Determinants of appropriate antibiotic and NSAID prescribing in unscheduled outpatient settings in the veterans health administration.

BACKGROUND: Despite efforts to enhance the quality of medication prescribing in outpatient settings, potentially inappropriate prescribing remains common, particularly in unscheduled settings where patients can present with infectious and pain-related complaints. Two of the most commonly prescribed medication classes in outpatient settings with frequent rates of potentially inappropriate prescribing include antibiotics and nonsteroidal anti-inflammatory drugs (NSAIDs). In the setting of persistent inappropriate prescribing, we sought to understand a diverse set of perspectives on the determinants of inappropriate prescribing of antibiotics and NSAIDs in the Veterans Health Administration. METHODS: We conducted a qualitative study guided by the Consolidated Framework for Implementation Research and Theory of Planned Behavior. Semi-structured interviews were conducted with clinicians, stakeholders, and Veterans from March 1, 2021 through December 31, 2021 within the Veteran Affairs Health System in unscheduled outpatient settings at the Tennessee Valley Healthcare System. Stakeholders included clinical operations leadership and methodological experts. Audio-recorded interviews were transcribed and de-identified. Data coding and analysis were conducted by experienced qualitative methodologists adhering to the Consolidated Criteria for Reporting Qualitative Studies guidelines. Analysis was conducted using an iterative inductive/deductive process. RESULTS: We conducted semi-structured interviews with 66 participants: clinicians (N = 25), stakeholders (N = 24), and Veterans (N = 17). We identified six themes contributing to potentially inappropriate prescribing of antibiotics and NSAIDs: 1) Perceived versus actual Veterans expectations about prescribing; 2) the influence of a time-pressured clinical environment on prescribing stewardship; 3) Limited clinician knowledge, awareness, and willingness to use evidence-based care; 4) Prescriber uncertainties about the Veteran condition at the time of the clinical encounter; 5) Limited communication; and 6) Technology barriers of the electronic health record and patient portal. CONCLUSIONS: The diverse perspectives on prescribing underscore the need for interventions that recognize the detrimental impact of high workload on prescribing stewardship and the need to design interventions with the end-user in mind. This study revealed actionable themes that could be addressed to improve guideline concordant prescribing to enhance the quality of prescribing and to reduce patient harm.

Randomized Controlled Comparative Effectiveness Trial of Risk Model-Guided Clinical Decision Support for Suicide Screening.

Suicide prevention requires risk identification, appropriate intervention, and follow-up. Traditional risk identification relies on patient self-reporting, support network reporting, or face-to-face screening with validated instruments or history and physical exam. In the last decade, statistical risk models have been studied and more recently deployed to augment clinical judgment. Models have generally been found to be low precision or problematic at scale due to low incidence. Few have been tested in clinical practice, and none have been tested in clinical trials to our knowledge. Methods: We report the results of a pragmatic randomized controlled trial (RCT) in three outpatient adult Neurology clinic settings. This two-arm trial compared the effectiveness of Interruptive and Non-Interruptive Clinical Decision Support (CDS) to prompt further screening of suicidal ideation for those predicted to be high risk using a real-time, validated statistical risk model of suicide attempt risk, with the decision to screen as the primary end point. Secondary outcomes included rates of suicidal ideation and attempts in both arms. Manual chart review of every trial encounter was used to determine if suicide risk assessment was subsequently documented. Results: From August 16, 2022, through February 16, 2023, our study randomized 596 patient encounters across 561 patients for providers to receive either Interruptive or Non-Interruptive CDS in a 1:1 ratio. Adjusting for provider cluster effects, Interruptive CDS led to significantly higher numbers of decisions to screen (42%=121/289 encounters) compared to Non-Interruptive CDS (4%=12/307) (odds ratio=17.7, p-value <0.001). Secondarily, no documented episodes of suicidal ideation or attempts occurred in either arm. While the proportion of documented assessments among those noting the decision to screen was higher for providers in the Non-Interruptive arm (92%=11/12) than in the Interruptive arm (52%=63/121), the interruptive CDS was associated with more frequent documentation of suicide risk assessment (63/289 encounters compared to 11/307, p-value<0.001). Conclusions: In this pragmatic RCT of real-time predictive CDS to guide suicide risk assessment, Interruptive CDS led to higher numbers of decisions to screen and documented suicide risk assessments. Well-powered large-scale trials randomizing this type of CDS compared to standard of care are indicated to measure effectiveness in reducing suicidal self-harm. ClinicalTrials.gov Identifier: NCT05312437.

