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Modifications to Fried’s frailty phenotype (FFP) are common. We evaluated a self-reported modified frailty phenotype (Mod-FP) used among people with HIV (PWH). Among 522 PWH engaged in two longitudinal studies, we assessed validity of the four-item Mod-FP compared with the five-item FFP. We compared the phenotypes via receiver operator characteristic curves, agreement in classifying frailty, and criterion validity via association with having experienced falls. Mod-FP classified 8% of PWH as frail, whereas FFP classified 9%. The area under the receiver operator characteristic curve for Mod-FP classifying frailty was 0.93 (95% CI = 0.91–0.96). We observed kappa ranging from 0.64 (unweighted) to 0.75 (weighted) for categorizing frailty status. Both definitions found frailty associated with a greater odds of experiencing a fall; FFP estimated a slightly greater magnitude (i.e., OR) for the association than Mod-FP. The Mod-FP has good performance in measuring frailty among PWH and is reasonable to use when the gold standards of observed assessments (i.e., weakness and slowness) are not feasible.

ObjectiveDigital exposure notifications (DEN) systems were an emergency response to the coronavirus disease 2019 (COVID-19) pandemic, harnessing smartphone-based technology to enhance conventional pandemic response strategies such as contact tracing. We identify and describe performance measurement constructs relevant to the implementation of DEN tools: (1) reach (number of users enrolled in the intervention); (2) engagement (utilization of the intervention); and (3) effectiveness in preventing transmissions of COVID-19 (impact of the intervention). We also describe WA State’s experience utilizing these constructs to design data-driven evaluation approaches.MethodsWe conducted an environmental scan of DEN documentation and relevant publications. Participation in multidisciplinary collaborative environments facilitated shared learning. Compilation of available data sources and their relevance to implementation and operation workflows were synthesized to develop implementation evaluation constructs.ResultsWe identified 8 useful performance indicators within reach, engagement, and effectiveness constructs.DiscussionWe use implementation science to frame the evaluation of DEN tools by linking the theoretical constructs with the metrics available in the underlying disparate, deidentified, and aggregate data infrastructure. Our challenges in developing meaningful metrics include limited data science competencies in public health, validation of analytic methodologies in the complex and evolving pandemic environment, and the lack of integration with the public health infrastructure.ConclusionContinued collaboration and multidisciplinary consensus activities can improve the utility of DEN tools for future public health emergencies.

ImportanceDiscussions about goals of care are important for high-quality palliative care yet are often lacking for hospitalized older patients with serious illness.ObjectiveTo evaluate a communication-priming intervention to promote goals-of-care discussions between clinicians and hospitalized older patients with serious illness.Design, Setting, and ParticipantsA pragmatic, randomized clinical trial of a clinician-facing communication-priming intervention vs usual care was conducted at 3 US hospitals within 1 health care system, including a university, county, and community hospital. Eligible hospitalized patients were aged 55 years or older with any of the chronic illnesses used by the Dartmouth Atlas project to study end-of-life care or were aged 80 years or older. Patients with documented goals-of-care discussions or a palliative care consultation between hospital admission and eligibility screening were excluded. Randomization occurred between April 2020 and March 2021 and was stratified by study site and history of dementia.InterventionPhysicians and advance practice clinicians who were treating the patients randomized to the intervention received a 1-page, patient-specific intervention (Jumpstart Guide) to prompt and guide goals-of-care discussions.Main Outcomes and MeasuresThe primary outcome was the proportion of patients with electronic health record–documented goals-of-care discussions within 30 days. There was also an evaluation of whether the effect of the intervention varied by age, sex, history of dementia, minoritized race or ethnicity, or study site.ResultsOf 3918 patients screened, 2512 were enrolled (mean age, 71.7 [SD, 10.8] years and 42% were women) and randomized (1255 to the intervention group and 1257 to the usual care group). The patients were American Indian or Alaska Native (1.8%), Asian (12%), Black (13%), Hispanic (6%), Native Hawaiian or Pacific Islander (0.5%), non-Hispanic (93%), and White (70%). The proportion of patients with electronic health record–documented goals-of-care discussions within 30 days was 34.5% (433 of 1255 patients) in the intervention group vs 30.4% (382 of 1257 patients) in the usual care group (hospital- and dementia-adjusted difference, 4.1% [95% CI, 0.4% to 7.8%]). The analyses of the treatment effect modifiers suggested that the intervention had a larger effect size among patients with minoritized race or ethnicity. Among 803 patients with minoritized race or ethnicity, the hospital- and dementia-adjusted proportion with goals-of-care discussions was 10.2% (95% CI, 4.0% to 16.5%) higher in the intervention group than in the usual care group. Among 1641 non-Hispanic White patients, the adjusted proportion with goals-of-care discussions was 1.6% (95% CI, −3.0% to 6.2%) higher in the intervention group than in the usual care group. There was no evidence of differential treatment effects of the intervention on the primary outcome by age, sex, history of dementia, or study site.Conclusions and RelevanceAmong hospitalized older adults with serious illness, a pragmatic clinician-facing communication-priming intervention significantly improved documentation of goals-of-care discussions in the electronic health record, with a greater effect size in racially or ethnically minoritized patients.Trial RegistrationClinicalTrials.gov Identifier: NCT04281784

CONTEXT: Documented goals-of-care discussions are an important quality metric for patients with serious illness. Natural language processing (NLP) is a promising approach for identifying goals-of-care discussions in the electronic health record (EHR). OBJECTIVES: To compare three NLP modeling approaches for identifying EHR documentation of goals-of-care discussions and generate hypotheses about differences in performance. METHODS: We conducted a mixed-methods study to evaluate performance and misclassification for three NLP featurization approaches modeled with regularized logistic regression: bag-of-words (BOW), rule-based, and a hybrid approach. From a prospective cohort of 150 patients hospitalized with serious illness over 2018–2020, we collected 4,391 inpatient EHR notes; 99 (2.3%) contained documented goals-of-care discussions. We used leave-one-out cross-validation to estimate performance by comparing pooled NLP predictions to human abstractors with receiver-operating-characteristic (ROC) and precision-recall (PR) analyses. We qualitatively examined a purposive sample of 70 NLP-misclassified notes using content analysis to identify linguistic features that allowed us to generate hypotheses underpinning misclassification. RESULTS: All three modeling approaches discriminated between notes with and without goals-of-care discussions (AUC(ROC): BOW, 0.907; rule-based, 0.948; hybrid, 0.965). Precision and recall were only moderate (precision at 70% recall: BOW, 16.2%; rule-based, 50.4%; hybrid, 49.3%; AUC(PR): BOW, 0.505; rule-based, 0.579; hybrid, 0.599). Qualitative analysis revealed patterns underlying performance differences between BOW and rule-based approaches. CONCLUSION: NLP holds promise for identifying EHR-documented goals-of-care discussions. However, the rarity of goals-of-care content in EHR data limits performance. Our findings highlight opportunities to optimize NLP modeling approaches, and support further exploration of different NLP approaches to identify goals-of-care discussions.

Background and Objective Care partners of people living with dementia require support to knowledgeably navigate decision making about how and when to use monitoring technologies for care purposes. We conducted a pilot study of a novel self-administered intervention, “Let’s Talk Tech,” for people living with mild dementia and their care partners. This paper presents preliminary efficacy findings of this intervention designed to educate and facilitate dyadic communication about a range of technologies used in dementia care and to document the preferences of the person living with dementia. It is the first-of-its-kind decision-making and planning tool with a specific focus on technology use. Research Design and Methods We used a 1-group pretest–post-test design and paired t tests to assess change over 2 time periods in measures of technology comprehension, care partner knowledge of the participant living with mild Alzheimer’s disease’s (AD) preferences, care partner preparedness to make decisions about technology use, and mutual understanding. Thematic analysis was conducted on postintervention interview transcripts to elucidate mechanisms and experiences with Let’s Talk Tech. Results Twenty-nine mild AD dementia care dyads who live together completed the study. There was statistically significant improvement with medium and large effect sizes on outcome measures of care partners’ understanding of each technology, care partners’ perceptions of the person living with dementia’s understanding of each technology, knowledge of the person living with dementia’s preferences, decision-making preparedness, and care partners’ feelings of mutual understanding. Participants reported that it helped them have important and meaningful conversations about using technology. Discussion and Implications Let’s Talk Tech demonstrated promising preliminary efficacy on targeted measures that can lead to informed, shared decision making about technologies used in dementia care. Future studies should assess efficacy with larger samples and more diverse sample populations in terms of race, ethnicity, and dementia type.

