incorporate pharmacogenomic data, conduct iterative risk simulations, and integrate seamlessly with normal workflow.

Nurses' decision-making about cancer patients' end-of-life skin care in Wales: an exploratory mixed-method vignette study protocol

INTRODUCTION

Patients with cancer are at high risk of developing pressure ulcers at the end of life as a result of their underlying condition or cancer treatment. There are many guidelines which set out best practice with regard to end-of-life skin care. However, the complexity of palliative cancer care often means that it is challenging for nurses to make the appropriate person-centred decisions about end-of-life skin care. This study seeks to explore the perceived importance that nurses place on different factors in their end-of-life skin care for patients with cancer. The utility, face validity and content validity of a prototype decision-making tool for end-of-life skin care will also be evaluated.

METHODS AND ANALYSIS

A mixed-method design will be used to gather data from primary and secondary care nurses working in different hospitals and local authority areas across Wales. Clinical vignettes will be used to gather qualitative and quantitative data from nurses in individual interviews. Qualitative data will be subject to thematic analysis and quantitative data will be subject to descriptive statistical analysis. Qualitative and quantitative data will then be synthesised, which will enhance the rigour of this study, and pertinently inform the further development of an end-of-life skin care decision-making tool for patients with cancer.

ETHICS AND DISSEMINATION

Ethical approval to undertake the study has been granted by Cardiff University School of Healthcare Sciences Research Governance and Ethics Screening Committee. Informed consent will be obtained in writing from all the participants in this study. The results of this study will be disseminated through journal articles, as well as presentations at national and international conferences. We will also report our findings to patient and public involvement groups with an interest in improving cancer care, palliative care as well as skin care.

Confronting challenges to opioid risk mitigation in the U.S. health system: Recommendations from a panel of national experts

BACKGROUND

Amid the ongoing U.S. opioid crisis, achieving safe and effective chronic pain management while reducing opioid-related morbidity and mortality is likely to require multi-level efforts across health systems, including the Military Health System (MHS), Department of Veterans Affairs (VA), and civilian sectors.

OBJECTIVE

We conducted a series of qualitative panel discussions with national experts to identify core challenges and elicit recommendations toward improving the safety of opioid prescribing in the U.S.

DESIGN

We invited national experts to participate in qualitative panel discussions regarding challenges in opioid risk mitigation and how best to support providers in delivery of safe and effective opioid prescribing across MHS, VA, and civilian health systems.

PARTICIPANTS

Eighteen experts representing primary care, emergency medicine, psychology, pharmacy, and public health/policy participated.

APPROACH

Six qualitative panel discussions were conducted via teleconference with experts. Transcripts were coded using team-based qualitative content analysis to identify key challenges and recommendations in opioid risk mitigation.

KEY RESULTS

Panelists provided insight into challenges across multiple levels of the U.S. health system, including the technical complexity of treating chronic pain, the fraught national climate around opioids, the need to integrate surveillance data across a fragmented U.S. health system, a lack of access to non-pharmacological options for chronic pain care, and difficulties in provider and patient communication. Participating experts identified recommendations for multi-level change efforts spanning policy, research, education, and the organization of healthcare delivery.

CONCLUSIONS

Reducing opioid risk while ensuring safe and effective pain management, according to participating experts, is likely to require multi-level efforts spanning military, veteran, and civilian health systems. Efforts to implement risk mitigation strategies at the patient level should be accompanied by efforts to increase education for patients and providers, increase access to non-pharmacological pain care, and support use of existing clinical decision support, including state-level prescription drug monitoring programs.

Medical knowledge infused convolutional neural networks for cohort selection in clinical trials

OBJECTIVE

In this era of digitized health records, there has been a marked interest in using de-identified patient records for conducting various health related surveys. To assist in this research effort, we developed a novel clinical data representation model entitled medical knowledge-infused convolutional neural network (MKCNN), which is used for learning the clinical trial criteria eligibility status of patients to participate in cohort studies.

MATERIALS AND METHODS

In this study, we propose a clinical text representation infused with medical knowledge (MK). First, we isolate the noise from the relevant data using a medically relevant description extractor; then we utilize log-likelihood ratio based weights from selected sentences to highlight "met" and "not-met" knowledge-infused representations in bichannel setting for each instance. The combined medical knowledge-infused representation (MK) from these modules helps identify significant clinical criteria semantics, which in turn renders effective learning when used with a convolutional neural network architecture.

RESULTS

MKCNN outperforms other Medical Knowledge (MK) relevant learning architectures by approximately 3%; notably SVM and XGBoost implementations developed in this study. MKCNN scored 86.1% on F1metric, a gain of 6% above the average performance assessed from the submissions for n2c2 task. Although pattern/rule-based methods show a higher average performance for the n2c2 clinical data set, MKCNN significantly improves performance of machine learning implementations for clinical datasets.