Adapting Cognitive Task Analysis Methods for Use in a Large Sample Simulation Study of High-Risk Healthcare Events.

Cognitive task analysis (CTA) methods are traditionally used to conduct small-sample, in-depth studies. In this case study, CTA methods were adapted for a large multi-site study in which 102 anesthesiologists worked through four different high-fidelity simulated high-consequence incidents. Cognitive interviews were used to elicit decision processes following each simulated incident. In this paper, we highlight three practical challenges that arose: (1) standardizing the interview techniques for use across a large, distributed team of diverse backgrounds; (2) developing effective training; and (3) developing a strategy to analyze the resulting large amount of qualitative data. We reflect on how we addressed these challenges by increasing standardization, developing focused training, overcoming social norms that hindered interview effectiveness, and conducting a staged analysis. We share findings from a preliminary analysis that provides early validation of the strategy employed. Analysis of a subset of 64 interview transcripts using a decompositional analysis approach suggests that interviewers successfully elicited descriptions of decision processes that varied due to the different challenges presented by the four simulated incidents. A holistic analysis of the same 64 transcripts revealed individual differences in how anesthesiologists interpreted and managed the same case.

Medication Safety Amid Technological Change: Usability Evaluation to Inform Inpatient Nurses' Electronic Health Record System Transition.

BACKGROUND: Electronic health record (EHR) system transitions are challenging for healthcare organizations. High-volume, safety-critical tasks like barcode medication administration (BCMA) should be evaluated, yet standards for ensuring safety during transition have not been established. OBJECTIVE: Identify risks in common and problem-prone medication tasks to inform safe transition between BCMA systems and establish benchmarks for future system changes. DESIGN: Staff nurses completed simulation-based usability testing in the legacy system (R1) and new system pre- (R2) and post-go-live (R3). Tasks included (1) Hold/Administer, (2) IV Fluids, (3) PRN Pain, (4) Insulin, (5) Downtime/PRN, and (6) Messaging. Audiovisual recordings of task performance were systematically analyzed for time, navigation, and errors. The System Usability Scale measured perceived usability and satisfaction. Post-simulation interviews captured nurses' qualitative comments and perceptions of the systems. PARTICIPANTS: Fifteen staff nurses completed 2-3-h simulation sessions. Eleven completed both R1 and R2, and seven completed all three rounds. Clinical experience ranged from novice (< 1 year) to experienced (> 10 years). Practice settings included adult and pediatric patient populations in ICU, stepdown, and acute care departments. MAIN MEASURES: Task completion rates/times, safety and non-safety-related use errors (interaction difficulties), and user satisfaction. KEY RESULTS: Overall success rates remained relatively stable in all tasks except two: IV Fluids task success increased substantially (R1: 17%, R2: 54%, R3: 100%) and Downtime/PRN task success decreased (R1: 92%, R2: 64%, R3: 22%). Among the seven nurses who completed all rounds, overall safety-related errors decreased 53% from R1 to R3 and 50% from R2 to R3, and average task times for successfully completed tasks decreased 22% from R1 to R3 and 38% from R2 to R3. CONCLUSIONS: Usability testing is a reasonable approach to compare different BCMA tasks to anticipate transition problems and establish benchmarks with which to monitor and evaluate system changes going forward.

Inpatient nurses' preferences and decisions with risk information visualization.

OBJECTIVE: We examined the influence of 4 different risk information formats on inpatient nurses' preferences and decisions with an acute clinical deterioration decision-support system. MATERIALS AND METHODS: We conducted a comparative usability evaluation in which participants provided responses to multiple user interface options in a simulated setting. We collected qualitative data using think aloud methods. We collected quantitative data by asking participants which action they would perform after each time point in 3 different patient scenarios. RESULTS: More participants (n = 6) preferred the probability format over relative risk ratios (n = 2), absolute differences (n = 2), and number of persons out of 100 (n = 0). Participants liked average lines, having a trend graph to supplement the risk estimate, and consistent colors between trend graphs and possible actions. Participants did not like too much text information or the presence of confidence intervals. From a decision-making perspective, use of the probability format was associated with greater concordance in actions taken by participants compared to the other 3 risk information formats. DISCUSSION: By focusing on nurses' preferences and decisions with several risk information display formats and collecting both qualitative and quantitative data, we have provided meaningful insights for the design of clinical decision-support systems containing complex quantitative information. CONCLUSION: This study adds to our knowledge of presenting risk information to nurses within clinical decision-support systems. We encourage those developing risk-based systems for inpatient nurses to consider expressing risk in a probability format and include a graph (with average line) to display the patient's recent trends.

