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Abstract Background Normalization Process Theory (NPT) is an implementation theory that can be used to explain how and why implementation strategies work or not in particular circumstances. We used it to understand the mechanisms that lead to the adoption and routinization of palliative care within hemodialysis centers. Methods We employed a longitudinal, mixed methods approach to comprehensively evaluate the implementation of palliative care practices among ten hemodialysis centers participating in an Institute for Healthcare Improvement Breakthrough- Series learning collaborative. Qualitative methods included longitudinal observations of collaborative activities, and interviews with implementers at the end of the study. We used an inductive and deductive approach to thematic analysis informed by NPT constructs (coherence, cognitive participation, collective action, reflexive monitoring) and implementation outcomes. The NoMAD survey, which measures NPT constructs, was completed by implementers at each hemodialysis center during early and late implementation. Results The four mechanisms posited in NPT had a dynamic and layered relationship during the implementation process. Collaborative participants participated because they believed in the value and legitimacy of palliative care for patients receiving hemodialysis and thus had high levels of cognitive participation at the start. Didactic Learning Sessions were important for building practice coherence, and sense-making was solidified through testing new skills in practice and first-hand observation during coaching visits by an expert. Collective action was hampered by limited time among team members and practical issues such as arranging meetings with patients. Reflexive monitoring of the positive benefit to patient and family experiences was key in shifting mindsets from disease-centric towards a patient-centered model of care. NoMAD survey scores showed modest improvement over time, with collective action having the lowest scores. Conclusions NPT was a useful framework for understanding the implementation of palliative care practices within hemodialysis centers. We found a nonlinear relationship among the mechanisms which is reflected in our model of implementation of palliative care practices through a learning collaborative. These findings suggest that the implementation of complex practices such as palliative care may be more successful through iterative learning and practice opportunities as the mechanisms for change are layered and mutually reinforcing. Trial registration ClinicalTrials.gov, NCT04125537 . Registered 14 October 2019 – Retrospectively registered.

Background Cardiovascular procedural treatments were deferred at scale during the COVID‐19 pandemic, with unclear impact on patients presenting with non–ST‐segment–elevation myocardial infarction (NSTEMI). Methods and Results In a retrospective cohort study of all patients diagnosed with NSTEMI in the US Veterans Affairs Healthcare System from January 1, 2019 to October 30, 2022 (n=67 125), procedural treatments and outcomes were compared between the prepandemic period and 6 unique pandemic phases: (1) acute phase, (2) community spread, (3) first peak, (4) post vaccine, (5) second peak, and (6) recovery. Multivariable regression analysis was performed to assess the association between pandemic phases and 30‐day mortality. NSTEMI volumes dropped significantly with the pandemic onset (62.7% of prepandemic peak) and did not revert to prepandemic levels in subsequent phases, even after vaccine availability. Percutaneous coronary intervention and coronary artery bypass grafting volumes declined proportionally. Compared with the prepandemic period, patients with NSTEMI experienced higher 30‐day mortality during Phases 2 and 3, even after adjustment for COVID‐19‐positive status, demographics, baseline comorbidities, and receipt of procedural treatment (adjusted odds ratio for Phases 2 and 3 combined, 1.26 [95% CI, 1.13–1.43], P

Quality measurement is typically the domain of clinical experts and health system leaders; patient/caregiver perspectives are rarely solicited. We aimed to describe and integrate clinician and patient/caregiver conceptualizations of high-quality palliative symptom care for patients receiving care for advanced cancer within the US Veterans Health Administration in the context of existing quality measures. We conducted a secondary qualitative analysis of transcripts from prioritization discussions of process quality measures relevant to cancer palliative care. These discussions occurred during 2 modified RAND-UCLA appropriateness panels: a panel of 10 palliative care clinical expert stakeholders (7 physicians, 2 nurses, 1 social worker) and a panel of 9 patients/caregivers with cancer experience. Discussions were recorded, transcribed, and independently double-coded using an a priori logical framework. Content analysis was used to identify subthemes within codes and axial coding was used to identify crosscutting themes. Patients/caregivers and clinical experts contributed important perspectives to 3 crosscutting themes. First, proactive elicitation of symptoms is critical. Patients/caregivers especially emphasized importance of comprehensive and proactive screening and assessment, especially for pain and mental health. Second, screening and assessment alone is not enough; information elicited from patients must inform care. Measuring screening/assessment and management care processes separately has important limitations. Lastly, high-quality symptom management can be broadly defined if it is patient-centered; high-quality care takes an individualized approach and might include non-medical or non-pharmacological symptom management. Integrating the perspectives of clinical experts and patients/caregivers is critical for health systems to consider as they design and implement quality measures for palliative cancer care.

Background Opioids are commonly prescribed for postoperative pain, but may lead to prolonged use and addiction. Diabetes impairs nerve function, complicates pain management, and makes opioid prescribing particularly challenging. Methods This retrospective observational study included a cohort of postoperative patients from a multisite academic health system to assess the relationship between diabetes, pain, and prolonged opioid use (POU), 2008–2019. POU was defined as a new opioid prescription 3–6 months after discharge. The odds that a patient had POU was assessed using multivariate logistic regression controlling for patient factors (e.g., demographic and clinical factors, as well as prior pain and opiate use). Findings A total of 43,654 patients were included, 12.4% with diabetes. Patients with diabetes had higher preoperative pain scores (2.1 vs 1.9, pInterpretation In conclusion, surgical patients with diabetes are at increased risk for POU even after controlling for likely covariates, yet they receive similar postoperative opiate therapy. The results suggest a more tailored approach to diabetic postoperative pain management is warranted.