CONCLUSION

MKCNN scored 86.1% on the F1 score metric. In contrast to many of the rule-based systems introduced during the n2c2 challenge workshop, our system presents a model that heavily draws on machine-based learning. In addition, the MK representations add more value to clinical comprehension and interpretation of natural texts.

Development of Decision Support System to Predict Neurofeedback Response in ADHD: an Artificial Neural Network Approach

INTRODUCTION

Clinical decision support system (CDSS) is an analytical tool that converts raw data into useful information to help clinicians make better decisions for patients.

AIM

The purpose of this study was to investigate the efficacy of neurofeedback (NF), in Attention Deficit Hyperactivity Disorder (ADHD) by the development of CDSS based on artificial neural network (ANN).

METHODS

This study analyzed 122 patients with ADHD who underwent NF in the Parand-Human Potential Empowerment Institute in Tehran. The patients were divided into two groups according to the effects of NF: effective and non-effective groups. The patients' record information was mined by data mining techniques to identify effective features. Based on unsaturated condition of data and imbalanced classes between the patient groups (patients with successful NF response and those without it), the SMOTE technique was applied on dataset. Using MATLAB 2014a, a modular program was designed to test both multiple architectures of neural networks and their performance. Selected architecture of the neural networks was then applied in the procedure.

RESULTS

Eleven features from 28 features of the initial dataset were selected as effective features. Using the SMOTE technique, number of the samples rose to around 300 samples. Based on the multiple neural networks architecture testing, a network by 11-20-16-2 neurons was selected (specify>00.91%, sensivity=100%) and applied in the software.

CONCLUSION

The ANN used in this study has led to good results in sensivity, specificity, and AUC. The ANN and other intelligent techniques can be used as supportive tools for decision making by healthcare providers.

Efficacy of CPP-ACP and CPP-ACPF for Prevention and Remineralization of White Spot Lesions in Orthodontic Patients: a Systematic Review of Randomized Controlled Clinical Trials

INTRODUCTION

Enamel subsurface lesions or white spot lesions (WSLs) are commonly found in orthodontic patients with a prevalence of 5% to 97%.

AIM

This systematic review aimed to evaluate the efficacy of casein phosphopeptide amorphous calcium phosphate (CPP-ACP) and casein phosphopeptide amorphous calcium phosphate fluoride (CPP-ACPF) for prevention and remineralization of WSLs in orthodontic patients in human randomized controlled clinical trials (RCTs).

METHODS

Relevant articles were retrieved by searching the Web of Science, Scopus, PubMed, and Cochrane Library databases up to November 2018 with no language or date restriction. The collected data included examination method, groups included in each study with number of patients in each group, study design, follow-up period and summary of important findings of each study. The risk of bias of each study was assessed according to the guidelines of the Cochrane Collaboration's tool.

RESULTS

Of 213 articles retrieved, 13 RCTs were included in this systematic review (none of them were included in the meta-analysis). Three articles showed superior efficacy of CPP-ACP for remineralization of WSLs while four studies reported the superior clinical efficacy of CPP-ACPF for this purpose.

CONCLUSION

Both CPP-ACP and CPP-ACPF can decrease the prevalence and increase the remineralization of WSLs during/after orthodontic treatment.

Optimizing pain treatment interventions (OPTI): A pilot randomized controlled trial of collaborative care to improve chronic pain management and opioid safety-Rationale, methods, and lessons learned

Veterans seeking care in VA medical facilities have high rates of chronic pain, which often co-occur with mental health and substance use disorders, including prescription opioid misuse. The overall goal of the Optimizing Pain Treatment Interventions (OPTI) study was to pilot a 12-week Collaborative Care intervention to improve opioid safety, chronic pain disability, and use of non-pharmacological pain management strategies in veterans in VA primary care. Between November 2014 and January 2017, 100 veteran patients with chronic pain and high-risk prescription opioid use (e.g., high-dose therapy, early refills, etc.) were enrolled and completed an initial one-hour study visit with a primary care provider (PCP) within 4 weeks of enrollment. Study PCPs were guided by a web-based opioid management decision support program and templated notes in the VA electronic medical record. After assessment and education, study PCPs used Shared Decision-Making to formulate a Pain Care Plan aligned with a participant's personal values and goals. After the initial visit, patients randomized to Collaborative Care received one Motivational Interviewing (MI) session with a Care Manager followed by 3 Care Manager-delivered brief telephone MI sessions at 6, 8, and 12 weeks to reinforce Pain Care Plans; patients randomized to an Attention Control condition met with a Care Manager briefly, followed by 3 brief scripted telephone psychoeducation sessions at 6, 8, and 12 weeks. Masked evaluators assessed outcomes at baseline, end of intervention (12 weeks), and after eight weeks of no contact (20 weeks). We present study rationale, detailed methods, preliminary results and lessons learned.