Making EHR Data More Available for Research and Public Health (MedMorph) is a Centers for Disease Control and Prevention-led initiative developing and demonstrating a reference architecture (RA) and implementation, including Health Level Seven International Fast Healthcare Interoperability Resources (HL7 FHIR) implementation guides (IGs), describing how to leverage FHIR for aligned research and public health access to clinical data for automated data exchange. MedMorph engaged a technical expert panel of more than 100 members to model representative use cases, develop IGs (architectural and content), align with existing efforts in the FHIR community, and demonstrate the RA in research and public health uses. The RA IG documents common workflows needed to automatically send research data to Research Patient Data Repositories for multiple use cases. Sharing a common RA and canonical data model will improve data sharing for research and public health needs and generate evidence. MedMorph delivers a robust, reusable method to utilize data from electronic health records addressing multiple research and public health needs.

Goals-of-care discussions are important for patient-centered care among hospitalized patients with serious illness. However, there are little data on the occurrence, predictors, and timing of these discussions.To examine the occurrence, predictors, and timing of electronic health record (EHR)-documented goals-of-care discussions for hospitalized patients.This retrospective cohort study used natural language processing (NLP) to examine EHR-documented goals-of-care discussions for adults with chronic life-limiting illness or age ≥80 hospitalized 2015-2019. The primary outcome was NLP-identified documentation of a goals-of-care discussion during the index hospitalization. We used multivariable logistic regression to evaluate associations with baseline characteristics.Of 16,262 consecutive, eligible patients without missing data, 5,918 (36.4%) had a documented goals-of-care discussion during hospitalization; approximately 57% of these discussions occurred within 24 hours of admission. In multivariable analysis, documented goals-of-care discussions were more common for women (OR=1.26, 95%CI 1.18-1.36), older patients (OR=1.04 per year, 95%CI 1.03-1.04), and patients with more comorbidities (OR=1.11 per Deyo-Charlson point, 95%CI 1.10-1.13), cancer (OR=1.88, 95%CI 1.72-2.06), dementia (OR=2.60, 95%CI 2.29-2.94), higher acute illness severity (OR=1.12 per National Early Warning Score point, 95%CI 1.11-1.14), or prior advance care planning documents (OR=1.18, 95%CI 1.08-1.30). Documentation of these discussions was less common for racially or ethnically minoritized patients (OR=0.823, 95%CI 0.75-0.90).Among hospitalized patients with serious illness, documented goals-of-care discussions identified by NLP were more common among patients with older age and increased burden of acute or chronic illness, and less common among racially or ethnically minoritized patients. This suggests important disparities in goals-of-care discussions.

Substance use in the U.S. varies by geographic region. Opioid prescribing practices and marijuana, heroin, and methamphetamine availability are evolving differently across regions. We examined self-reported substance use among people living with HIV (PLWH) in care at seven sites from 2017-2019 to understand current regional substance use patterns. We calculated the percentage and standardized percentage of PLWH reporting current drug use and at-risk and binge alcohol use by U.S. Census Bureau geographic region and examined associations in adjusted logistic regression analyses. Among 7,686 PLWH, marijuana use was the most prevalent drug (30%), followed by methamphetamine/crystal (8%), cocaine/crack (7%), and illicit opioids (3%). One-third reported binge alcohol use (32%). Differences in percent of current use by region were seen for marijuana (24-41%) and methamphetamine/crystal (2-15%), with more use in the West and Northeast, and binge alcohol use (26-40%). In adjusted analyses, PLWH in the Midwest were significantly less likely to use methamphetamine/crystal (aOR: 0.13;0.06-0.25) or illicit opioids (aOR:0.16;0.05-0.53), and PLWH in the Northeast were more likely to use cocaine/crack (aOR:1.59;1.16-2.17), compared to PLWH in the West. Understanding differences in substance use patterns in the current era, as policies continue to evolve, will enable more targeted interventions in clinical settings among PLWH.

BACKGROUND In an effort to increase vaccination coverage in low-resource settings, digital tools have been introduced to better track immunization records, improve data management practices, and provide improved access to vaccination coverage data for decision-making. Despite the potential of these electronic systems to improve the provision of health services, few digital health interventions have been institutionalized at scale in low- and middle-income countries. OBJECTIVE In this paper, we aimed to describe how health care workers in Kenya had integrated an electronic immunization registry into their immunization clinic workflows and to use these findings to inform the development of a refined program theory on the registry’s usability. METHODS Informed by realist methodology, we developed a program theory to explain usability of the electronic immunization registry. We designed a qualitative study based on our theory to describe the barriers and facilitators influencing data entry and use. Qualitative data were collected through semistructured interviews with users and workflow observations of immunization clinic sessions. Our findings were summarized by context-mechanism-outcome relationships formed after analyzing our key themes across interviews and workflow observations. Using these relationships, we were able to identify common rules for future implementers. RESULTS Across the 12 facilities included in our study, 19 health care workers were interviewed, and 58 workflow sessions were observed. The common rules developed from our qualitative findings are as follows: rule 1—ensure that the users complete training to build familiarity with the system, understand the value of the system and data, and know where to find support; rule 2—confirm that the system captures all data needed for users to provide routine health care services and is easy to navigate; rule 3—identify work-arounds for poor network, system performance, and too few staff or resources; and rule 4—make users aware of expected changes to their workflow, and how these changes might differ over time and by facility size or number of patients. Upon study completion, we revised the program theory to reflect the importance of the goals and workflows of electronic immunization registries aligning with reality. CONCLUSIONS We created a deeper understanding of the underlying mechanisms for usability of the registry. We found that the electronic immunization registry had high acceptability among users; however, there were numerous barriers to using the system, even under ideal conditions, causing a misalignment between the system and the reality of the users’ workflows and their environment. Human-centered design and human-factors methods can assist during pilot stages to better align systems with users’ needs and again after scale-up to ensure that interventions are suitable for all user settings.

Objective Lung transplant (LTx) saves lives in cystic fibrosis (CF). However, many potential candidates express uncertainty about LTx and die before receiving this treatment. CF guidelines recommend LTx education and clinical discussions well before the need for LTx arises, but limited patient resources exist. Materials and Methods We engaged people with CF and CF physicians in human-centered design of “Take On Transplant” (TOT), a web-based education tool to prepare patients for LTx discussions. Across 3 phases, needs assessment, design groups, and iterative user testing of TOT, we refined TOT from wireframe prototypes, to an interactive website, to a fully functional intervention ready for clinical trials. Results Fifty-five people with CF and 105 physicians identified information needs to prepare for LTx discussions. Design groups (n = 14 participants) then established core requirements: didactic education (“Resource Library”), patient narratives (“CF Stories”), frequently asked questions (“FAQ”), and self-assessment to tailor content (“My CF Stage”). Iterative usability testing (n = 39) optimized the design of CF Stories and prototype layout. We then developed the TOT website and demonstrated feasibility and preliminary efficacy of use through 2-week field testing (n = 9). Discussion Our human-centered design process provided guidance for educational tools to serve the evolving needs of potential LTx candidates. Our findings support the process of patient deliberation as a foundation for shared decision-making in CF, and inform educational tools that could potentially translate beyond LTx. Conclusion TOT fills a critical gap in preparing people with CF for shared decision-making about LTx and may serve as a model for educational tools for other preference-sensitive decisions.