ObjectiveThe opioid crisis brought scrutiny to opioid prescribing. Understanding how opioid prescribing patterns and corresponding patient outcomes changed during the epidemic is essential for future targeted policies. Many studies attempt to model trends in opioid prescriptions therefore understanding the temporal shift in opioid prescribing patterns across populations is necessary. This study characterized postoperative opioid prescribing patterns across different populations, 2010–2020.Data SourceAdministrative data from Veteran Health Administration (VHA), six Medicaid state programs and an Academic Medical Center (AMC).Data extractionSurgeries were identified using the Clinical Classifications Software.Study DesignTrends in average daily discharge Morphine Milligram Equivalent (MME), postoperative pain and subsequent opioid prescription were compared using regression and likelihood ratio test statistics.Principal FindingsThe cohorts included 595,106 patients, with populations that varied considerably in demographics. Over the study period, MME decreased significantly at VHA (37.5–30.1; p = 0.002) and Medicaid (41.6–31.3; p = 0.019), and increased at AMC (36.9–41.7; p

Abstract Introduction Many healthcare delivery systems have developed clinician‐led quality improvement (QI) initiatives but fewer have also developed in‐house evaluation units. Engagement between the two entities creates unique opportunities. Stanford Medicine funded a collaboration between their Improvement Capability Development Program (ICDP), which coordinates and incentivizes clinician‐led QI efforts, and the Evaluation Sciences Unit (ESU), a multidisciplinary group of embedded researchers with expertise in implementation and evaluation sciences. Aim To describe the ICDP‐ESU partnership and report key learnings from the first 2 y of operation September 2019 to August 2021. Methods Department‐level physician and operational QI leaders were offered an ESU consultation to workshop design, methods, and overall scope of their annual QI projects. A steering committee of high‐level stakeholders from operational, clinical, and research perspectives subsequently selected three projects for in‐depth partnered evaluation with the ESU based on evaluability, importance to the health system, and broader relevance. Selected project teams met regularly with the ESU to develop mixed methods evaluations informed by relevant implementation science frameworks, while aligning the evaluation approach with the clinical teams' QI goals. Results Sixty and 62 ICDP projects were initiated during the 2 cycles, respectively, across 18 departments, of which ESU consulted with 15 (83%). Within each annual cycle, evaluators made actionable, summative findings rapidly available to partners to inform ongoing improvement. Other reported benefits of the partnership included rapid adaptation to COVID‐19 needs, expanded clinician evaluation skills, external knowledge dissemination through scholarship, and health system‐wide knowledge exchange. Ongoing considerations for improving the collaboration included the need for multi‐year support to enable nimble response to dynamic health system needs and timely data access. Conclusion Presence of embedded evaluation partners in the enterprise‐wide QI program supported identification of analogous endeavors (eg, telemedicine adoption) and cross‐cutting lessons across QI efforts, clinician capacity building, and knowledge dissemination through scholarship.

Abstract Background Like in many settings, implementation of evidence-based practices often fall short in pediatric intensive care units (PICU). Very few prior studies have applied implementation science frameworks to understand how best to improve practices in this unique environment. We used the relatively new integrated Promoting Action on Research Implementation in Health Services (iPARIHS) framework to assess practice improvement in the PICU and to explore the utility of the framework itself for that purpose. Methods We used the iPARIHS framework to guide development of a semi-structured interview tool to examine barriers, facilitators, and the process of change in the PICU. A framework approach to qualitative analysis, developed around iPARIHS constructs and subconstructs, helped identify patterns and themes in provider interviews. We assessed the utility of iPARIHS to inform PICU practice change. Results Fifty multi-professional providers working in 8 U.S. PICUs completed interviews. iPARIHS constructs shaped the development of a process model for change that consisted of phases that include planning, a decision to adopt change, implementation and facilitation, and sustainability; the PICU environment shaped each phase. Large, complex multi-professional teams, and high-stakes work at near-capacity impaired receptivity to change. While the unit leaders made decisions to pursue change, providers’ willingness to accept change was based on the evidence for the change, and provider’s experiences, beliefs, and capacity to integrate change into a demanding workflow. Limited analytic structures and resources frustrated attempts to monitor changes’ impacts. Variable provider engagement, time allocated to work on changes, and limited collaboration impacted facilitation. iPARIHS constructs were useful in exploring implementation; however, we identified inter-relation of subconstructs, unique concepts not captured by the framework, and a need for subconstructs to further describe facilitation. Conclusions The PICU environment significantly shaped the implementation. The described process model for implementation may be useful to guide efforts to integrate changes and select implementation strategies. iPARIHS was adequate to identify barriers and facilitators of change; however, further elaboration of subconstructs for facilitation would be helpful to operationalize the framework. Trial registration Not applicable, as no health care intervention was performed.

Opiates used for acute pain are an established risk factor for chronic opioid use (COU). Patient characteristics contribute to progression from acute opioid use to COU, but most are not clinically modifiable. To develop and validate machine-learning algorithms that use claims data to predict progression from acute to COU in the Medicaid population, Adult opioid naïve Medicaid patients from 6 anonymized states who received an opioid prescription between 2015 and 2019 were included. Five machine learning (ML) Models were developed, and model performance assessed by area under the receiver operating characteristic curve (auROC), precision and recall. In the study, 29.9% (53820/180000) of patients transitioned from acute opioid use to COU. Initial opioid prescriptions in COU patients had increased morphine milligram equivalents (MME) (33.2 vs. 23.2), tablets per prescription (45.6 vs. 36.54), longer prescriptions (26.63 vs 24.69 days), and higher proportions of tramadol (16.06% vs. 13.44%) and long acting oxycodone (0.24% vs 0.04%) compared to non- COU patients. The top performing model was XGBoost that achieved average precision of 0.87 and auROC of 0.63 in testing and 0.55 and 0.69 in validation, respectively. Top-ranking prescription-related features in the model included quantity of tablets per prescription, prescription length, and emergency department claims. In this study, the Medicaid population, opioid prescriptions with increased tablet quantity and days supply predict increased risk of progression from acute to COU in opioid-naïve patients. Future research should evaluate the effects of modifying these risk factors on COU incidence. Author summary Prescription opioids in the United States contribute to opioid-related overdose deaths. Evidence suggests the Medicaid population has a greater likelihood of opioid-related mortality. However, the current standard for postoperative pain management often doesn’t account for previous opioid use and/or misuse. Machine learning can help healthcare systems identify patients at risk of progression from opioid naïve to chronic opioid use (COU) in Medicaid patients. In this cohort study of 180,000 opioid naïve Medicaid patients, machine learning models accurately identified opioid naïve patients at risk of progression to COU. Features of the initial opioid prescription were strong predictors of progression to COU, including number of tablets, prescription length, and prescription of oxycodone. We found that prescription quantity and length are discrete features related to opioid prescribing that are predictive of COU incidence. This research suggests that stronger prescription guidelines are needed for opioid prescriptions among opioid naïve patients.