Selecting Test Cases from the Electronic Health Record for Software Testing of Knowledge-Based Clinical Decision Support Systems

Software testing of knowledge-based clinical decision support systems is challenging, labor intensive, and expensive; yet, testing is necessary since clinical applications have heightened consequences. Thoughtful test case selection improves testing coverage while minimizing testing burden. ATHENA-CDS is a knowledge-based system that provides guideline-based recommendations for chronic medical conditions. Using the ATHENA-CDS diabetes knowledgebase, we demonstrate a generalizable approach for selecting test cases using rules/ filters to create a set of paths that mimics the system's logic. Test cases are allocated to paths using a proportion heuristic. Using data from the electronic health record, we found 1,086 cases with glycemic control above target goals. We created a total of 48 filters and 50 unique system paths, which were used to allocate 200 test cases. We show that our method generates a comprehensive set of test cases that provides adequate coverage for the testing of a knowledge-based CDS.

Use of Electronic Health Record Integration for Down Syndrome Guidelines

OBJECTIVES

Established guidelines from the American Academy of Pediatrics for the care of patients with Down syndrome are often not followed. Our goal was to integrate aspects of the guidelines into the electronic health record (EHR) to improve guideline adherence throughout a child's life span.

METHODS

Two methods of EHR integration with age-based logic were created and implemented in June 2016: (1) a best-practice advisory that prompts an order for referral to genetics; and (2) a health maintenance record that tracks completion of complete blood cell count and/or hemoglobin testing, thyrotropin testing, echocardiogram, and sleep study. Retrospective chart review of patients with Down syndrome and visits to locations with EHR integration (NICUs, primary care centers, and genetics clinics) assessed adherence to the components of EHR integration; the impact was analyzed through statistical process control charts.

RESULTS

From July 2015 to October 2017, 235 patients with Down syndrome (ages 0 to 32 years) had 466 visits to the EHR integration locations. Baseline adherence for individual components ranged from 51% (sleep study and hemoglobin testing) to 94% (echocardiogram). EHR integration was associated with a shift in adherence to all select recommendations from 61.6% to 77.3% (P < .001) including: genetic counseling, complete blood cell count and/or hemoglobin testing, thyrotropin testing, echocardiogram, and sleep study.

CONCLUSIONS

Integrating specific aspects of Down syndrome care into the EHR can improve adherence to guideline recommendations that span the life of a child. Future quality improvement should be focused on older children and adults with Down syndrome.

ProFUSO: Business process and ontology-based framework to develop ubiquitous computing support systems for chronic patients' management

New advances in telemedicine, ubiquitous computing, and artificial intelligence have supported the emergence of more advanced applications and support systems for chronic patients. This trend addresses the important problem of chronic illnesses, highlighted by multiple international organizations as a core issue in future healthcare. Despite the myriad of exciting new developments, each application and system is designed and implemented for specific purposes and lacks the flexibility to support different healthcare concerns. Some of the known problems of such developments are the integration issues between applications and existing healthcare systems, the reusability of technical knowledge in the creation of new and more sophisticated systems and the usage of data gathered from multiple sources in the generation of new knowledge. This paper proposes a framework for the development of chronic disease support systems and applications as an answer to these shortcomings. Through this framework our pursuit is to create a common ground methodology upon which new developments can be created and easily integrated to provide better support to chronic patients, medical staff and other relevant participants. General requirements are inferred for any support system from the primary attention process of chronic patients by the Business Process Management Notation. Numerous technical approaches are proposed to design a general architecture that considers the medical organizational requirements in the treatment of a patient. A framework is presented for any application in support of chronic patients and evaluated by a case study to test the applicability and pertinence of the solution.

Utilizing patient data from the veterans administration electronic health record to support web-based clinical decision support: informatics challenges and issues from three clinical domains

BACKGROUND

The US Veterans Administration (VA) has developed a robust and mature computational infrastructure in support of its electronic health record (EHR). Web technology offers a powerful set of tools for structuring clinical decision support (CDS) around clinical care. This paper describes informatics challenges and design issues that were confronted in the process of building three Web-based CDS systems in the context of the VA EHR.

METHODS

Over the course of several years, we implemented three Web-based CDS systems that extract patient data from the VA EHR environment to provide patient-specific CDS. These were 1) the VACS (Veterans Aging Cohort Study) Index Calculator which estimates prognosis for HIV+ patients, 2) Neuropath/CDS which assists in the medical management of patients with neuropathic pain, and 3) TRIM (Tool to Reduce Inappropriate Medications) which identifies potentially inappropriate medications in older adults and provides recommendations for improving the medication regimen.