We assessed acceptability/usability of tablet-based patient-reported outcome (PRO) assessments among patients in HIV care, and relationships with health outcomes using a modified version of the 6-item Acceptability E-Scale (AES) within a self-administered PRO assessment. Using multivariable linear regression, we measured associations between patient characteristics and continuous combined AES score. Among 786 patients (median age=48; 91% male; 49% white; 17% Spanish-speaking) overall mean score was 26/30 points (SD: 4.4). Mean scores per dimension (max 5, 1=lowest acceptability, 5=highest): ease of use 4.7, understandability 4.7, time burden 4.3, overall satisfaction 4.3, helpfulness describing symptoms/behaviors 4.2, and enjoyability 3.8. Higher overall score was associated with race/ethnicity (+1.3 points/African-American patients (95%CI:0.3-2.3); +1.6 points/Latino patients (95%CI:0.9-2.3) compared to white patients). Patients completing PROs in Spanish scored +2.4 points on average (95%CI:1.6-3.3). Higher acceptability was associated with better quality of life (0.3 points (95%CI:0.2-0.5)) and adherence (0.4 points (95%CI:0.2-0.6)). Lower acceptability was associated with: higher depression symptoms (−0.9 points (95%CI:-1.4 to −0.4)); recent illicit opioid use (−2.0 points (95%CI:-3.9 to −0.2)); multiple recent sex partners (−0.8 points (95%CI:−1.5 to −0.1)). While patients endorsing depression symptoms, recent opioid use, condomless sex, or multiple sex partners found PROs to be less acceptable, overall, patients found self-administered, tablet-based PRO assessments to be highly acceptable and easy to use.

Introduction: Many patients use mobile devices to track health conditions by recording patient-generated health data. However, patients and clinicians may disagree how to use these data. Objective: To systematically review the literature to identify how patient-generated health data and patient-reported outcomes collected outside of clinical settings can affect patient–clinician relationships within surgery and primary care. Methods: Six research databases were queried for publications documenting the effect of patient-generated health data or patient-reported outcomes on patient–clinician relationships. We conducted thematic synthesis of the results of the included publications. Results: Thirteen of the 3204 identified publications were included for synthesis. Three main themes were identified: patient-generated health data supported patient–clinician communication and health awareness, patients desired for their clinicians to be involved with their patient-generated health data, which clinicians had difficulty accommodating, and patient-generated health data platform features may support or hinder patient–clinician collaboration. Conclusion: Patient-generated health data and patient-reported outcomes may improve patient health awareness and communication with clinicians but may negatively affect patient–clinician relationships.

BackgroundPeople with HIV (PWH) are at increased risk of cardiovascular comorbidities and substance use is a potential predisposing factor. We evaluated associations of tobacco smoking and alcohol use with venous thromboembolism (VTE) in PWH.MethodsWe assessed incident, centrally adjudicated VTE among 12957 PWH within the Centers for AIDS Research Network of Integrated Clinical Systems (CNICS) cohort between January 2009 and December 2018. Using separate Cox proportional hazards models, we evaluated associations of time-updated alcohol and cigarette use with VTE, adjusting for demographic and clinical characteristics. Smoking was evaluated as pack-years and never, former, or current use with current cigarettes per day. Alcohol use was parameterized using categorical and continuous alcohol use score, frequency of use, and binge frequency.ResultsDuring a median of 3.6years of follow-up, 213 PWH developed a VTE. One-third of PWH reported binge drinking and 40% reported currently smoking. In adjusted analyses, risk of VTE was increased among both current (HR: 1.44, 95% CI: 1.02-2.03) and former (HR: 1.44, 95% CI: 0.99-2.07) smokers compared to PWH who never smoked. Additionally, total pack-years among ever-smokers (HR: 1.10 per 5 pack-years; 95% CI: 1.03-1.18) was associated with incident VTE in a dose-dependent manner. Frequency of binge drinking was associated with incident VTE (HR: 1.30 per 7days/month, 95% CI: 1.11-1.52); however, alcohol use frequency was not. Severity of alcohol use was not significantly associated with VTE.ConclusionsCurrent smoking and pack-year smoking history were dose-dependently associated with incident VTE among PWH in CNICS. Binge drinking was also associated with VTE. Interventions for smoking and binge drinking may decrease VTE risk among PWH.

Background In an effort to increase vaccination coverage in low-resource settings, digital tools have been introduced to better track immunization records, improve data management practices, and provide improved access to vaccination coverage data for decision-making. Despite the potential of these electronic systems to improve the provision of health services, few digital health interventions have been institutionalized at scale in low- and middle-income countries. Objective In this paper, we aimed to describe how health care workers in Kenya had integrated an electronic immunization registry into their immunization clinic workflows and to use these findings to inform the development of a refined program theory on the registry’s usability. Methods Informed by realist methodology, we developed a program theory to explain usability of the electronic immunization registry. We designed a qualitative study based on our theory to describe the barriers and facilitators influencing data entry and use. Qualitative data were collected through semistructured interviews with users and workflow observations of immunization clinic sessions. Our findings were summarized by context-mechanism-outcome relationships formed after analyzing our key themes across interviews and workflow observations. Using these relationships, we were able to identify common rules for future implementers. Results Across the 12 facilities included in our study, 19 health care workers were interviewed, and 58 workflow sessions were observed. The common rules developed from our qualitative findings are as follows: rule 1—ensure that the users complete training to build familiarity with the system, understand the value of the system and data, and know where to find support; rule 2—confirm that the system captures all data needed for users to provide routine health care services and is easy to navigate; rule 3—identify work-arounds for poor network, system performance, and too few staff or resources; and rule 4—make users aware of expected changes to their workflow, and how these changes might differ over time and by facility size or number of patients. Upon study completion, we revised the program theory to reflect the importance of the goals and workflows of electronic immunization registries aligning with reality. Conclusions We created a deeper understanding of the underlying mechanisms for usability of the registry. We found that the electronic immunization registry had high acceptability among users; however, there were numerous barriers to using the system, even under ideal conditions, causing a misalignment between the system and the reality of the users’ workflows and their environment. Human-centered design and human-factors methods can assist during pilot stages to better align systems with users’ needs and again after scale-up to ensure that interventions are suitable for all user settings.

ImportanceMany clinical trial outcomes are documented in free-text electronic health records (EHRs), making manual data collection costly and infeasible at scale. Natural language processing (NLP) is a promising approach for measuring such outcomes efficiently, but ignoring NLP-related misclassification may lead to underpowered studies.ObjectiveTo evaluate the performance, feasibility, and power implications of using NLP to measure the primary outcome of EHR-documented goals-of-care discussions in a pragmatic randomized clinical trial of a communication intervention.Design, Setting, and ParticipantsThis diagnostic study compared the performance, feasibility, and power implications of measuring EHR-documented goals-of-care discussions using 3 approaches: (1) deep-learning NLP, (2) NLP-screened human abstraction (manual verification of NLP-positive records), and (3) conventional manual abstraction. The study included hospitalized patients aged 55 years or older with serious illness enrolled between April 23, 2020, and March 26, 2021, in a pragmatic randomized clinical trial of a communication intervention in a multihospital US academic health system.Main Outcomes and MeasuresMain outcomes were natural language processing performance characteristics, human abstractor-hours, and misclassification-adjusted statistical power of methods of measuring clinician-documented goals-of-care discussions. Performance of NLP was evaluated with receiver operating characteristic (ROC) curves and precision-recall (PR) analyses and examined the effects of misclassification on power using mathematical substitution and Monte Carlo simulation.ResultsA total of 2512 trial participants (mean [SD] age, 71.7 [10.8] years; 1456 [58%] female) amassed 44 324 clinical notes during 30-day follow-up. In a validation sample of 159 participants, deep-learning NLP trained on a separate training data set identified patients with documented goals-of-care discussions with moderate accuracy (maximal F1 score, 0.82; area under the ROC curve, 0.924; area under the PR curve, 0.879). Manual abstraction of the outcome from the trial data set would require an estimated 2000 abstractor-hours and would power the trial to detect a risk difference of 5.4% (assuming 33.5% control-arm prevalence, 80% power, and 2-sided α = .05). Measuring the outcome by NLP alone would power the trial to detect a risk difference of 7.6%. Measuring the outcome by NLP-screened human abstraction would require 34.3 abstractor-hours to achieve estimated sensitivity of 92.6% and would power the trial to detect a risk difference of 5.7%. Monte Carlo simulations corroborated misclassification-adjusted power calculations.Conclusions and RelevanceIn this diagnostic study, deep-learning NLP and NLP-screened human abstraction had favorable characteristics for measuring an EHR outcome at scale. Adjusted power calculations accurately quantified power loss from NLP-related misclassification, suggesting that incorporation of this approach into the design of studies using NLP would be beneficial.