Decision-Making During High-Risk Events: A Systematic Literature Review.

Effective decision-making in crisis events is challenging due to time pressure, uncertainty, and dynamic decisional environments. We conducted a systematic literature review in PubMed and PsycINFO, identifying 32 empiric research papers that examine how trained professionals make naturalistic decisions under pressure. We used structured qualitative analysis methods to extract key themes. The studies explored different aspects of decision-making across multiple domains. The majority (19) focused on healthcare; military, fire and rescue, oil installation, and aviation domains were also represented. We found appreciable variability in research focus, methodology, and decision-making descriptions. We identified five main themes: (1) decision-making strategy, (2) time pressure, (3) stress, (4) uncertainty, and (5) errors. Recognition-primed decision-making (RPD) strategies were reported in all studies that analyzed this aspect. Analytical strategies were also prominent, appearing more frequently in contexts with less time pressure and explicit training to generate multiple explanations. Practitioner experience, time pressure, stress, and uncertainty were major influencing factors. Professionals must adapt to the time available, types of uncertainty, and individual skills when making decisions in high-risk situations. Improved understanding of these decisional factors can inform evidence-based enhancements to training, technology, and process design.

Using intervention mapping to design and implement a multicomponent intervention to improve antibiotic and NSAID prescribing.

Successfully changing prescribing behavior to reduce inappropriate antibiotic and nonsteroidal anti-inflammatory drug (NSAID) prescriptions often requires combining components into a multicomponent intervention. However, multicomponent interventions often fail because of development and implementation complexity. To increase the likelihood of successfully changing prescribing behavior, we applied a systematic process to design and implement a multicomponent intervention. We used Intervention Mapping to create a roadmap for a multicomponent intervention in unscheduled outpatient care settings in the Veterans Health Administration. Intervention Mapping is a systematic process consisting of six steps that we grouped into three phases: (i) understand behavioral determinants and barriers to implementation, (ii) develop the intervention, and (iii) define evaluation plan and implementation strategies. A targeted literature review, combined with 25 prescriber and 25 stakeholder interviews, helped identify key behavioral determinants to inappropriate prescribing (e.g. perceived social pressure from patients to prescribe). We targeted three desired prescriber behaviors: (i) review guideline-concordant prescribing and patient outcomes, (ii) manage diagnostic and treatment uncertainty, and (iii) educate patients and caregivers. The intervention consisted of components for academic detailing, prescribing feedback, and alternative prescription order sets. Implementation strategies consisted of preparing clinical champions, conducting readiness assessments, and incentivizing use of the intervention. We chose a mixed-method study design with a commonly used evaluation framework to assess effectiveness and implementation outcomes in a subsequent trial. This study furthers knowledge about causes of inappropriate antibiotic and NSAID prescribing and demonstrates how theoretical, empirical, and practical information can be systematically applied to develop a multicomponent intervention to help address these causes.

Reducing adverse drug events from antibiotics and nonsteroidal anti-inflammatory drugs (NSAIDs) is a patient safety priority. Successfully changing prescribing behavior to reduce inappropriate prescriptions can require combining intervention components, each with different mechanisms for behavior change, into a multicomponent intervention. However, multicomponent interventions often fail because of development and implementation complexity. To increase the chance of successfully changing antibiotic and NSAID prescribing, the objective this study was to apply a systematic process to design and implement a multicomponent intervention. Three desired prescriber behaviors were targeted: (i) review guideline-concordant prescribing and patient outcomes, (ii) manage diagnostic and treatment uncertainty, and (iii) educate patients and caregivers. The designed intervention consisted of components for prescribing feedback, academic detailing, and alternative prescription order sets. Strategies to improve use of the intervention consisted of preparing clinical champions, conducting readiness assessments prior to study onset, and incentivizing use of the intervention. We chose a mixed-method study design with a commonly used evaluation framework to assess effectiveness and implementation outcomes of the multicomponent intervention in a subsequent trial.