RESULTS

The paper provides an overview of the VA EHR environment and discusses specific informatics issues/challenges that arose in the context of each of the three Web-based CDS systems. We discuss specific informatics methods and provide details of approaches that may be useful within this setting.

CONCLUSIONS

Informatics issues and challenges relating to data access and data availability arose because of the particular architecture of the national VA infrastructure and the need to link to that infrastructure from local Web-based CDS systems. Idiosyncrasies of VA patient data, especially the medication data, also posed challenges. Other issues related to specific functional needs of individual CDS systems. The goal of this paper is to describe these issues so that our experience may serve as a useful foundation to assist others who wish to build such systems in the future.

Iterative User Interface Design for Automated Sequential Organ Failure Assessment Score Calculator in Sepsis Detection

Background

The new sepsis definition has increased the need for frequent sequential organ failure assessment (SOFA) score recalculation and the clerical burden of information retrieval makes this score ideal for automated calculation.

Objective

The aim of this study was to (1) estimate the clerical workload of manual SOFA score calculation through a time-motion analysis and (2) describe a user-centered design process for an electronic medical record (EMR) integrated, automated SOFA score calculator with subsequent usability evaluation study.

Methods

First, we performed a time-motion analysis by recording time-to-task-completion for the manual calculation of 35 baseline and 35 current SOFA scores by 14 internal medicine residents over a 2-month period. Next, we used an agile development process to create a user interface for a previously developed automated SOFA score calculator. The final user interface usability was evaluated by clinician end users with the Computer Systems Usability Questionnaire.

Results

The overall mean (standard deviation, SD) time-to-complete manual SOFA score calculation time was 61.6 s (33). Among the 24% (12/50) usability survey respondents, our user-centered user interface design process resulted in >75% favorability of survey items in the domains of system usability, information quality, and interface quality.

Conclusions

Early stakeholder engagement in our agile design process resulted in a user interface for an automated SOFA score calculator that reduced clinician workload and met clinicians’ needs at the point of care. Emerging interoperable platforms may facilitate dissemination of similarly useful clinical score calculators and decision support algorithms as “apps.” A user-centered design process and usability evaluation should be considered during creation of these tools.

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.

Assessing the accuracy of opioid overdose and poisoning codes in diagnostic information from electronic health records, claims data, and death records

PURPOSE

The purpose of this study is to assess positive predictive value (PPV), relative to medical chart review, of International Classification of Diseases (ICD)-9/10 diagnostic codes for identifying opioid overdoses and poisonings.

METHODS

Data were obtained from Kaiser Permanente Northwest and Northern California. Diagnostic data from electronic health records, submitted claims, and state death records from Oregon, Washington, and California were linked. Individual opioid-related poisoning codes (e.g., 965.xx and X42), and adverse effects of opioids codes (e.g., E935.xx) combined with diagnoses possibly indicative of overdoses (e.g., respiratory depression), were evaluated by comparison with chart audits.

RESULTS

Opioid adverse effects codes had low PPV to detect overdoses (13.4%) as assessed in 127 charts and were not pursued. Instead, opioid poisoning codes were assessed in 2100 individuals who had those codes present in electronic health records in the period between the years 2008 and 2012. Of these, 10/2100 had no available information and 241/2100 were excluded potentially as anesthesia-related. Among the 1849 remaining individuals with opioid poisoning codes, 1495 events were accurately identified as opioid overdoses; 69 were miscodes or misidentified, and 285 were opioid adverse effects, not overdoses. Thus, PPV was 81%. Opioid adverse effects or overdoses were accurately identified in 1780 of 1849 events (96.3%).

CONCLUSIONS

Opioid poisoning codes have a predictive value of 81% to identify opioid overdoses, suggesting ICD opioid poisoning codes can be used to monitor overdose rates and evaluate interventions to reduce overdose. Further research to assess sensitivity, specificity, and negative predictive value are ongoing. Copyright © 2017 John Wiley & Sons, Ltd.

Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention

BACKGROUND

Management of uncontrolled symptoms is an important component of quality cancer care. Clinical guidelines are available for optimal symptom management, but are not often integrated into the front lines of care. The use of clinical decision support (CDS) at the point-of-care is an innovative way to incorporate guideline-based symptom management into routine cancer care.

OBJECTIVE

The objective of this study was to develop and evaluate a rule-based CDS system to enable management of multiple symptoms in lung cancer patients at the point-of-care.

METHODS

This study was conducted in three phases involving a formative evaluation, a system evaluation, and a contextual evaluation of clinical use. In Phase 1, we conducted iterative usability testing of user interface prototypes with patients and health care providers (HCPs) in two thoracic oncology clinics. In Phase 2, we programmed complex algorithms derived from clinical practice guidelines into a rules engine that used Web services to communicate with the end-user application. Unit testing of algorithms was conducted using a stack-traversal tree-spanning methodology to identify all possible permutations of pathways through each algorithm, to validate accuracy. In Phase 3, we evaluated clinical use of the system among patients and HCPs in the two clinics via observations, structured interviews, and questionnaires.