Purpose: Risks of red blood cell transfusion may outweigh benefits for many patients in Pediatric Intensive Care Units (PICUs). The Transfusion and Anemia eXpertise Initiative (TAXI) recommendations seek to limit unnecessary and potentially harmful transfusions, but use has been variable. We sought to identify barriers and facilitators to using the TAXI recommendations to inform implementation efforts.Materials and Methods: The integrated Promoting Action on Research Implementation in Health Services (iPARIHS) framework guided semi-structured interviews conducted in 8 U.S. ICUs; 50 providers in multiple ICU roles completed interviews. Adapted Framework analysis, a form of content analysis, used the iPARIHS innovation, recipient, context and facilitation constructs and subconstructs to categorize data and identify patterns as well as unique informative statements.Results: Providers perceived that the TAXI recommendations would reduce transfusion rates and practice variability, but adoption faced challenges posed by attitudes around transfusion and care in busy and complex units. Development of widespread buy-in and inclusion in implementation, integration into workflow, designating committed champions, and monitoring outcomes data were expected to enhance implementation.Conclusions: Targeted activities to create buy-in, educate, and plan for use are necessary for TAXI implementation. Recognition of contextual challenges posed by the PICU environment and an approach that adjusts for barriers may optimize adoption.

Although COVID-19 vaccines have been available to many U.S. Veterans Affairs (VA) healthcare system employees and Veteran patients since early 2021, vaccine receipt data indicates some groups are not receiving them. Our objective was to conduct a rapid qualitative assessment of Veterans’ and VA employees’ views on COVID-19 vaccination to inform clinical leaders’ ongoing efforts to increase vaccine uptake across the VA. We employed semi-structured interviews and a focus group involving employees and Veterans as part of a quality improvement project between January and June 2021 at three VA medical centers. Thirty-one employees and 27 Veterans participated in semi-structured interviews; 5 Veterans from a national stakeholder organization participated in a focus group. Data were analyzed using directed content analysis, involving an a priori coding framework comprised of four domains with subcodes under each: contextual influences, barriers and facilitators, vaccine-specific issues, and VA/military experiences. We then classified initial codes into five categories of hesitancy: vaccine deliberation, dissent, distrust, indifference and skepticism. A subset of Veterans (n = 14) and employees (n = 8) identified as vaccine hesitant. Vaccine hesitancy categories were represented by subcodes of religion, culture, gender or socio-economic factors, perceptions of politics and policies, role of healthcare providers, and historical influences; (contextual influences); knowledge or awareness of vaccines, perceived susceptibility to COVID-19, and beliefs and attitudes about health and illness (barriers and facilitators); vaccine development process (vaccine-specific issues) and military experiences (VA/military factors). Facilitators involved talking with trusted others, ease of vaccine access, and perceptions of family and societal benefits of vaccines. Vaccine hesitancy is multi-faceted and likely requires multiple strategies for engaging in conversations to address Veteran and VA employee concerns. Messages should involve patient-centered communication strategies delivered by trusted healthcare providers and peers and should focus on addressing expected benefits for family, friends, and society.

Purpose. We investigated how health care systems should communicate with patients about possible exposures to blood-borne pathogens that may have occurred during their care. Our goal was to determine how best to communicate uncertain risk information in a way that would minimize harm to patients, maintain their trust, and encourage patients to seek follow-up treatment. Methods. Participants ( N = 1103) were randomized to receive one of six vignette surveys; 997 (98.4%) responded. All vignettes described the same event, but differed by risk level and recommendations (lower risk v. higher risk) and by communication mode (telephone, letter, social media). We measured participants’ perceived risk of blood-borne infection, trust in the health care system, and shared decision making about next clinical steps. Open-ended questions were analyzed using grounded thematic analysis. Results. When the vignette requested patients to undergo testing and practice certain health behaviors (higher risk), participants’ likelihood of seeking follow-up testing for blood-borne pathogens and their understanding of health issues increased. Perceived trust was unaffected by risk level or communication processes. Qualitative data indicated a desire for telephone communication from providers known to the patient. Limitations. It is not clear whether higher risk language or objective risk levels in vignettes motivated patients’ behavioral intentions. Conclusion. Using higher risk language when disclosing large-scale adverse events increased participants’ willingness to seek follow-up care. Implications. Health care organizations’ disclosures should focus on the next steps to take after health care exposures. This communication should involve helping patients to understand their personal health issues better, make them feel that they know which steps to take following the receipt of this information, and encouraging them to seek follow-up infectious disease testing in order to better take care of themselves.

Artificial Intelligence (AI) has generated a large amount of excitement in healthcare, mostly driven by the emergence of increasingly accurate machine learning models. However, the promise of AI delivering scalable and sustained value for patient care in the real world setting has yet to be realized. In order to safely and effectively bring AI into use in healthcare, there needs to be a concerted effort around not just the creation, but also the delivery of AI. This AI “delivery science” will require a broader set of tools, such as design thinking, process improvement, and implementation science, as well as a broader definition of what AI will look like in practice, which includes not just machine learning models and their predictions, but also the new systems for care delivery that they enable. The careful design, implementation, and evaluation of these AI enabled systems will be important in the effort to understand how AI can improve healthcare.