Evaluation of inpatient medication guidance from an artificial intelligence chatbot.

PURPOSE: To analyze the clinical completeness, correctness, usefulness, and safety of chatbot and medication database responses to everyday inpatient medication-use questions. METHODS: We evaluated the responses from an artificial intelligence chatbot, a medication database, and clinical pharmacists to 200 real-world medication-use questions. Answer quality was rated by a blinded group of pharmacists, providers, and nurses. Chatbot and medication database responses were deemed "acceptable" if the mean reviewer rating was within 3 points of the mean rating for pharmacists' answers. We used descriptive statistics for reviewer ratings and Kendall's coefficient to evaluate interrater agreement. RESULTS: The medication database generated responses to 194 (97%) questions, with 88% considered acceptable for clinical correctness, 76% considered acceptable for completeness, 83% considered acceptable for safety, and 81% considered acceptable for usefulness compared to pharmacists' answers. The chatbot responded to only 160 (80%) questions, with 85% considered acceptable for clinical correctness, 65% considered acceptable for completeness, 71% considered acceptable for safety, and 68% considered acceptable for usefulness. CONCLUSION: Traditional search methods using a drug database provide more clinically correct, complete, safe, and useful answers than a chatbot. When the chatbot generated a response, the clinical correctness was similar to that of a drug database; however, it was not rated as favorably for clinical completeness, safety, or usefulness. Our results highlight the need for ongoing training and continued improvements to artificial intelligence chatbots for them to be incorporated reliably into the clinical workflow. With continued improvement in chatbot functionality, chatbots could be a useful pharmacist adjunct, providing healthcare providers with quick and reliable answers to medication-use questions.

Empowering patients to address diabetes care gaps: formative usability testing of a novel patient portal intervention.

Objective: The aim of this study was to design and assess the formative usability of a novel patient portal intervention designed to empower patients with diabetes to initiate orders for diabetes-related monitoring and preventive services. Materials and Methods: We used a user-centered Design Sprint methodology to create our intervention prototype and assess its usability with 3 rounds of iterative testing. Participants (5/round) were presented with the prototype and asked to perform common, standardized tasks using think-aloud procedures. A facilitator rated task performance using a scale: (1) completed with ease, (2) completed with difficulty, and (3) failed. Participants completed the System Usability Scale (SUS) scored 0-worst to 100-best. All testing occurred remotely via Zoom. Results: We identified 3 main categories of usability issues: distrust about the automated system, content concerns, and layout difficulties. Changes included improving clarity about the ordering process and simplifying language; however, design constraints inherent to the electronic health record system limited our ability to respond to all usability issues (eg, could not modify fixed elements in layout). Percent of tasks completed with ease across each round were 67%, 60%, and 80%, respectively. Average SUS scores were 87, 74, and 93, respectively. Across rounds, participants found the intervention valuable and appreciated the concept of patient-initiated ordering. Conclusions: Through iterative user-centered design and testing, we improved the usability of the patient portal intervention. A tool that empowers patients to initiate orders for disease-specific services as part of their existing patient portal account has potential to enhance the completion of recommended health services and improve clinical outcomes.

Human-Centered Design to Address Biases in Artificial Intelligence.

The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies potential biases in each stage of the AI life cycle, including data collection, annotation, machine learning model development, evaluation, deployment, operationalization, monitoring, and feedback integration. To mitigate these biases, we suggest involving a diverse group of stakeholders, using human-centered AI principles. Human-centered AI can help ensure that AI systems are designed and used in a way that benefits patients and society, which can reduce health disparities and inequities. By recognizing and addressing biases at each stage of the AI life cycle, AI can achieve its potential in health care.

Understanding Patient and Clinician Reported Nonroutine Events in Ambulatory Surgery.