RESULTS

In Phase 1, 13 patients and 5 HCPs engaged in two rounds of formative testing, and suggested improvements leading to revisions until overall usability scores met a priori benchmarks. In Phase 2, symptom management algorithms contained between 29 and 1425 decision nodes, resulting in 19 to 3194 unique pathways per algorithm. Unit testing required 240 person-hours, and integration testing required 40 person-hours. In Phase 3, both patients and HCPs found the system usable and acceptable, and offered suggestions for improvements.

CONCLUSIONS

A rule-based CDS system for complex symptom management was systematically developed and tested. The complexity of the algorithms required extensive development and innovative testing. The Web service-based approach allowed remote access to CDS knowledge, and could enable scaling and sharing of this knowledge to accelerate availability, and reduce duplication of effort. Patients and HCPs found the system to be usable and useful.

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.

Clinical Decision Support Tool for Parental Tobacco Treatment in Hospitalized Children

OBJECTIVES

To create and evaluate the feasibility, acceptability, and usability of a clinical decision support (CDS) tool within the electronic health record (EHR) to help pediatricians provide smoking cessation counseling and treatment to parents of hospitalized children exposed to secondhand smoke (SHS).

METHODS

Mixed method study of first-year pediatric residents on one inpatient unit. Residents received training in smoking cessation counseling, nicotine replacement therapy (NRT) prescribing, and use of a CDS tool to aid in this process. The tool, which alerted when a patient was identified as exposed to SHS based on the history taken on admission or during a prior encounter, had the following capabilities: adding SHS exposure to the patient's problem list; referral to Free Quitline through discharge instructions; and linking to a printable NRT prescription form. We measured feasibility by EHR utilization data. We measured acceptability and usability of the tool by administering questionnaires to residents.

RESULTS

From June-August 2015, the alert triggered for 106 patients, and the tool was used for 52 (49%) patients. 41 (39%) patients had SHS exposure added to the problem list, 34 (32%) parents were referred to the Quitline through discharge instructions, and 15 (14%) parents were prescribed NRT. 10 out of 15 (67%) eligible pediatricians used the tool. All clinicians surveyed (9 out of 10) found the tool acceptable and rated its usability good to excellent (average System Usability Scale score was 85 out of 100, 95% CI, 76-93).

CONCLUSIONS

A non-interruptive CDS tool to help residents provide smoking cessation counseling in the hospital was feasible, acceptable, and usable. Future work will investigate impacts on patient outcomes.

Clinical Decision Support Tool for Parental Tobacco Treatment in Primary Care

OBJECTIVES

We created a clinical decision support (CDS) tool and evaluated its feasibility, acceptability, usability, and clinical impact within the electronic health record to help primary care pediatricians provide smoking cessation treatment to parents/caregivers who smoke.

METHODS

This prospective study of pediatric clinicians and parents was conducted at 1 urban primary care site. Clinicians received training in smoking cessation counseling, nicotine replacement therapy (NRT) prescribing, referral to an adult treatment program, and use of the CDS tool. The tool prompted clinicians to ask about secondhand smoke exposure, provide an electronic NRT prescription, and refer. Feasibility was measured by using electronic health record utilization data, and acceptability and usability were assessed with the use of clinician surveys. Parents reported clinical impact, including NRT accepted and used.

RESULTS

From June to August 2015, clinicians used the tool to screen for secondhand smoke exposure at 2286 (76%) of 3023 visits. Parent smokers were identified at 308 visits, and 165 parents (55% of smokers) were interested in and offered treatment. Twenty-four (80%) of 30 eligible pediatric clinicians used the tool. Ninety-four percent of clinicians surveyed (n = 17) were satisfied with the tool, and the average system usability scale score was 83 of 100 (good to excellent range). We reached 69 of 100 parents sampled who received treatment; 44 (64%) received NRT, and 17 (25%) were currently using NRT.

CONCLUSIONS

A CDS tool to help urban primary care pediatric clinicians provide smoking cessation treatment was feasible, acceptable, usable, and influenced clinical care. A larger scale investigation in varied practice settings is warranted.

Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: Protocol for a Randomized Study Funded by the US Department of Veterans Affairs Health Services Research and Development Program

BACKGROUND

Cognitive behavioral therapy (CBT) is one of the most effective treatments for chronic low back pain. However, only half of Department of Veterans Affairs (VA) patients have access to trained CBT therapists, and program expansion is costly. CBT typically consists of 10 weekly hour-long sessions. However, some patients improve after the first few sessions while others need more extensive contact.