Background Digital health interventions (DHI) have the potential to improve the management and utilization of health information to optimize health care worker performance and provision of care. Despite the proliferation of DHI projects in low-and middle-income countries, few have been evaluated in an effort to understand their impact on health systems and health-related outcomes. Although more evidence is needed on their impact and effectiveness, the use of DHIs among immunization programs has become more widespread and shows promise for improving vaccination uptake and adherence to immunization schedules. Methods Our aim was to assess the impact of an electronic immunization registry (EIR) using an interrupted time-series analysis to analyze the effect on proportion of on-time vaccinations following introduction of an EIR in Tanzania. We hypothesized that the introduction of the EIR would lead to statistically significant changes in vaccination timeliness at 3, 6, and > 6 months post-introduction. Results For our primary analysis, we observed a decrease in the proportion of on-time vaccinations following EIR introduction. In contrast, our sensitivity analysis estimated improvements in timeliness among those children with complete vaccination records. However, we must emphasize caution interpreting these findings as they are likely affected by implementation challenges. Conclusions This study highlights the complexities of using digitized individual-level routine health information system data for evaluation and research purposes. EIRs have the potential to improve vaccination timeliness, but analyses using EIR data can be complicated by data quality issues and inconsistent data entry leading to difficulties interpreting findings.

Background: An optimal adherence to antiretroviral therapy (ART) is fundamental for suppression of HIV viral load and favourable treatment outcomes. Patient-reported outcomes (PROs) are effective tools for improving patient–provider communication and focusing providers’ awareness on current health problems. The objectives of this analysis were (1) to determine the feasibility of implementing an electronic screening tool to measure PROs in a Canadian HIV clinic to obtain information on ART adherence and related factors and (2) to determine the factors related to sub-optimal adherence. Methods: This implementation research with a convenience sample of 600 people living with HIV (PLWH) was conducted in a busy, academic, urban HIV clinic in Toronto, Canada. PLWH were approached to participate in PRO assessments just prior to their in-clinic appointments, including health-related domains such as mental health, housing, nutrition, financial stress and medication adherence, and responses were summarized on a single sheet available for providers to review. Feasibility of implementing PROs was assessed by quantifying response rate, completion rate, time taken and participation rate. Medication adherence was elicited by self-report of the percentage of prescribed HIV medications taken in the last month. Unadjusted and adjusted odds ratios were estimated from logistic regression models to identify factors associated with adherence of

Many patients presenting to emergency departments (EDs) do not have primary care and risk being lost to follow-up. Technology has been used successfully in surgical populations for wound care follow-up yet this is not well studied in ED populations.We aimed to conduct a pilot study demonstrating "smartphone" application-based follow-up after wound care in the ED.We enrolled participants in 2 urban EDs using a smartphone application called Mobile Post-Operative Wound Evaluator (mPOWEr) and defined participation as photographic submission at any time during the study period. We collected demographic data, frequency of use of mPOWEr, number of photographs uploaded, and timing of uploads.We approached patients for study enrollment, and 67 patients (28%) were not enrolled because they had no access to a smartphone. Seventy-one patients (30%) declined to enroll, leaving 100 (42%) successfully enrolled. Smartphone ownership was more common among patients40 years of age (81% vs. 64%, p = 0.004), more common among white patients than nonwhite patients (75% vs. 15%, p = 0.046), more common among patients approached at the university medical center than the trauma center (84% vs. 66%, p = 0.003), and among patients with commercial or other insurance than those with Medicare or Medicaid (92% vs. 54%, p 0.001). Of those enrolled, 58% submitted a photograph.Patients presenting for wound care to the ED will participate in smartphone-based app communication for wound care follow-up and are satisfied with this option. Disparities in smartphone access must be considered when using this follow-up method.

Background: Emerging technologies such as smartphones and wearable sensors have enabled the paradigm shift to new patient-centered healthcare, together with recent mobile health (mHealth) app development. One such promising healthcare app is incision monitoring based on patient-taken incision images. In this review, challenges and potential solution strategies are investigated for surgical site infection (SSI) detection and evaluation using surgical site images taken at home. Methods: Potential image quality issues, feature extraction, and surgical site image analysis challenges are discussed. Recent image analysis and machine learning solutions are reviewed to extract meaningful representations as image markers for incision monitoring. Discussions on opportunities and challenges of applying these methods to derive accurate SSI prediction are provided. Conclusions: Interactive image acquisition as well as customized image analysis and machine learning methods for SSI monitoring will play critical roles in developing sustainable mHealth apps to achieve the expected outcomes of patient-taken incision images for effective out-of-clinic patient-centered healthcare with substantially reduced cost.

Background: Patients increasingly use mobile devices to send text messages and photographic data to surgeons. There is potential to harness this patient-generated health data (PGHD) for clinical and public health surveillance of surgical site infection (SSI). Leveraging PGHD collected via remote monitoring in the post-operative period has the potential to produce important benefits for patients, surgeons, care teams, and infection surveillance and prevention. Methods: We conducted a health technology assessment (HTA), drawing heavily on stakeholder engagement to better understand current and potential uses of PGHD in post-operative care. Stakeholder engagement activities included assembling an advisory board composed of stakeholder experts, interviewing key informants, and seeking out stakeholder guidance to synthesize evidence from interviews, literature review, and technical app review in order to develop recommendations on the use of PGHD in SSI surveillance. Results: We conducted a review of the published literature, a technical/market scan of available apps for capturing post-operative PGHD, and two rounds of key informant interviews with stakeholders. In addition, we held a day-long workshop to solicit stakeholder feedback on initial findings of the project and to guide additional work. These activities culminated in an HTA report that provides guidance and recommendations on the use of PGHD in SSI surveillance, including practice, research, and public health surveillance, and identifies open issues on post-operative use of PGHD for which additional evidence and experience are needed to optimize application of those data for clinical and public health purposes. Conclusion: Stakeholders, individuals with direct experience, or interest in a given topic are critical to the HTA process. They provide insight to guide the work conducted, ensure that the topics addressed are relevant and important, and that products of the work are accessible and meaningful to the individuals who will be most impacted.

Background: There has been tremendous growth in the amount of new surgical site infection (SSI) data generated. Key challenges exist in understanding the data for robust clinical decision-support. Limitations of traditional methodologies to handle these data led to the emergence of artificial intelligence (AI). This article emphasizes the capabilities of AI to identify patterns of SSI data. Method: Artificial intelligence comprises various subfields that present potential solutions to identify patterns of SSI data. Discussions on opportunities, challenges, and limitations of applying these methods to derive accurate SSI prediction are provided. Results: Four main challenges in dealing with SSI data were defined: (1) complexities in using SSI data, (2) disease knowledge, (3) decision support, and (4) heterogeneity. The implications of some of the recent advances in AI methods to optimize clinical effectiveness were discussed. Conclusions: Artificial intelligence has the potential to provide insight in detecting and decision-support of SSI. As we turn SSI data into intelligence about the disease, we increase the possibility of improving surgical practice with the promise of a future optimized for the highest quality patient care.