OBJECTIVE: Nonroutine events (NREs, i.e., deviations from optimal care) can identify care process deficiencies and safety risks. Nonroutine events reported by clinicians have been shown to identify systems failures, but this methodology fails to capture the patient perspective. The objective of this prospective observational study is to understand the incidence and nature of patient- and clinician-reported NREs in ambulatory surgery. METHODS: We interviewed patients about NREs that occurred during their perioperative care using a structured interview tool before discharge and in a 7-day follow-up call. Concurrently, we interviewed the clinicians caring for these patients immediately postoperatively to collect NREs. We trained 2 experienced clinicians and 2 patients to assess and code each reported NRE for type, theme, severity, and likelihood of reoccurrence (i.e., likelihood that the same event would occur for another patient). RESULTS: One hundred one of 145 ambulatory surgery cases (70%) contained at least one NRE. Overall, 214 NREs were reported-88 by patients and 126 by clinicians. Cases containing clinician-reported NREs were associated with increased patient body mass index ( P = 0.023) and lower postcase patient ratings of being treated with respect ( P = 0.032). Cases containing patient-reported NREs were associated with longer case duration ( P = 0.040), higher postcase clinician frustration ratings ( P < 0.001), higher ratings of patient stress ( P = 0.019), and lower patient ratings of their quality of life ( P = 0.010), of the quality of clinician teamwork ( P = 0.010), being treated with respect ( P = 0.003), and being listened to carefully ( P = 0.012). Trained patient raters evaluated NRE severity significantly higher than did clinician raters ( P < 0.001), while clinicians rated recurrence likelihood significantly higher than patients for both clinician ( P = 0.032) and patient-reported NREs ( P = 0.001). CONCLUSIONS: Both patients and clinicians readily report events during clinical care that they believe deviate from optimal care expectations. These 2 primary stakeholders in safe, high-quality surgical care have different experiences and perspectives regarding NREs. The combination of patient- and clinician-reported NREs seems to be a promising patient-centered method of identifying healthcare system deficiencies and opportunities for improvement.

Developing Ethics and Equity Principles, Terms, and Engagement Tools to Advance Health Equity and Researcher Diversity in AI and Machine Learning: Modified Delphi Approach.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) technology design and development continues to be rapid, despite major limitations in its current form as a practice and discipline to address all sociohumanitarian issues and complexities. From these limitations emerges an imperative to strengthen AI and ML literacy in underserved communities and build a more diverse AI and ML design and development workforce engaged in health research. OBJECTIVE: AI and ML has the potential to account for and assess a variety of factors that contribute to health and disease and to improve prevention, diagnosis, and therapy. Here, we describe recent activities within the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Ethics and Equity Workgroup (EEWG) that led to the development of deliverables that will help put ethics and fairness at the forefront of AI and ML applications to build equity in biomedical research, education, and health care. METHODS: The AIM-AHEAD EEWG was created in 2021 with 3 cochairs and 51 members in year 1 and 2 cochairs and ~40 members in year 2. Members in both years included AIM-AHEAD principal investigators, coinvestigators, leadership fellows, and research fellows. The EEWG used a modified Delphi approach using polling, ranking, and other exercises to facilitate discussions around tangible steps, key terms, and definitions needed to ensure that ethics and fairness are at the forefront of AI and ML applications to build equity in biomedical research, education, and health care. RESULTS: The EEWG developed a set of ethics and equity principles, a glossary, and an interview guide. The ethics and equity principles comprise 5 core principles, each with subparts, which articulate best practices for working with stakeholders from historically and presently underrepresented communities. The glossary contains 12 terms and definitions, with particular emphasis on optimal development, refinement, and implementation of AI and ML in health equity research. To accompany the glossary, the EEWG developed a concept relationship diagram that describes the logical flow of and relationship between the definitional concepts. Lastly, the interview guide provides questions that can be used or adapted to garner stakeholder and community perspectives on the principles and glossary. CONCLUSIONS: Ongoing engagement is needed around our principles and glossary to identify and predict potential limitations in their uses in AI and ML research settings, especially for institutions with limited resources. This requires time, careful consideration, and honest discussions around what classifies an engagement incentive as meaningful to support and sustain their full engagement. By slowing down to meet historically and presently underresourced institutions and communities where they are and where they are capable of engaging and competing, there is higher potential to achieve needed diversity, ethics, and equity in AI and ML implementation in health research.

A Time and Motion Analysis of Nursing Workload and Electronic Health Record Use in the Emergency Department.