OBJECTIVE

We are applying principles from "reinforcement learning" (a field of artificial intelligence or AI) to develop an evidence-based, personalized CBT pain management service that automatically adapts to each patient's unique and changing needs (AI-CBT). AI-CBT uses feedback from patients about their progress in pain-related functioning measured daily via pedometer step counts to automatically personalize the intensity and type of patient support. The specific aims of the study are to (1) demonstrate that AI-CBT has pain-related outcomes equivalent to standard telephone CBT, (2) document that AI-CBT achieves these outcomes with more efficient use of clinician resources, and (3) demonstrate the intervention's impact on proximal outcomes associated with treatment response, including program engagement, pain management skill acquisition, and patients' likelihood of dropout.

METHODS

In total, 320 patients with chronic low back pain will be recruited from 2 VA healthcare systems and randomized to a standard 10 sessions of telephone CBT versus AI-CBT. All patients will begin with weekly hour-long telephone counseling, but for patients in the AI-CBT group, those who demonstrate a significant treatment response will be stepped down through less resource-intensive alternatives including: (1) 15-minute contacts with a therapist, and (2) CBT clinician feedback provided via interactive voice response calls (IVR). The AI engine will learn what works best in terms of patients' personally tailored treatment plans based on daily feedback via IVR about their pedometer-measured step counts, CBT skill practice, and physical functioning. Outcomes will be measured at 3 and 6 months post recruitment and will include pain-related interference, treatment satisfaction, and treatment dropout. Our primary hypothesis is that AI-CBT will result in pain-related functional outcomes that are at least as good as the standard approach, and that by scaling back the intensity of contact that is not associated with additional gains in pain control, the AI-CBT approach will be significantly less costly in terms of therapy time.

RESULTS

The trial is currently in the start-up phase. Patient enrollment will begin in the fall of 2016 and results of the trial will be available in the winter of 2019.

CONCLUSIONS

This study will evaluate an intervention that increases patients' access to effective CBT pain management services while allowing health systems to maximize program expansion given constrained resources.

Paediatric models in motion: requirements for model-based decision support at the bedside

Optimal paediatric pharmacotherapy is reliant on a detailed understanding of the individual patient including their developmental status and disease state as well as the pharmaceutical agents he/she is receiving for treatment or management of side effects. Our appreciation for size and maturation effects on the pharmacokinetic/pharmacodynamic (PK/PD) phenomenon has improved to the point that we can develop predictive models that permit us to individualize therapy, especially in the situation where we are monitoring drug effects or therapeutic concentrations. The growth of efforts to guide paediatric pharmacotherapy via model-based decision support necessitates a coordinated and systematic approach to ensuring reliable and robust output to caregivers that represents the current standard of care and adheres to governance imposed by the host institution or coalition responsible. Model-based systems which guide caregivers on dosing paediatric patients in a more comprehensive manner are in development at several institutions. Care must be taken that these systems provide robust guidance with the current best practice. These systems must evolve as new information becomes available and ultimately are best constructed from diverse data representing global input on demographics, ethnic / racial diversity, diet and other lifestyle factors. Multidisciplinary involvement at the project team level is key to the ultimate clinical valuation. Likewise, early engagement of clinical champions is also critical for the success of model-based tools. Adherence to regulatory requirements as well as best practices with respect to software development and testing are essential if these tools are to be used as part of the routine standard of care.

Usability of clinical decision support system as a facilitator for learning the assistive technology adaptation process

The aim of this study was to evaluate the usability of Ontology Supported Computerized Assistive Technology Recommender (OSCAR), a Clinical Decision Support System (CDSS) for the assistive technology adaptation process, its impact on learning the matching process, and to determine the relationship between its usability and learnability. Two groups of expert and novice clinicians (total, n = 26) took part in this study. Each group filled out system usability scale (SUS) to evaluate OSCAR's usability. The novice group completed a learning questionnaire to assess OSCAR's effect on their ability to learn the matching process. Both groups rated OSCAR's usability as "very good", (M [SUS] = 80.7, SD = 11.6, median = 83.7) by the novices, and (M [SUS] = 81.2, SD = 6.8, median = 81.2) by the experts. The Mann-Whitney results indicated that no significant differences were found between the expert and novice groups in terms of OSCAR's usability. A significant positive correlation existed between the usability of OSCAR and the ability to learn the adaptation process (rs = 0.46, p = 0.04). Usability is an important factor in the acceptance of a system. The successful application of user-centered design principles during the development of OSCAR may serve as a case study that models the significant elements to be considered, theoretically and practically in developing other systems. Implications for Rehabilitation Creating a CDSS with a focus on its usability is an important factor for its acceptance by its users. Successful usability outcomes can impact the learning process of the subject matter in general, and the AT prescription process in particular. The successful application of User-Centered Design principles during the development of OSCAR may serve as a case study that models the significant elements to be considered, theoretically and practically. The study emphasizes the importance of close collaboration between the developers and the end users.