Background The updated Veterans Aging Cohort Study (VACS) Index 2.0 combines general and human immunodeficiency virus (HIV)–specific biomarkers to generate a continuous score that accurately discriminates risk of mortality in diverse cohorts of persons with HIV (PWH), but a score alone is difficult to interpret. Using data from the North American AIDS Cohort Collaboration (NA-ACCORD), we translate VACS Index 2.0 scores into validated probability estimates of mortality. Methods Because complete mortality ascertainment is essential for accurate calibration, we restricted analyses to cohorts with mortality from the National Death Index or equivalent sources. VACS Index 2.0 components were ascertained from October 1999 to April 2018. Mortality was observed up to March 2019. Calibration curves compared predicted (estimated by fitting a gamma model to the score) to observed mortality overall and within subgroups: cohort (VACS/NA-ACCORD subset), sex, age 500 copies/mL, CD4 count Results Among 37230 PWH in VACS and 8061 PWH in the NA-ACCORD subset, median age was 53 and 44 years; 3% and 19% were women; and 48% and 39% were black. Discrimination in NA-ACCORD (C-statistic = 0.842 [95% confidence interval {CI}, .830–.854]) was better than in VACS (C-statistic = 0.813 [95% CI, .809–.817]). Predicted and observed mortality largely overlapped in VACS and the NA-ACCORD subset, overall and within subgroups. Conclusions Based on this validation, VACS Index 2.0 can reliably estimate probability of all-cause mortality, at various follow-up times, among PWH in North America.

As the COVID-19 pandemic continues to unfold and states experience the impacts of reopened economies, it is critical to efficiently manage new outbreaks through widespread testing and monitoring of both new and possible cases. Existing labor-intensive public health workflows may benefit from information collection directly from individuals through patient-reported outcomes (PROs) systems. Our objective was to develop a reusable, mobile-friendly application for collecting PROs and experiences to support COVID-19 symptom self-monitoring and data sharing with appropriate public health agencies, using Fast Healthcare Interoperability Resources (FHIR) for interoperability. We conducted a needs assessment and designed and developed StayHome, a mobile PRO administration tool. FHIR serves as the primary data model and driver of business logic. Keycloak, AWS, Docker, and other technologies were used for deployment. Several FHIR modules were used to create a novel “FHIR-native” application design. By leveraging FHIR to shape not only the interface strategy but also the information architecture of the application, StayHome enables the consistent standards-based representation of data and reduces the barrier to integration with public health information systems. FHIR supported rapid application development by providing a domain-appropriate data model and tooling. FHIR modules and implementation guides were referenced in design and implementation. However, there are gaps in the FHIR specification which must be recognized and addressed appropriately. StayHome is live and accessible to the public at https://stayhome.app. The code and resources required to build and deploy the application are available from https://github.com/uwcirg/stayhome-project.

Introduction Foundational to a learning health system (LHS) is the presence of a data infrastructure that can support continuous learning and improve patient outcomes. To advance their capacity to drive patient‐centered care, health systems are increasingly looking to expand the electronic capture of patient data, such as electronic patient‐reported outcome (ePRO) measures. Yet ePROs bring unique considerations around workflow, measurement, and technology that health systems may not be poised to navigate. We report on our effort to develop generalizable learnings that can support the integration of ePROs into clinical practice within an LHS framework. Methods Guided by action research methodology, we engaged in iterative cycles of planning, acting, observing, and reflecting around ePRO use with two primary goals: (1) mobilize an ePRO community of practice to facilitate knowledge sharing, and (2) establish guidelines for ePRO use in the context of LHS practice. Multiple, emergent data collection activities generated generalizable guidelines that document the tangible best practices for ePRO use in clinical care. We organized guidelines around thematic areas that reflect LHS structures and stakeholders. Results Three core thematic areas (and 24 guidelines) emerged. The theme of governance reflects the importance of leadership, knowledge management, and facilitating organizational learning around best practice models for ePRO use. The theme of integration considers the intersection of workflow, technology, and human factors for ePROs across areas of care delivery. Lastly, the theme of reporting reflects critical considerations for curating data and information, designing system functions and interactions, and presentation of ePRO data to support the translation of knowledge to action. Conclusions The guidelines produced from this work highlight the complex, multidisciplinary nature of implementing change within LHS contexts, and the value of action research approaches to enable rapid, iterative learning that leverages the knowledge and experience of communities of practice.

Natural Language Processing (NLP) is a key enabling technology for re-use of information in free-text clinical notes. However, a barrier to deployment is the availability of labeled corpora for supervised machine learning, which are expensive to acquire as they must be annotated by experienced clinicians. Where corpora are available, they may be opportunistically collected and thus vulnerable to bias. Here we evaluate an approach for accounting for dataset bias in the context of identifying specific patient-provider interactions. In this context, bias is the result of a phenomenon being over or under-represented in a particular type of clinical note as a result of the way a dataset was curated. Using a clinical dataset which represents a great deal of variation in terms of author and setting, we control for confounding variables using a backdoor adjustment approach [1, 2], which to our knowledge has not been previously applied the clinical domain. This approach improves precision by up to 5% and the adjusted models’ scores for false positives are generally lower, resulting in a more generalizable model with the potential to enhance the downstream utility of models trained using opportunistically collected clinical corpora.

Background Most studies of stroke in people living with HIV (PLWH) do not use verified stroke diagnoses, are small, and/or do not differentiate stroke types and subtypes. Setting CNICS, a U.S. multisite clinical cohort of PLWH in care. Methods We implemented a centralized adjudication stroke protocol to identify stroke type, subtype, and precipitating conditions identified as direct causes including infection and illicit drug use in a large diverse HIV cohort. Results Among 26,514 PLWH, there were 401 strokes, 75% of which were ischemic. Precipitating factors such as sepsis or same-day cocaine use were identified in 40% of ischemic strokes. Those with precipitating factors were younger, had more severe HIV disease, and fewer traditional stroke risk factors such as diabetes and hypertension. Ischemic stroke subtypes included cardioembolic (20%), large vessel atherosclerosis (13%), and small vessel (24%) ischemic strokes. Individuals with small vessel strokes were older, were more likely to have a higher current CD4 cell count than those with cardioembolic strokes and had the highest mean blood pressure of the ischemic stroke subtypes. Conclusion Ischemic stroke, particularly small vessel and cardioembolic subtypes, were the most common strokes among PLWH. Traditional and HIV-related risk factors differed by stroke type/subtype. Precipitating factors including infections and drug use were common. These results suggest that there may be different biological phenomena occurring among PLWH and that understanding HIV-related and traditional risk factors and in particular precipitating factors for each type/subtype may be key to understanding, and therefore preventing, strokes among PLWH.

Context Goals-of-care discussions are an important quality metric in palliative care. However, goals-of-care discussions are often documented as free text in diverse locations. It is difficult to identify these discussions in the electronic health record (EHR) efficiently. Objectives To develop, train, and test an automated approach to identifying goals-of-care discussions in the EHR, using natural language processing (NLP) and machine learning (ML). Methods From the electronic health records of an academic health system, we collected a purposive sample of 3183 EHR notes (1435 inpatient notes and 1748 outpatient notes) from 1426 patients with serious illness over 2008–2016, and manually reviewed each note for documentation of goals-of-care discussions. Separately, we developed a program to identify notes containing documentation of goals-of-care discussions using NLP and supervised ML. We estimated the performance characteristics of the NLP/ML program across 100 pairs of randomly partitioned training and test sets. We repeated these methods for inpatient-only and outpatient-only subsets. Results Of 3183 notes, 689 contained documentation of goals-of-care discussions. The mean sensitivity of the NLP/ML program was 82.3% (SD 3.2%), and the mean specificity was 97.4% (SD 0.7%). NLP/ML results had a median positive likelihood ratio of 32.2 (IQR 27.5–39.2) and a median negative likelihood ratio of 0.18 (IQR 0.16–0.20). Performance was better in inpatient-only samples than outpatient-only samples. Conclusion Using NLP and ML techniques, we developed a novel approach to identifying goals-of-care discussions in the EHR. NLP and ML represent a potential approach toward measuring goals-of-care discussions as a research outcome and quality metric.