INTRODUCTION: The use of an electronic health record may create unanticipated consequences for emergency care delivery. We sought to describe emergency department nursing task distribution and the use of the electronic health record. METHODS: This was a prospective observational study of nurses in the emergency department using a time-and-motion methodology. Three trained research assistants conducted 1:1 observations between March and September 2019. Nurse tasks were classified into 6 established categories: electronic health record, direct/indirect patient care, communication, personal time, and other. Nurses' perceived workload was assessed using the National Aeronautics and Space Administration (NASA) Task Load Index. RESULTS: Twenty-three observations were conducted over 46 hours. Overall, nurses spent 27% of their time on electronic health record tasks, 25% on direct patient care, 17% on personal time, 15% on indirect patient care, and 6% on communication. During morning (7 am-12 pm) and afternoon shifts (12 pm-3 pm), the use of the health record was the most commonly performed task, whereas indirect patient care was the task most performed during evening shifts (3 pm-12 pm). Using the National Aeronautics and Space Administration (NASA) Task Load Index, nurses reported an increase in mental demand and effort during afternoon shifts compared with morning shifts. DISCUSSION: We observed that emergency nurses spent more time using the electronic health record as compared to other tasks. Increased usability of the electronic health record, particularly during high occupancy periods, may be a target for improvement.

User-Centered Design in Pediatric Acute Care Settings Antimicrobial Stewardship.

BACKGROUND: Antibiotic prescribing in ambulatory care centers is increasing. Previous research suggests that 20 to 50% of antibiotic prescriptions are either unnecessary or inappropriate. Unnecessary antibiotic consumption can harm patients by increasing antibiotic resistance and drug-associated toxicities, and the reasons for such use are multifactorial. Antimicrobial Stewardship Programs (ASP) were developed to guide better use of antibiotics. A core element of ASP is to provide feedback to clinical providers. To create clinically meaningful feedback, user-center design (UCD) is a robust approach to include end-users in the design process to improve systems. OBJECTIVE: The study aimed to take a UCD approach to developing antibiotic prescribing feedback through input from clinicians in two ambulatory care settings. METHODS: We conducted two group prototyping sessions with pediatric clinicians who practice in the emergency department and urgent care settings at a tertiary care children's hospital. Participants received background on the problem of antibiotic prescribing and then were interviewed about their information needs, perceived value, and desired incentives for a prescribing feedback system. Sessions concluded with their response and recommendations to sample sections of an antibiotic feedback report including orienting material, report detail, targeted education, and resources. RESULTS: A UCD approach was found to be highly valuable in the development of a feedback mechanism that is viewed as desirable by clinicians. Clinicians preferred interpreting the data themselves with aids such as diagrams and charts over the researcher concluded statements about the clinician's behavior. Specific feedback that clinicians considered redundant were removed from the model if preexisting alerts were established. CONCLUSION: Integrating a UCD approach in developing ASP feedback identified desirable report characteristics that substantially modified preliminary wireframes for feedback. Future research will evaluate the clinical effectiveness of our feedback reports in outpatient settings.

Primary Care Clinicians' Beliefs and Strategies for Managing Chronic Pain in an Era of a National Opioid Epidemic.

BACKGROUND: Little is known about how primary care clinicians (PCCs) approach chronic pain management in the current climate of rapidly changing guidelines and the growing body of research about risks and benefits of opioid therapy. OBJECTIVE: To better understand PCCs' approaches to managing patients with chronic pain and explore implications for technological and administrative interventions. DESIGN: We conducted adapted critical decision method interviews with 20 PCCs. Each PCC participated in 1-5 interviews. PARTICIPANTS: PCCs interviewed had a mean of 14 years of experience. They were sampled from 13 different clinics in rural, suburban, and urban health settings across the state of Indiana. APPROACH: Interviews included discussion of participants' general approach to managing chronic pain, as well as in-depth discussion of specific patients with chronic pain. Interviews were audio recorded. Transcripts were analyzed thematically. KEY RESULTS: PCCs reflected on strategies they use to encourage and motivate patients. We identified four associated strategic themes: (1) developing trust, (2) eliciting information from the patient, (3) diverting attention from pain to function, and (4) articulating realistic goals for the patient. In discussion of chronic pain management, PCCs often explained their beliefs about opioid therapy. Three themes emerged: (1) Opioid use tends to reduce function, (2) Opioids are often not effective for long-term pain treatment, and (3) Response to pain and opioids is highly variable. CONCLUSIONS: PCC beliefs about opioid therapy generally align with the clinical evidence, but may have some important gaps. These findings suggest the potential value of interventions that include improved access to research findings; organizational changes to support PCCs in spending time with patients to develop rapport and trust, elicit information about pain, and manage patient expectations; and the need for innovative clinical cognitive support.