Adherence to prescription opioid monitoring guidelines among residents and attending physicians in the primary care setting

OBJECTIVE

The aim of this study was to compare adherence to opioid prescribing guidelines and potential opioid misuse in patients of resident vs attending physicians.

DESIGN

Retrospective cross-sectional study.

SETTING

Large primary care practice at a safety net hospital in New England.

SUBJECTS

Patients 18-89 years old, with at least one visit to the primary care clinic within the past year and were prescribed long-term opioid treatment for chronic noncancer pain.

METHODS

Data were abstracted from the electronic medical record by a trained data analyst through a clinical data warehouse. The primary outcomes were adherence to any one of two American Pain Society Guidelines: (1) documentation of at least one opioid agreement (contract) ever and (2) any urine drug testing in the past year, and evidence of potential prescription misuse defined as ≥2 early refills. We employed logistic regression analysis to assess whether patients' physician status predicts guideline adherence and/or potential opioid misuse.

RESULTS

Similar proportions of resident and attending patients had a controlled substance agreement (45.1% of resident patients vs. 42.4% of attending patient, P = 0.47) or urine drug testing (58.6% of resident patients vs. 63.6% of attending patients, P = 0.16). Resident patients were more likely to have two or more early refills in the past year relative to attending patients (42.8% vs. 32.5%; P = 0.004). In the adjusted regression analysis, resident patients were more likely to receive early refills (odds ratio 1.82, 95% confidence interval 1.26-2.62) than attending patients.

CONCLUSIONS

With some variability, residents and attending physicians were only partly compliant with national guidelines. Residents were more likely to manage patients with a higher likelihood of opioid misuse.

Exploring a clinically friendly web-based approach to clinical decision support linked to the electronic health record: design philosophy, prototype implementation, and framework for assessment

BACKGROUND

Computer-based clinical decision support (CDS) is an important component of the electronic health record (EHR). As an increasing amount of CDS is implemented, it will be important that this be accomplished in a fashion that assists in clinical decision making without imposing unacceptable demands and burdens upon the provider's practice.

OBJECTIVE

The objective of our study was to explore an approach that allows CDS to be clinician-friendly from a variety of perspectives, to build a prototype implementation that illustrates features of the approach, and to gain experience with a pilot framework for assessment.

METHODS

The paper first discusses the project's design philosophy and goals. It then describes a prototype implementation (Neuropath/CDS) that explores the approach in the domain of neuropathic pain and in the context of the US Veterans Administration EHR. Finally, the paper discusses a framework for assessing the approach, illustrated by a pilot assessment of Neuropath/CDS.

RESULTS

The paper describes the operation and technical design of Neuropath/CDS, as well as the results of the pilot assessment, which emphasize the four areas of focus, scope, content, and presentation.

CONCLUSIONS

The work to date has allowed us to explore various design and implementation issues relating to the approach illustrated in Neuropath/CDS, as well as the development and pilot application of a framework for assessment.

Changes in opioid prescribing for Washington workers' compensation claimants after implementation of an opioid dosing guideline for chronic noncancer pain: 2004 to 2010

An opioid overdose epidemic emerged in the United States following increased opioid prescribing for chronic noncancer pain. In 2007, Washington State agencies implemented an opioid dosing guideline on safe prescribing for chronic noncancer pain. The objective of this population-based observational study was to evaluate opioid use and dosing before and after guideline implementation. We identified 161,283 workers aged 18 to 64 years with ≥1 opioid prescriptions in Washington Workers' Compensation, April 1, 2004, to December 31, 2010. Prevalence and incidence rates of opioid use were assessed. We compared pre- and postguideline chronic and high-dose use (≥120 mg/d) among incident users. The mean monthly prevalence of opioid use declined by 25.6% between 2004 (14.4%) and 2010 (10.7%). Fewer incident users went on to chronic opioid therapy in the postguideline period (4.7%; 95% confidence interval [CI], 4.5-5.0%) than in the preguideline period (6.3%; 95% CI, 6.1-6.6%). Compared with preguideline incident users, postguideline incident users were 35% less likely to receive high doses (adjusted odds ratio = .65; 95% CI, .59-.71). Although the extent to which decreases were due to the guidelines is uncertain, to our knowledge, this is the first report of significant decreases in chronic and high-dose prescription opioid use among incident users.

PERSPECTIVE

Evidence-based strategies for opioid risk management are needed to help abate the epidemic of opioid-related morbidity and mortality. The study findings suggest that opioid dosing guidelines that specify a "yellow flag" dosing threshold may be a useful tool in preventing escalation of doses into ranges associated with increased mortality risk.