Most people prefer to die at home, yet most do not. Understanding factors associated with terminal hospitalization may inform interventions to improve care.Among patients with chronic illness receiving care in a multihospital healthcare system, we identified the following: (1) predictors of death in any hospital; (2) predictors of death in a hospital outside the system; and (3) trends from 2010 to 2015.Retrospective cohort using death certificates and electronic health records. Settings/Subjects: Decedents with one of nine chronic illnesses.Among 20,486 decedents, those most likely to die in a hospital were younger (odds ratio [OR] 0.977, confidence interval [CI] 0.974-0.980), with more comorbidities (OR 1.188, CI 1.079-1.308), or more outpatient providers (OR 1.031, CI 1.015-1.047); those with cancer or dementia, or more outpatient visits were less likely to die in hospital. Among hospital deaths, patients more likely to die in an outside hospital had lower education (OR 0.952, CI 0.923-0.981), cancer (OR 1.388, CI 1.198-1.608), diabetes (OR 1.507, CI 1.262-1.799), fewer comorbidities (OR 0.745, CI 0.644-0.862), or fewer hospitalizations within the system during the prior year (OR 0.900, CI 0.864-0.938). Deaths in hospital did not change from 2010 to 2015, but the proportion of hospital deaths outside the system increased (p 0.022).Patients dying in the hospital who are more likely to die in an outside hospital, and therefore at greater risk for inaccessibility of advance care planning, were more likely to be less well-educated and have cancer or diabetes, fewer comorbidities, and fewer hospitalizations. These findings may help target interventions to improve end-of-life care.

Context Recent analyses of Medicare data show decreases over time in intensity of end-of-life care. Few studies exist regarding trends in intensity of end-of-life care for those under 65 years of age. Objectives To examine recent temporal trends in place of death, and both hospital and intensive care unit (ICU) utilization, for age-stratified decedents with chronic, life-limiting diagnoses ( Methods Retrospective cohort using death certificates and electronic health records for 22,068 patients with chronic illnesses who died between 2010 and 2015. We examined utilization overall and stratified by age using multiple regression. Results The proportion of deaths at home did not change, but hospital admissions in the last 30 days of life decreased significantly from 2010 to 2015 (hospital b = −0.026; CI = −0.041, −0.012). ICU admissions in the last 30 days also declined over time for the full sample and for patients aged 65 years or older (overall b = −0.023; CI = −0.039, −0.007), but was not significant for younger decedents. Length of stay (LOS) did not decrease for those using the hospital or ICU. Conclusion From 2010 to 2015, we observed a decrease in hospital admissions for all age groups and in ICU admissions for those over 65 years. As there were no changes in the proportion of patients with chronic illness who died at home nor in hospital or ICU LOS in the last 30 days, hospital and ICU admissions in the last 30 days may be a more responsive quality metric than site of death or LOS for palliative care interventions.

Background: A landscape analysis of mobile health (mHealth) applications and published literature related to their use in surgical site infection (SSI) detection and surveillance was conducted by the Assessing Surgical Site Infection Surveillance Technologies (ASSIST) investigators. Methods: The literature review focused on post-discharge SSI detection or tracking by caregivers or patients using mHealth technology. This report is unique in its review across both commercial and research-based mHealth apps. Apps designed for long-term wound tracking and those focused on care coordination and scheduling were excluded. A structured evaluation framework was used to assess the operational, technical, and policy features of the apps. Results: Of the 10 apps evaluated, only two were in full clinical use. A variety of data were captured by the apps including wound photographs (eight apps), wound measurements (three apps), dressing assessments (two apps), physical activity metrics (three apps), medication adherence (three apps) as well as structured surveys, signs, and symptoms. Free-text responses were permitted by at least two apps. The extent of integration with the native electronic health record system was variable. Conclusion: The examination of rapidly evolving technologies is challenged by lack of standard evaluative methods, such as those more commonly used in clinical research. This review is unique in its application of a structured evaluation framework across both commercial and research-based mHealth apps.

Background: The implementation of health information technology interventions is at the forefront of most hospital institutional policy agendas. Despite the availability of numerous apps a...

Background: The patient's history of present illness provides an important part of the data with which clinicians diagnose and treat. Once surgical patients are discharged, the ability to incorporate direct observation requires coordinating patient and provider for a clinical visit. Mobile technologies offer the ability to gather and organize the patient's history, supplement that history with photographs and other clinical observations, and convey those data accurately and rapidly to the entire clinical team. Methods: We review our experience with patient-generated health data in surgical site infection, draw parallels with similar work in other domains, and identify principles we have found useful. Results: Health information system implementations require substantial changes in provider workflow. Shared expectations between the patient and the surgical team, an incremental approach to change in clinical processes, and an emphasis on clinical utility all support successful implementation. Conclusions: The data collection and rapid information exchange afforded by monitoring post-operative, post-discharge patients using mobile technologies can support the expectations of both patients and providers and may provide a novel data source for public health surveillance of surgical site infection. Both uses of these data require careful attention to introducing changes in clinical workflow.

Background Persons with human immunodeficiency virus (HIV) have higher risks for myocardial infarction (MI) than the general population. This is driven in part by higher type 2 MI (T2MI, due to coronary supply-demand mismatch) rates among persons with HIV (PWH). In the general population, T2MI has higher mortality than type 1 MI (T1MI, spontaneous and generally due to plaque rupture and thrombosis). PWH have a greater burden of comorbidities and may therefore have an even greater excess risk for complication and death in the setting of T2MI. However, mortality patterns after T1MI and T2MI in HIV are unknown. Methods We analyzed mortality after MI among PWH enrolled in the multicenter, US-based Centers for AIDS Research Network of Integrated Clinical Systems (CNICS) cohort (N = 28,186). Incident MIs occurring between January 1, 1996, and December 31, 2014, were centrally adjudicated and classified as T1MI or T2MI. We first compared mortality following T1MI vs. T2MI among PWH. Cox survival analyses and Bayesian model averaging were then used to evaluate pre-MI covariates associated with mortality following T1MI and T2MI. Results Among the 596 out of 28,186 PWH who experienced MI (2.1%; 293 T1MI and 303 T2MI), mortality rates were significantly greater after T2MI (22.2/100 person-years; 1-, 3-, and 5-year mortality 39%, 52%, and 62%) than T1MI (8.2/100 person-years; 1-, 3-, and 5-year mortality 15%, 22%, and 30%). Significant mortality predictors after T1MI were higher HIV viral load, renal dysfunction, and older age. Significant predictors of mortality after T2MI were low body-mass index (BMI) and detectable HIV viral load. Conclusions Mortality is high following MI for PWH and substantially greater after T2MI than T1MI. Predictors of death after MI differed by type of MI, reinforcing the different clinical scenarios associated with each MI type and the importance of considering MI types separately. Electronic supplementary material The online version of this article (10.1186/s12916-019-1385-7) contains supplementary material, which is available to authorized users.

Objective: To evaluate the association between the number of chronic conditions and hospital utilization at the end of life. Background: An understanding of the association of multimorbidi...

BackgroundResearch is needed to better understand relations between immunosuppression and HIV viraemia and risk for non-Hodgkin lymphoma, a common cancer in people living with HIV. We aimed to identify key CD4 count and HIV RNA (viral load) predictors of risk for non-Hodgkin lymphoma, overall and by subtype.MethodsWe studied people living with HIV during 1996-2014 from 21 Canadian and US cohorts participating in the North American AIDS Cohort Collaboration on Research and Design. To determine key independent predictors of risk for non-Hodgkin lymphoma, we assessed associations with time-updated recent, past, cumulative, and nadir or peak measures of CD4 count and viral load, using demographics-adjusted, cohort-stratified Cox models, and we compared models using Akaike's information criterion.FindingsOf 102 131 people living with HIV during the study period, 712 people developed non-Hodgkin lymphoma. The key independent predictors of risk for overall non-Hodgkin lymphoma were recent CD4 count (ie, lagged by 6 months

Background At-risk alcohol use is important to identify in clinical settings to facilitate interventions. The Patient-Reported Outcomes Measurement Information System (PROMIS) Alcohol Use Short Form was developed through an item response theory process, but its utility as a screening instrument in clinical care has not been reported. Objective To determine the ability of the PROMIS Alcohol Use Short Form to identify people with current or future at-risk alcohol use defined by the Alcohol Use Disorders Identification Test consumption (AUDIT-C) instrument. Methods Observational study of people living with HIV (PLWH) in clinical care at four sites across the US. Patients completed a tablet-based clinical assessment prior to seeing their providers at clinic appointments. We used 3 definitions of clinically-relevant at-risk alcohol use and determined the proportion of PLWH with current or future at-risk drinking identified by the PROMIS instrument. Results Of 2497 PLWH who endorsed ≥1 drink in the prior 12 months, 1500 PLWH (60%) endorsed “never” for all PROMIS items. In that group, 26% had clinically-relevant at-risk alcohol use defined by one or more AUDIT-C definitions. At follow-up (N = 1608), high baseline PROMIS scores had 55% sensitivity for at-risk drinking among those with at-risk drinking at baseline, and 22% sensitivity among those without baseline risk. Conclusions The PROMIS Alcohol Use Short Form cannot be used alone to identify PLWH with clinically-relevant at-risk alcohol use. Optimal assessment of problem drinking behavior is not clear, but there does not seem to be an important role for the PROMIS instrument in this clinical setting.