Everyday objects and spaces: How they afford resilience in diabetes routines.

Thirty million Americans currently have diabetes, and a substantial portion do not reach the goals of clinical treatment. This is in part due to the complex barriers to effective self-care faced by people with diabetes. This study uses a patient work perspective, focusing on the everyday, lived experience of managing diabetes. Our primary research goal was to explore how the work of self-care is embedded in the other routines of everyday living. We found that everyday objects and spaces were instrumental in the incorporation of diabetes work into daily routines. Objects anchored diabetes tasks by linking illness-specific artifacts to space and time (e.g. a morning routine), and by enabling the performance on diabetes tasks while on the move in either planned or unplanned ways.

Factors That Influence Changes to Existing Chronic Pain Management Plans.

BACKGROUND: The objective of this qualitative study is to better understand primary care clinician decision making for managing chronic pain. Specifically, we focus on the factors that influence changes to existing chronic pain management plans. Limitations in guidelines and training leave clinicians to use their own judgment and experience in managing the complexities associated with treating patients with chronic pain. This study provides insight into those judgments based on clinicians' first-person accounts. Insights gleaned from this study could inspire innovations aimed at supporting primary care clinicians (PCCs) in managing chronic pain. METHODS: We conducted 89 interviews with PCCs to obtain their first-person perspective of the factors that influenced changes in treatment plans for their patients. Interview transcripts were analyzed thematically by a multidisciplinary team of clinicians, cognitive scientists, and public health researchers. RESULTS: Seven themes emerged through our analysis of factors that influenced a change in chronic pain management: 1) change in patient condition; 2) outcomes related to treatment; 3) nonadherent patient behavior; 4) insurance constraints; 5) change in guidelines, laws, or policies; 6) approaches to new patients; and 7) specialist recommendations. CONCLUSIONS: Our analysis sheds light on the factors that lead PCCs to change treatment plans for patients with chronic pain. An understanding of these factors can inform the types of innovations needed to support PCCs in providing chronic pain care. We highlight key insights from our analysis and offer ideas for potential practice innovations.

Health Management in the Home: A Qualitative Study of Pregnant Women and Their Caregivers.

With growth in consumer health technologies, patients and caregivers have become increasingly involved in their health and medical care. Such health-related engagement often occurs at home. Pregnancy is a common condition and, for many women, their first exposure to health management practices. This study examined how pregnant women and caregivers managed health in their homes. Participants completed sociodemographic surveys and semi-structured interviews about living situation, information needs, and technology use. Using an iterative, inductive coding approach, we identified themes about health management, including the physical home, help at home, community, the virtual home, and biggest concerns. Most expectant mothers encountered everyday problems with mobility and household management. Pregnant women desired more assistance from caregivers, who often did not know how to help. Caregivers who provided help took on new roles. Many expectant families did not trust advice found online. Over half of expectant families had biggest concerns that involved the home.

Improving the Effectiveness of Health Information Technology: The Case for Situational Analytics.

Health information technology has contributed to improvements in quality and safety in clinical settings. However, the implementation of new technologies in health care has also been associated with the introduction of new sociotechnical hazards, produced through a range of complex interactions that vary with social, physical, temporal, and technological context. Other industries have been confronted with this problem and have developed advanced analytics to examine context-specific activities of workers and related outcomes. The skills and data exist in health care to develop similar insights through situational analytics, defined as the application of analytic methods to characterize human activity in situations and identify patterns in activity and outcomes that are influenced by contextual factors. This article describes the approach of situational analytics and potentially useful data sources, including trace data from electronic health record activity, reports from users, qualitative field data, and locational data. Key implementation requirements are discussed, including the need for collaboration among qualitative researchers and data scientists, organizational and federal level infrastructure requirements, and the need to implement a parallel research program in ethics to understand how the data are being used by organizations and policy makers.