Improving opioid prescription practices and reducing patient risk in the primary care setting

Chronic pain is complex, and the patient suffering from chronic pain frequently experiences concomitant medical and psychiatric disorders, including mood and anxiety disorders, and in some cases substance use disorders. Ideally these patients would be referred to an interdisciplinary pain program staffed by pain medicine, behavioral health, and addiction specialists. In practice, the majority of patients with chronic pain are managed in the primary care setting. The primary care clinician typically has limited time, training, or access to resources to effectively and efficiently evaluate, treat, and monitor these patients, particularly when there is the added potential liability of prescribing opioids. This paper reviews the role of opioids in managing chronic noncancer pain, including efficacy and risk for misuse, abuse, and addiction, and discusses several models employing novel technologies and health delivery systems for risk assessment, intervention, and monitoring of patients receiving opioids in a primary care setting.

Measurement of adherence to clinical practice guidelines for opioid therapy for chronic pain

The safe and effective prescribing of opioid therapy for chronic pain has become a significant health care priority over the last several years. Substantial research has focused on patient-oriented interventions toward preventing problematic use, but provider and system level factors may be more amenable to quality improvement approaches. Here, we outline administrative data-based metrics that are intended to assess adherence to key practices outlined in the 2010 Department of Veterans Affairs/Department of Defense Clinical Practice Guideline for management of opioid therapy for chronic pain. In addition to the metrics, we discuss their development process, which was done in consultation with experts on chronic opioid therapy.

Australian survey of current practice and guideline use in adult cancer pain assessment and management: perspectives of oncologists

AIMS

Cancer pain continues to be undertreated in up to half of cases, despite the availability of evidence-based guidelines. This study aimed to: (i) identify barriers and facilitators to adult cancer pain assessment and management, as perceived by Australian health professionals; (ii) establish the perceived need for new Australian guidelines and implementation strategy; (iii) identify which guidelines are used; (iv) identify barriers and facilitators to guideline use. This article focuses on the perceptions of responding oncologists.

METHODS

A cross-sectional survey was administered online. Invitations were circulated via peak bodies and clinical leaders. Comments were coded independently by two researchers.

RESULTS

In all 76 oncologists self-reported high concordance with evidence-based recommendations, except validated pain scales. Perceived barriers to pain management included insufficient non-pharmacological interventions, access to /coordination between services, and time. Only 22 percent of respondents reported using pain guidelines. Perceived barriers to guideline use included lack of access, awareness and any single standard. Respondents were generally supportive of new Australian guidelines and especially an implementation strategy.

CONCLUSION

Barriers to evidence-based practice and guideline use identified by our survey might be addressed via a clinical pathway that gives step-by-step guidance on evidence-based practice along with a framework for evaluation. Particular attention should be paid to promoting use of validated scales, patient education and non-pharmacological interventions, training of an appropriately skilled workforce and improving care coordination. Challenges are discussed.

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.

Managing pain in high-risk patients within a patient-centered medical home

Chronic pain remains a major healthcare problem despite noteworthy advancements in diagnostics, pharmacotherapy, and invasive and non-invasive interventions. The prevalence of chronic pain in the United States is staggering and continues to grow, and the personal and societal costs are not inconsequential. The etiology of pain is complex, and individuals suffering from chronic pain tend to have significant medical and psychiatric comorbidities such as depression, anxiety, and in some cases, substance use disorders. There is great concern regarding the burgeoning rate of prescription opioid misuse/abuse both for non-medical use and in pain patients receiving chronic opioid therapy. While there is ongoing debate about the "true" incidence of opioid abuse in the pain population, clearly, patients afflicted with both pain and substance use disorder are particularity challenging. The majority of patients with chronic pain including those with co-occurring substance use disorders are managed in the primary care setting. Primary care practitioners have scant time, resources and training to effectively assess, treat and monitor these complicated cases. A number of evidence- and expert consensus-based treatment guidelines on opioid therapy and risk mitigation have been developed but they have been underutilized in both specialty and primary care clinics. This article will discuss the utilization of new technologies and delivery systems for risk stratification, intervention and monitoring of patients with pain receiving opioid.

Opioid use in primary care: asking the right questions

Pain is one of the most common reasons that patients seek treatment from health care professionals, often their primary care providers. One tool for treating pain is opioid therapy, and opioid prescriptions have increased dramatically in recent years in the United States. This article will review recent research about opioids that is most relevant to treating chronic pain in the context of a typical primary care practice. It will focus on four key practices that providers can engage in before and during the course of opioid therapy that we believe will enhance the likelihood that opioids, when used, are an effective tool for pain management: avoiding sole reliance on opioids; using adequate opioid doses to address pain; mitigating the risk of opioid misuse by patients; and fostering collaborative relationships for treating complex patients.