Importance Patients with chronic illness frequently use Physician Orders for Life-Sustaining Treatment (POLST) to document treatment limitations. Objectives To evaluate the association between POLST order for medical interventions and intensive care unit (ICU) admission for patients hospitalized near the end of life. Design, Setting, and Participants Retrospective cohort study of patients with POLSTs and with chronic illness who died between January 1, 2010, and December 31, 2017, and were hospitalized 6 months or less before death in a 2-hospital academic health care system. Exposures POLST order for medical interventions (“comfort measures only” vs “limited additional interventions” vs “full treatment”), age, race/ethnicity, education, days from POLST completion to admission, histories of cancer or dementia, and admission for traumatic injury. Main Outcomes and Measures The primary outcome was the association between POLST order and ICU admission during the last hospitalization of life; the secondary outcome was receipt of a composite of 4 life-sustaining treatments: mechanical ventilation, vasopressors, dialysis, and cardiopulmonary resuscitation. For evaluating factors associated with POLST-discordant care, the outcome was ICU admission contrary to POLST order for medical interventions during the last hospitalization of life. Results Among 1818 decedents (mean age, 70.8 [SD, 14.7] years; 41% women), 401 (22%) had POLST orders for comfort measures only, 761 (42%) had orders for limited additional interventions, and 656 (36%) had orders for full treatment. ICU admissions occurred in 31% (95% CI, 26%-35%) of patients with comfort-only orders, 46% (95% CI, 42%-49%) with limited-interventions orders, and 62% (95% CI, 58%-66%) with full-treatment orders. One or more life-sustaining treatments were delivered to 14% (95% CI, 11%-17%) of patients with comfort-only orders and to 20% (95% CI, 17%-23%) of patients with limited-interventions orders. Compared with patients with full-treatment POLSTs, those with comfort-only and limited-interventions orders were significantly less likely to receive ICU admission (comfort only: 123/401 [31%] vs 406/656 [62%], aRR, 0.53 [95% CI, 0.45-0.62]; limited interventions: 349/761 [46%] vs 406/656 [62%], aRR, 0.79 [95% CI, 0.71-0.87]). Across patients with comfort-only and limited-interventions POLSTs, 38% (95% CI, 35%-40%) received POLST-discordant care. Patients with cancer were significantly less likely to receive POLST-discordant care than those without cancer (comfort only: 41/181 [23%] vs 80/220 [36%], aRR, 0.60 [95% CI, 0.43-0.85]; limited interventions: 100/321 [31%] vs 215/440 [49%], aRR, 0.63 [95% CI, 0.51-0.78]). Patients with dementia and comfort-only orders were significantly less likely to receive POLST-discordant care than those without dementia (23/111 [21%] vs 98/290 [34%], aRR, 0.44 [95% CI, 0.29-0.67]). Patients admitted for traumatic injury were significantly more likely to receive POLST-discordant care (comfort only: 29/64 [45%] vs 92/337 [27%], aRR, 1.52 [95% CI, 1.08-2.14]; limited interventions: 51/91 [56%] vs 264/670 [39%], aRR, 1.36 [95% CI, 1.09-1.68]). In patients with limited-interventions orders, older age was significantly associated with less POLST-discordant care (aRR, 0.93 per 10 years [95% CI, 0.88-1.00]). Conclusions and Relevance Among patients with POLSTs and with chronic life-limiting illness who were hospitalized within 6 months of death, treatment-limiting POLSTs were significantly associated with lower rates of ICU admission compared with full-treatment POLSTs. However, 38% of patients with treatment-limiting POLSTs received intensive care that was potentially discordant with their POLST.

IMPORTANCE: Surgeons are increasingly interested in using mobile and online applications with wound photography to monitor patients after surgery. Early work using remote care to diagnose surgical site infections (SSIs) demonstrated improved diagnostic accuracy using wound photographs to augment patients’ electronic reports of symptoms, but it is unclear whether these findings are reproducible in real-world practice. OBJECTIVE: To determine how wound photography affects surgeons’ abilities to diagnose SSIs in a pragmatic setting. DESIGN, SETTING, AND PARTICIPANTS: This prospective study compared surgeons’ paired assessments of postabdominal surgery case vignettes with vs without wound photography for detection of SSIs. Data for case vignettes were collected prospectively from May 1, 2007, to January 31, 2009, at Erasmus University Medical Center, Rotterdam, the Netherlands, and from July 1, 2015, to February 29, 2016, at Vanderbilt University Medical Center, Nashville, Tennessee. The surgeons were members of the American Medical Association whose self-designated specialty is general, abdominal, colorectal, oncologic, or vascular surgery and who completed internet-based assessments from May 21 to June 10, 2016. INTERVENTION: Surgeons reviewed online clinical vignettes with or without wound photography. MAIN OUTCOMES AND MEASURES: Surgeons’ diagnostic accuracy, sensitivity, specificity, confidence, and proposed management with respect to SSIs. RESULTS: A total of 523 surgeons (113 women and 410 men; mean [SD] age, 53 [10] years) completed a mean of 2.9 clinical vignettes. For the diagnosis of SSIs, the addition of wound photography did not change accuracy (863 of 1512 [57.1%] without and 878 of 1512 [58.1%] with photographs). Photographs decreased sensitivity (from 0.58 to 0.50) but increased specificity (from 0.56 to 0.63). In 415 of 1512 cases (27.4%), the addition of wound photography changed the surgeons’ assessment (215 of 1512 [14.2%] changed from incorrect to correct and 200 of 1512 [13.2%] changed from correct to incorrect). Surgeons reported greater confidence when vignettes included a wound photograph compared with vignettes without a wound photograph, regardless of whether they correctly identified an SSI (median, 8 [interquartile range, 6-9] vs median, 8 [interquartile range, 7-9]; P

Catheter-associated urinary tract infection (CAUTI) is a common and costly healthcare-associated infection, yet measuring it accurately is challenging and resource-intensive. Electronic surveillance promises to make this task more objective and efficient in an era of new financial and regulatory imperatives, but previous surveillance approaches have used a simplified version of the definition. We applied a complete definition, including subjective elements identified through natural language processing of clinical notes. Through examination of documentation practices, we defined a set of rules that identified positively and negatively asserted symptoms of CAUTI. Our algorithm was developed on a training set of 1421 catheterizedpatients and prospectively validated on 1567 catheterizedpatients. Compared to gold standard chart review, our tool had a sensitivity of 97.1%, specificity of 94.5% PPV of 66.7% and NPV of 99.6% for identifying CAUTI. We discuss sources of error and suggestions for more computable future definitions.

As our population ages and the burden of chronic illness rises, there is increasing need to implement quality metrics that measure and benchmark care of the seriously ill, including the delivery of both primary care and specialty palliative care. Such metrics can be used to drive quality improvement, value-based payment, and accountability for population-based outcomes.In this article, we examine use of the electronic health record (EHR) as a tool to assess quality of serious illness care through narrative review and description of a palliative care quality metrics program in a large healthcare system.In the search for feasible, reliable, and valid palliative care quality metrics, the EHR is an attractive option for collecting quality data on large numbers of seriously ill patients. However, important challenges to using EHR data for quality improvement and accountability exist, including understanding the validity, reliability, and completeness of the data, as well as acknowledging the difference between care documented and care delivered. Challenges also include developing achievable metrics that are clearly linked to patient and family outcomes and addressing data interoperability across sites as well as EHR platforms and vendors. This article summarizes the strengths and weakness of the EHR as a data source for accountability of community- and population-based programs for serious illness, describes the implementation of EHR data in the palliative care quality metrics program at the University of Washington, and, based on that experience, discusses opportunities and challenges. Our palliative care metrics program was designed to serve as a resource for other healthcare systems.Although the EHR offers great promise for enhancing quality of care provided for the seriously ill, significant challenges remain to operationalizing this promise on a national scale and using EHR data for population-based quality and accountability.