Abstract Background The goal of Lean Enterprise Transformation (LET) is to go beyond simply using Lean tools and instead embed Lean principles and practices in the system so that it becomes a fundamental, collective mindset of the entire enterprise. The Veterans Engineering Resource Center (VERC) launched the Veterans Affairs (VA) LET pilot program to improve quality, safety, and the Veteran’s experience. A national evaluation will examine the pilot program sites’ implementation processes, outcomes and impacts, and abilities to improve LET adoption and sustainment. This paper describes the evaluation design for the VA LET national evaluation and describes development of a conceptual framework to evaluate LET specifically in healthcare settings. Methods A targeted literature review of Lean evaluation frameworks was performed to inform the development of the conceptual framework. Key domains were identified by a multidisciplinary expert group and then validated with key stakeholders. The national evaluation design will examine LET implementation using qualitative, survey, and quantitative methods at ten VA facilities. Qualitative data include site visits, interviews, and field observation notes. Survey data include an employee engagement survey to be administered to front-line staff at all pilot sites. Quantitative data include site-level quality improvement metrics collected by the Veterans Services Support Center. Qualitative, quantitative, and mixed-methods analyses will be conducted to examine implementation of LET strategic initiatives and variations in implementation success across sites. Discussion This national evaluation of a large-scale LET implementation effort will provide insights helpful to other systems interested in embarking on a Lean journey. Additionally, we created a multi-faceted conceptual framework to capture the specific features of a Lean healthcare organization. This framework will guide this evaluation and may be useful as an assessment tool for other organizations interested in implementing Lean principles at an enterprise level.

OBJECTIVES/GOALS: The Virtual CTSA Visiting Scholars Program allows KL2 scholars to serve as visiting professors at host CTSA consortium institutions. This program facilitates connections with faculty outside scholars home institutions, as well as fostering collaboration among different CTSA hubs. Stanford aimed to incorporate participants into its KL2 program. METHODS/STUDY POPULATION: Stanford is one of 57 host institutions for the Virtual CTSA Visiting Scholars Program. The program includes a virtual meeting between each participant and a chosen faculty at the host institution. Program participants also give a virtual Grand Rounds lecture, open to the entire CTSA consortium. As well as encouraging Spectrum KL2 Scholars to participate in the virtual exchange, the Spectrum KL2 program incorporated the visiting scholars into weekly activities over the course of 3 months. Visiting scholars were invited to participate virtually in KL2 educational sessions, which provide career development training and mentoring. As meetings transitioned to in-person, KL2 scholars were allowed to attend virtually when needed. Hybrid in-person sessions were also conducive to participation by visiting scholars. RESULTS/ANTICIPATED RESULTS: Stanfords Spectrum KL2 program was virtual (Zoom) for academic year 2020-2021 and included 1:1 mentoring sessions, weekly career development seminars, and 1:1 peer virtual lunches to integrate the 9 junior faculty scholars. Visiting scholars joined the weekly Zoom meetings when their schedules allowed. KL2 faculty mentored visiting scholars for 3 months, exchanging ideas and forming collaborations. Each visiting participant was paired with a KL2 Scholar, who provided 1:1 peer mentorship. In addition, visiting scholars presented a work-in-progress seminar, to obtain feedback before the more formal Grand Rounds lecture. For academic year 2021-2022, Spectrum KL2 meetings include virtual and hybrid in-person sessions, allowing visiting scholars to join by Zoom during the 3-month virtual exchange program. DISCUSSION/SIGNIFICANCE: What set Stanfords Virtual CTSA Visiting Scholars Program apart was faculty engagement and mentorship provided to visiting participants. By incorporating visiting scholars virtually into the ongoing KL2 education program, participants could engage fully with mentors and scholars, even surpassing opportunities of pre-pandemic on-site visits.

Background. Shared decision making is a cornerstone of an informed consent process for cancer treatment, yet there are often many physician and patient-related barriers to participation in the process. Decisions in cancer care are often perceived as relating to a discrete, treatment decision event, yet there is evidence that decisions are longitudinal in nature and reflect a multifactorial experience. Objective. To explore patient and caregiver perceptions of the choices and decision-making opportunities within cancer care. Design. Qualitative in-depth interviews with 37 cancer patients and 7 caregivers carried out as part of an evaluation of a cancer center’s effort to improve patient experience. Results. Participants described decision making related to four distinct phases in complex cancer care, with physicians leading, and often limiting, decisions related to disease assessment and treatment options and access, and patients leading decisions related to physician selection. Though physicians led many decisions, patients had a moderating influence on treatment, such that if patients did not like options presented, they would reconsider their options and sometimes switch physicians. Patients had various strategies for dealing with uncertainty when faced with decisions, such as seeking additional information to make an informed choice or making a conscious choice to defer decision making to the physician. Limitations. Patients were sampled from one academic cancer center that serves a predominantly Caucasian, Asian, and Hispanic/Latino population and received complex treatment. Conclusion. Because of the complexity of cancer treatment, many patients felt as though they were a “passenger” in decision making about care and did not lead many of the decisions, though many patients trusted their doctors to make the best decisions and were comforted by their expertise.

Abstract Background Current evaluation methods are mismatched with the speed of health care innovation and needs of health care delivery partners. We introduce a qualitative approach called the lightning report method and its specific product—the “Lightning Report.” We compare implementation evaluation results across four projects to explore report sensitivity and the potential depth and breadth of lightning report method findings. Methods The lightning report method was refined over 2.5 years across four projects: team‐based primary care, cancer center transformation, precision health in primary care, and a national life‐sustaining decisions initiative. The novelty of the lightning report method is the application of Plus/Delta/Insight debriefing to dynamic implementation evaluation. This analytic structure captures Plus (“what works”), Delta (“what needs to be changed”), and Insights (participant or evaluator insights, ideas, and recommendations). We used structured coding based on implementation science barriers and facilitators outlined in the Consolidated Framework for Implementation Research (CFIR) applied to 17 Lightning Reports from four projects. Results Health care partners reported that Lighting Reports were valuable, easy to understand, and they implied reports supported “corrective action” for implementations. Comparative analysis revealed cross‐project emphasis on the domains of Inner Setting and Intervention Characteristics, with themes of communication, resources/staffing, feedback/reflection, alignment with simultaneous interventions and traditional care, and team cohesion. In three of the four assessed projects, the largest proportion of coding was to the clinic‐level domain of Inner Setting—ranging from 39% for the cancer center project to a high of 56% for the life‐sustaining decisions project. Conclusions The lightning report method can fill a gap in rapid qualitative approaches and is generalizable with consistent but flexible core methods. Comparative analysis suggests it is a sensitive tool, capable of uncovering differences and insights in implementation across projects. The Lightning Report facilitates partnered evaluation and communication with stakeholders by providing real‐time, actionable insights in dynamic health care implementations.

Abstract Background Although health disparities have been documented between Medicare beneficiaries based on age (

Abstract Background Many healthcare organizations have developed disclosure policies for large-scale adverse events, including the Veterans Health Administration (VA). This study evaluated VA’s national large-scale disclosure policy and identifies gaps and successes in its implementation. Methods Semi-structured qualitative interviews were conducted with leaders, hospital employees, and patients at nine sites to elicit their perceptions of recent large-scale adverse events notifications and the national disclosure policy. Data were coded using the constructs of the Consolidated Framework for Implementation Research (CFIR). Results We conducted 97 interviews. Insights included how to handle the communication of large-scale disclosures through multiple levels of a large healthcare organization and manage ongoing communications about the event with employees. Of the 5 CFIR constructs and 26 sub-constructs assessed, seven were prominent in interviews. Leaders and employees specifically mentioned key problem areas involving 1) networks and communications during disclosure, 2) organizational culture, 3) engagement of external change agents during disclosure, and 4) a need for reflecting on and evaluating the policy implementation and disclosure itself. Patients shared 5) preferences for personal outreach by phone in place of the current use of certified letters. All interviewees discussed 6) issues with execution and 7) costs of the disclosure. Conclusions CFIR analysis reveals key problem areas that need to be addresses during disclosure, including: timely communication patterns throughout the organization, establishing a supportive culture prior to implementation, using patient-approved, effective communications strategies during disclosures; providing follow-up support for employees and patients, and sharing lessons learned.

Background: Chronic hepatitis C viral (HCV) infection affects millions of Americans. Health care systems face complex choices between highly efficacious, costly treatments. This study assessed the cost-effectiveness of treatments for chronic, genotype 1 HCV monoinfected, treatment-naïve individuals in the Department of Veterans Affairs (VA) and general US health care systems. Methods: The study used a decision-analytic Markov model, employing appropriate payer perspectives and time horizons, and discounting benefits and costs at 3% annually. Interventions included the following: sofosbuvir/ledipasvir (SOF-LDV); ombitasvir/paritaprevir/ritonavir/dasabuvir (3D); sofosbuvir/simeprevir (SOF-SMV); sofosbuvir/pegylated interferon/ribavirin (SOF-RBV-PEG); boceprevir/pegylated interferon/ribavirin (BOC-RBV-PEG); and pegylated interferon/ribavirin (PEG-RBV). Outcomes were sustained virologic response (SVR), advanced liver disease, costs, quality adjusted life years (QALYs), and incremental cost-effectiveness. Results: SOF-LDV and 3D achieve high SVR rates, reducing advanced liver disease (>20% relative to no treatment), and increasing QALYs by >2 years per person. For the non-VA population, at current prices ($5040 per week for SOF-LDV; $4796 per week for 3D), SOF-LDV’s lifetime cost ($293,370) is $18,000 lower than 3D’s because of its shorter duration in subgroups. SOF-LDV costs $17,100 per QALY gained relative to no treatment. 3D costs $208,000 per QALY gained relative to SOF-LDV. Both dominate other treatments and are even more cost-effective for the VA, though VA aggregate treatment costs still exceed $4 billion at SOF-LDV prices of $3308 per week. Drug prices strongly determine relative cost-effectiveness for SOF-LDV and 3D; with price reductions of 20% to 30% depending on health system, 3D could be cost-effective relative to SOF-LDV. We currently lack head-to-head regimen effectiveness trials. Conclusions: New HCV treatments are cost-effective in multiple health care systems if trial-estimated efficacy is achieved in practice, though, at current prices, total expenditures could present substantial challenges.

Opiates used for acute pain are an established risk factor for chronic opioid use (COU). Patient characteristics contribute to progression from acute opioid use to COU, but most are not clinically modifiable. To develop and validate machine-learning algorithms that use claims data to predict progression from acute to COU in the Medicaid population, Adult opioid naïve Medicaid patients from 6 anonymized states who received an opioid prescription between 2015 and 2019 were included. Five machine learning (ML) Models were developed, and model performance assessed by area under the receiver operating characteristic curve (auROC), precision and recall. In the study, 29.9% (53820/180000) of patients transitioned from acute opioid use to COU. Initial opioid prescriptions in COU patients had increased morphine milligram equivalents (MME) (33.2 vs. 23.2), tablets per prescription (45.6 vs. 36.54), longer prescriptions (26.63 vs 24.69 days), and higher proportions of tramadol (16.06% vs. 13.44%) and long acting oxycodone (0.24% vs 0.04%) compared to non- COU patients. The top performing model was XGBoost that achieved average precision of 0.87 and auROC of 0.63 in testing and 0.55 and 0.69 in validation, respectively. Top-ranking prescription-related features in the model included quantity of tablets per prescription, prescription length, and emergency department claims. In this study, the Medicaid population, opioid prescriptions with increased tablet quantity and days supply predict increased risk of progression from acute to COU in opioid-naïve patients. Future research should evaluate the effects of modifying these risk factors on COU incidence.

BackgroundTreatment of surgical pain is a common reason for opioid prescriptions. Being able to predict which patients are at risk for opioid abuse, dependence, and overdose (opioid-related adverse outcomes [OR-AE]) could help physicians make safer prescription decisions. We aimed to develop a machine-learning algorithm to predict the risk of OR-AE following surgery using Medicaid data with external validation across states.MethodsFive machine learning models were developed and validated across seven US states (90-10 data split). The model output was the risk of OR-AE 6-months following surgery. The models were evaluated using standard metrics and area under the receiver operating characteristic curve (AUC) was used for model comparison. We assessed calibration for the top performing model and generated bootstrap estimations for standard deviations. Decision curves were generated for the top-performing model and logistic regression.ResultsWe evaluated 96,974 surgical patients aged 15 and 64. During the 6-month period following surgery, 10,464 (10.8%) patients had an OR-AE. Outcome rates were significantly higher for patients with depression (17.5%), diabetes (13.1%) or obesity (11.1%). The random forest model achieved the best predictive performance (AUC: 0.877; F1-score: 0.57; recall: 0.69; precision:0.48). An opioid disorder diagnosis prior to surgery was the most important feature for the model, which was well calibrated and had good discrimination.ConclusionsA machine learning models to predict risk of OR-AE following surgery performed well in external validation. This work could be used to assist pain management following surgery for Medicaid beneficiaries and supports a precision medicine approach to opioid prescribing.