The objective of this study was to develop and pilot test a chronic pain empowerment and self-management platform, derived from acceptance and commitment therapy, in a pain specialty setting. A controlled, sequential, nonrandomized study design was used to accommodate intervention development and to test the efficacy of the PainTracker Self-Manager (PTSM) intervention (Web-based educational modules and outcome tracking combined with tailored patient coaching sessions and provider guidance). Generalized estimating equations evaluated changes over time (baseline, 3 months, 6 months) in pain self-efficacy (primary outcome), chronic pain acceptance (activity engagement and pain willingness), perceived efficacy in patient–provider interactions, pain intensity and interference, and overall satisfaction with pain treatment (secondary outcomes) between intervention (n = 48) and usual care control groups (n = 51). The full study sample (N = 99) showed greater improvements over time (significant Group × Time interactions) in pain self-efficacy and satisfaction with pain treatment. Among study completers (n = 82), greater improvement in activity engagement as well as pain intensity and interference were also observed. These preliminary findings support the efficacy of the PTSM intervention in a pain specialty setting. Further research is needed to refine and expand the PTSM intervention and to test it in a randomized trial in primary care settings. Perspective We developed a Web-based patient empowerment platform that combined acceptance and commitment therapy-based educational modules and tailored coaching sessions with longitudinal tracking of treatments and patient-reported outcomes, named PTSM. Pilot controlled trial results provide preliminary support for its efficacy in improving pain self-efficacy, activity engagement, pain intensity and interference, and satisfaction with pain treatment.

BACKGROUND: Patient-provider sexual risk behavior discussions occur infrequently but may be facilitated by high-quality sexual risk screening tools. OBJECTIVE: To develop the Sexual Risk Behavior Inventory (SRBI), a brief computer-administered patient-reported measure. DESIGN: Qualitative item development/quantitative instrument validation. PARTICIPANTS: We developed SRBI items based on patient interviews (n = 128) at four geographically diverse US primary care clinics. Patients were diverse in gender identity, sex, sexual orientation, age, race/ethnicity, and HIV status. We compared sexual risk behavior identified by the SRBI and the Risk Assessment Battery (RAB) among patients (n = 422). APPROACH: We constructed an item pool based on validated measures of sexual risk, developed an in-depth interview guide based on pool content, and used interviews to elicit new sexual risk concepts. We coded concepts, matched them to item pool content, and developed new content where needed. A provider team evaluated item clinical relevance. We conducted cognitive interviews to assess item comprehensibility. We administered the SRBI and the RAB to patients. KEY RESULTS: Common, clinically relevant concepts in the SRBI included number of sex partners; partner HIV status; partner use of antiretroviral medication (ART)/pre-exposure prophylaxis (PrEP); and recent sex without barrier protection, direction of anal sex, and concern regarding HIV/STI exposure. While 90% reported inconsistent condom use on the RAB, same-day SRBI administration revealed that for over one third, all their partners were on ART/PrEP. CONCLUSION: The SRBI is a brief, skip-patterned, clinically relevant measure that ascertains sexual risk behavior across sex, sexual orientation, gender identity, partner HIV serostatus, and partner treatment status, furnishing providers with context to determine gradations of risk for HIV/STI.

We compared same-day provider medical record documentation and interventions addressing depression and risk behaviors before and after delivering point-of-care patient-reported outcomes (PROs) feedback for patients who self-reported clinically relevant levels of depression or risk behaviors. During the study period (1 January 2006–15 October 2010), 2289 PRO assessments were completed by HIV-infected patients. Comparing the 8 months before versus after feedback implementation, providers were more likely to document depression (74% before vs. 87% after feedback, p = 0.02) in patients with moderate-to-severe depression (n = 317 assessments), at-risk alcohol use (41 vs. 64%, p = 0.04, n = 155) and substance use (60 vs. 80%, p = 0.004, n = 212). Providers were less likely to incorrectly document good adherence among patients with inadequate adherence after feedback (42 vs. 24%, p = 0.02, n = 205). While PRO feedback of depression and adherence were followed by increased provider intervention, other domains were not. Further investigation of factors associated with the gap between awareness and intervention are needed in order to bridge this divide.

Phone-based unannounced pill counts to measure medication adherence are much more practical and less expensive than home-based unannounced pill counts, but their validity has not been widely assessed. We examined the validity of phone versus home-based pill counts using a simplified protocol streamlined for studies embedded in clinical care settings. A total of 100 paired counts were used to compare concordance between unannounced phone and home-based pill counts using interclass correlations. Discrepancy analyses using χ(2) tests compared demographic and clinical characteristics across patients who were concordant between phone and home-based pill counts and patients who were not concordant. Concordance was high for phone-based and home-based unannounced total pill counts, as well as individual medication counts and calculated adherence. This study demonstrates that a simplified phone-based pill count protocol can be implemented among patients from a routine clinical care setting and is a feasible means of monitoring medication adherence.

In this paper, we develop a novel formulation for contemporaneous patient risk monitoring by exploiting the emerging data-rich environment in many healthcare applications, where an abundance of longitudinal data that reflect the degeneration of the health condition can be continuously collected. Our objective, and the developed formulation, is fundamentally different from many existing risk score models for different healthcare applications, which mostly focus on predicting the likelihood of a certain outcome at a pre-specified time. Rather, our formulation translates multivariate longitudinal measurements into a contemporaneous health index (CHI) that captures patient condition changes over the course of progression. Another significant feature of our formulation is that, CHI can be estimated with or without label information, different from other risk score models strictly based on supervised learning. To develop this formulation, we focus on the degenerative disease conditions, for which we could utilize the monotonic progression characteristic (either towards disease or recovery) to learn CHI. Such a domain knowledge leads us to a novel learning formulation, and on top of that, we further generalize this formulation with a capacity to incorporate label information if available. We further develop algorithms to mitigate the challenges associated with the nonsmooth convex optimization problem by first identifying its dual reformulation as a constrained smooth optimization problem, and then, using the block coordinate descent algorithm to iteratively solve the optimization with a derived efficient projection at each iteration. Extensive numerical studies are performed on both synthetic datasets and real-world applications on Alzheimer's disease and Surgical Site Infection, which demonstrate the utility and efficacy of the proposed method on degenerative conditions that include a wide range of applications.

Little is known about psychiatric illness and utilization of end-of-life care.We hypothesized that preexisting psychiatric illness would increase hospital utilization at end of life among patients with chronic medical illness due to increased severity of illness and care fragmentation.We reviewed electronic health records to identify decedents with one or more of eight chronic medical conditions based on International Classification of Diseases-9 codes. We used International Classification of Diseases-9 codes and prescription information to identify preexisting psychiatric illness. Regression models compared hospital utilization among patients with and without psychiatric illness. Path analyses examined the effect of severity of illness and care fragmentation.Eleven percent of 16,214 patients with medical illness had preexisting psychiatric illness, which was associated with increased risk of death in nursing homes (P = 0.002) and decreased risk of death in hospitals (P 0.001). In the last 30 days of life, psychiatric illness was associated with reduced inpatient and intensive care unit utilization but increased emergency department utilization. Path analyses confirmed an association between psychiatric illness and increased hospital utilization mediated by severity of illness and care fragmentation, but a stronger direct effect of psychiatric illness decreasing hospitalizations.Our findings differ from the increased hospital utilization for patients with psychiatric illness in circumstances other than end-of-life care. Path analyses confirmed hypothesized associations between psychiatric illness and increased utilization mediated by severity of illness and care fragmentation but identified more powerful direct effects decreasing hospital use. Further investigation should examine whether this effect represents a disparity in access to preferred care.