BackgroundThe COVID-19 pandemic has amplified the need for web-based behavioral interventions to support individuals who are diagnosed with chronic conditions and their informal caregivers. However, most interventions focus on patient outcomes. Dyadic technology–enabled interventions that simultaneously improve outcomes for patients and caregivers are needed. ObjectiveThis study aimed to describe the methodology used to adapt a telephone-based, facilitated, and dyadic self-management program called Self-care Using Collaborative Coping Enhancement in Diseases (SUCCEED) into a self-guided, web-based version (web-SUCCEED) and to conduct usability testing for web-SUCCEED. MethodsWe developed web-SUCCEED in 6 steps: ideation—determine the intervention content areas; prototyping—develop the wireframes, illustrating the look and feel of the website; prototype refinement via feedback from focus groups; finalizing the module content; programming web-SUCCEED; and usability testing. A diverse team of stakeholders including content experts, web designers, patients, and caregivers provided input at various stages of development. Costs, including full-time equivalent employee, were summarized. ResultsAt the ideation stage, we determined the content of web-SUCCEED based on feedback from the program’s original pilot study. At the prototyping stage, the principal investigator and web designers iteratively developed prototypes that included inclusive design elements (eg, large font size). Feedback about these prototypes was elicited through 2 focus groups of veterans with chronic conditions (n=13). Rapid thematic analysis identified two themes: (1) web-based interventions can be useful for many but should include ways to connect with other users and (2) prototypes were sufficient to elicit feedback about the esthetics, but a live website allowing for continual feedback and updating would be better. Focus group feedback was incorporated into building a functional website. In parallel, the content experts worked in small groups to adapt SUCCEED’s content, so that it could be delivered in a didactic, self-guided format. Usability testing was completed by veterans (8/16, 50%) and caregivers (8/16, 50%). Veterans and caregivers gave web-SUCCEED high usability scores, noting that it was easy to understand, easy to use, and not overly burdensome. Notable negative feedback included “slightly agreeing” that the site was confusing and awkward. All veterans (8/8, 100%) agreed that they would choose this type of program in the future to access an intervention that aims to improve their health. Developing and maintaining the software and hosting together cost approximately US $100,000, excluding salary and fringe benefits for project personnel (steps 1-3: US $25,000; steps 4-6: US $75,000). ConclusionsAdapting an existing, facilitated self-management support program for delivery via the web is feasible, and such programs can remotely deliver content. Input from a multidisciplinary team of experts and stakeholders can ensure the program’s success. Those interested in adapting programs should have a realistic estimate of the budget and staffing requirements.

BackgroundOpioids are commonly prescribed for postoperative pain, but may lead to prolonged use and addiction. Diabetes impairs nerve function, complicates pain management, and makes opioid prescribing particularly challenging.MethodsThis retrospective observational study included a cohort of postoperative patients from a multisite academic health system to assess the relationship between diabetes, pain, and prolonged opioid use (POU), 2008-2019. POU was defined as a new opioid prescription 3-6 months after discharge. The odds that a patient had POU was assessed using multivariate logistic regression controlling for patient factors (e.g., demographic and clinical factors, as well as prior pain and opiate use).FindingsA total of 43,654 patients were included, 12.4% with diabetes. Patients with diabetes had higher preoperative pain scores (2.1 vs 1.9, pInterpretationIn conclusion, surgical patients with diabetes are at increased risk for POU even after controlling for likely covariates, yet they receive similar postoperative opiate therapy. The results suggest a more tailored approach to diabetic postoperative pain management is warranted.

Abstract Background Adaptation, a form of modification that aims to improve an intervention’s acceptability and sustainability in each context, is essential to successful implementation in some settings. Due to the COVID-19 pandemic, clinicians have rapidly adapted how they deliver patient care. PPE Portraits are a form of adaptation, whereby health workers affix a postcard size portrait of themselves to the front of their personal protective equipment (PPE) to foster human connection during COVID-19. Methods We used the expanded framework for reporting adaptations and modifications to evidence-based interventions (FRAME) method to better understand the reasoning behind and results of each adaptation. We hypothesized that using the FRAME in conjunction with design-thinking would lead to emerging best practices and that we would find adaptation similarities across sites. Throughout multiple implementations across 25 institutions, we piloted, tracked, and analyzed adaptations using FRAME and design thinking. For each adaptation, we assessed the stage of implementation, whether the change was planned, decision makers involved, level of delivery impacted, fidelity to original intervention, and the goal and reasoning for adaptation. We added three crucial components to the FRAME: original purpose of the adaptation, unintended consequences, and alternative adaptations. Results When implementing PPE Portraits across settings, from a local assisted living center’s memory unit to a pediatric emergency department, several requests for adaptations arose during early development stages before implementation. Adaptations primarily related to (1) provider convenience and comfort, (2) patient populations, and (3) scale. Providers preferred smaller portraits and rounded (rather than square) laminated edges that could potentially injure a patient. Affixing the portrait with a magnet was rejected given the potential choking hazard the magnetic strip presented for children. Other adaptations, related to ease of dissemination, included slowing the process down during early development and providing buttons, which could be produced easily at scale. Conclusions The FRAME was used to curate the reasoning for each adaptation and to inform future dissemination. We look forward to utilizing FRAME including our additions and design thinking, to build out a range of PPE Portrait best practices with accompanying costs and benefits.

BackgroundBoth clinicians and patients have increasingly turned to telemedicine to improve care access, even in physical examination–dependent specialties such as dermatology. However, little is known about whether teledermatology supports effective and timely transitions from inpatient to outpatient care, which is a common care coordination gap. ObjectiveUsing mixed methods, this study sought to retrospectively evaluate how teledermatology affected clinic capacity, scheduling efficiency, and timeliness of follow-up care for patients transitioning from inpatient to outpatient dermatology care. MethodsPatient-level encounter scheduling data were used to compare the number and proportion of patients who were scheduled and received in-clinic or video dermatology follow-ups within 14 and 90 days after discharge across 3 phases: June to September 2019 (before teledermatology), June to September 2020 (early teledermatology), and February to May 2021 (sustained teledermatology). The time from discharge to scheduling and completion of patient follow-up visits for each care modality was also compared. Dermatology clinicians and schedulers were also interviewed between April and May 2021 to assess their perceptions of teledermatology for postdischarge patients. ResultsMore patients completed follow-up within 90 days after discharge during early (n=101) and sustained (n=100) teledermatology use than at baseline (n=74). Thus, the clinic’s capacity to provide follow-up to patients transitioning from inpatient increased from baseline by 36% in the early (101 from 74) and sustained (100 from 74) teledermatology periods. During early teledermatology use, 61.4% (62/101) of the follow-ups were conducted via video. This decreased significantly to 47% (47/100) in the following year, when COVID-19–related restrictions started to lift (P=.04), indicating more targeted but still substantial use. The proportion of patients who were followed up within the recommended 14 days after discharge did not differ significantly between video and in-clinic visits during the early (33/62, 53% vs 15/39, 38%; P=.15) or sustained (26/53, 60% vs 28/47, 49%; P=.29) teledermatology periods. Interviewees agreed that teledermatology would continue to be offered. Most considered postdischarge follow-up patients to be ideal candidates for teledermatology as they had undergone a recent in-person assessment and might have difficulty attending in-clinic visits because of competing health priorities. Some reported patients needing technological support. Ultimately, most agreed that the choice of follow-up care modality should be the patient’s own. ConclusionsTeledermatology could be an important tool for maintaining accessible, flexible, and convenient care for recently discharged patients needing follow-up care. Teledermatology increased clinic capacity, even during the pandemic, although the timeliness of care transitions did not improve. Ultimately, the care modality should be determined through communication with patients to incorporate their and their caregivers’ preferences.

BackgroundCare coordination is challenging but crucial for children with medical complexity (CMC). Technology-based solutions are increasingly prevalent but little is known about how to successfully deploy them in the care of CMC. ObjectiveThe aim of this study was to assess the feasibility and acceptability of GoalKeeper (GK), an internet-based system for eliciting and monitoring family-centered goals for CMC, and to identify barriers and facilitators to implementation. MethodsWe used the Consolidated Framework for Implementation Research (CFIR) to explore the barriers and facilitators to the implementation of GK as part of a clinical trial of GK in ambulatory clinics at a children’s hospital (NCT03620071). The study was conducted in 3 phases: preimplementation, implementation (trial), and postimplementation. For the trial, we recruited providers at participating clinics and English-speaking parents of CMC

BackgroundThe early identification of clinical deterioration in patients in hospital units can decrease mortality rates and improve other patient outcomes; yet, this remains a challenge in busy hospital settings. Artificial intelligence (AI), in the form of predictive models, is increasingly being explored for its potential to assist clinicians in predicting clinical deterioration. ObjectiveUsing the Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model, this study aims to assess whether an AI-enabled work system improves clinical outcomes, describe how the clinical deterioration index (CDI) predictive model and associated work processes are implemented, and define the emergent properties of the AI-enabled work system that mediate the observed clinical outcomes. MethodsThis study will use a mixed methods approach that is informed by the SEIPS 2.0 model to assess both processes and outcomes and focus on how physician-nurse clinical teams are affected by the presence of AI. The intervention will be implemented in hospital medicine units based on a modified stepped wedge design featuring three stages over 11 months—stage 0 represents a baseline period 10 months before the implementation of the intervention; stage 1 introduces the CDI predictions to physicians only and triggers a physician-driven workflow; and stage 2 introduces the CDI predictions to the multidisciplinary team, which includes physicians and nurses, and triggers a nurse-driven workflow. Quantitative data will be collected from the electronic health record for the clinical processes and outcomes. Interviews will be conducted with members of the multidisciplinary team to understand how the intervention changes the existing work system and processes. The SEIPS 2.0 model will provide an analytic framework for a mixed methods analysis. ResultsA pilot period for the study began in December 2020, and the results are expected in mid-2022. ConclusionsThis protocol paper proposes an approach to evaluation that recognizes the importance of assessing both processes and outcomes to understand how a multifaceted AI-enabled intervention affects the complex team-based work of identifying and managing clinical deterioration. International Registered Report Identifier (IRRID)PRR1-10.2196/27532

BackgroundThe COVID-19 pandemic created new challenges to delivering safe and effective health care while minimizing virus exposure among staff and patients without COVID-19. Health systems worldwide have moved quickly to implement telemedicine in diverse settings to reduce infection, but little is understood about how best to connect patients who are acutely ill with nearby clinical team members, even in the next room. ObjectiveTo inform these efforts, this paper aims to provide an early example of inpatient telemedicine implementation and its perceived acceptability and effectiveness. MethodsUsing purposive sampling, this study conducted 15 semistructured interviews with nurses (5/15, 33%), attending physicians (5/15, 33%), and resident physicians (5/15, 33%) on a single COVID-19 unit within Stanford Health Care to evaluate implementation outcomes and perceived effectiveness of inpatient telemedicine. Semistructured interview protocols and qualitative analysis were framed around the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework, and key themes were identified using a rapid analytic process and consensus approach. ResultsAll clinical team members reported wide reach of inpatient telemedicine, with some use for almost all patients with COVID-19. Inpatient telemedicine was perceived to be effective in reducing COVID-19 exposure and use of personal protective equipment (PPE) without significantly compromising quality of care. Physician workflows remained relatively stable, as most standard clinical activities were conducted via telemedicine following the initial intake examination, though resident physicians reported reduced educational opportunities given limited opportunities to conduct physical exams. Nurse workflows required significant adaptations to cover nonnursing duties, such as food delivery and facilitating technology connections for patients and physicians alike. Perceived patient impact included consistent care quality, with some considerations around privacy. Reported challenges included patient–clinical team communication and personal connection with the patient, perceptions of patient isolation, ongoing technical challenges, and certain aspects of the physical exam. ConclusionsClinical team members reported inpatient telemedicine encounters to be acceptable and effective in reducing COVID-19 exposure and PPE use. Nurses adapted their workflows more than physicians in order to implement the new technology and bore a higher burden of in-person care and technical support. Recommendations for improved inpatient telemedicine use include information technology support and training, increased technical functionality, and remote access for the clinical team.

Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to exert their analgesic effects. The most commonly prescribed antidepressant, selective serotonin reuptake inhibitors (SSRIs), inhibits CYP-2D6 activity and therefore may reduce the effectiveness of prodrug opioids. We used a machine learning approach to identify patients prescribed a combination of SSRIs and prodrug opioids postoperatively and to examine the effect of this combination on postoperative pain control. Using EHR data from an academic medical center, we identified patients receiving surgery over a 9-year period. We developed and validated natural language processing (NLP) algorithms to extract depression-related information (diagnosis, SSRI use, symptoms) from structured and unstructured data elements. The primary outcome was the difference between preoperative pain score and postoperative pain at discharge, 3-week and 8-week time points. We developed computational models to predict the increase or decrease in the postoperative pain across the 3 time points by using the patient's EHR data (e.g. medications, vitals, demographics) captured before surgery. We evaluate the generalizability of the model using 10-fold cross-validation method where the holdout test method is repeated 10 times and mean area-under-the-curve (AUC) is considered as evaluation metrics for the prediction performance. We identified 4,306 surgical patients with symptoms of depression. A total of 14.1% were prescribed both an SSRI and a prodrug opioid, 29.4% were prescribed an SSRI and a non-prodrug opioid, 18.6% were prescribed a prodrug opioid but were not on SSRIs, and 37.5% were prescribed a non-prodrug opioid but were not on SSRIs. Our NLP algorithm identified depression with a F1 score of 0.95 against manual annotation of 300 randomly sampled clinical notes. On average, patients receiving prodrug opioids had lower average pain scores (p0.05). The machine learning algorithm accurately predicted the increase or decrease of the discharge, 3-week and 8-week follow-up pain scores when compared to the pre-operative pain score using 10-fold cross validation (mean area under the receiver operating characteristic curve 0.87, 0.81, and 0.69, respectively). Preoperative pain, surgery type, and opioid tolerance were the strongest predictors of postoperative pain control. We provide the first direct clinical evidence that the known ability of SSRIs to inhibit prodrug opioid effectiveness is associated with worse pain control among depressed patients. Current prescribing patterns indicate that prescribers may not account for this interaction when choosing an opioid. The study results imply that prescribers might instead choose direct acting opioids (e.g. oxycodone or morphine) in depressed patients on SSRIs.

BackgroundMost research manuscripts are not accepted for publication on first submission. A major part of the resubmission process is reformatting to another journal's specific requirements, a process separate from revising the scientific content. There has been little research to understand the magnitude of the burden imposed by the current resubmission process.MethodsWe analyzed original research article submission requirements from twelve randomly selected journals in each of eight scientific and clinical focus areas from the InCites Journal Citation Reports database. From the 96 journals selected, we randomly identified three recently published manuscripts and sent surveys to those first and/or corresponding authors (288 total) to solicit information on time spent reformatting resubmissions and opinions on the process.FindingsThere was significant variation in manuscript submission requirements for journals within the same scientific focus and only 4% of journals offered a fully format-free initial submission. Of 203 authors responding (71.5% response rate), only 11.8% expressed satisfaction with the resubmission process and 91% desired reforming the current system. Time spent on reformatting delays most publications by at least two weeks and by over three months in about 20% of manuscripts. The effort to comply with submission requirements has significant global economic burden, estimated at over $1.1 billion dollars annually when accounting for a research team's time.InterpretationWe demonstrate that there is significant resource utilization associated with resubmitting manuscripts, heretofore not properly quantified. The vast majority of authors are not satisfied with the current process. Addressing these issues by reconciling reformatting requirements among journals or adopting a universal format-free initial submission policy would help resolve a major subject for the scientific research community and provide more efficient dissemination of findings.

Background: Heart failure (HF) is associated with frequent exacerbations and shortened lifespan. Informal caregivers such as significant others often support self-management in patients with HF. However, existing programs that aim to enhance self-management seldom engage informal caregivers or provide tools that can help alleviate caregiver burden or improve collaboration between patients and their informal caregivers. Objective: To develop and pilot test a program targeting the needs of self-management support among HF patients as well as their significant others. Methods: We developed the Dyadic Health Behavior Change model and conducted semi-structured interviews to determine barriers to self-management from various perspectives. Participants’ feedback was used to develop a family-centered self-management program called SUCCEED: Self-management Using Couples’ Coping EnhancEment in Diseases. The goals of this program are to improve HF self-management, quality of life, communication within couples, relationship quality, and stress and caregiver burden. We conducted a pilot study with 17 Veterans with HF and their significant others to determine acceptability of the program. We piloted psychosocial surveys at baseline and after participants’ program completion to evaluate change in depressive symptoms, caregiver burden, self-management of HF, communication, quality of relationship, relationship mutuality, and quality of life. Results: Of the 17 couples, 14 completed at least 1 SUCCEED session. Results showed high acceptability for each of SUCCEED’s sessions. At baseline, patients reported poor quality of life, clinically significant depressive symptoms, and inadequate self-management of HF. After participating in SUCCEED, patients showed improvements in self-management of HF, communication, and relationship quality, while caregivers reported improvements in depressive symptoms and caregiver burden. Quality of life of both patients and significant others declined over time. Conclusions: In this small pilot study, we showed positive trends with involving significant others in self-management. SUCCEED has the potential of addressing the growing public health problem of HF among patients who receive care from their significant other.