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Visual field testing that is not frequent enough results in delayed identification of open-angle glaucoma (OAG) progression. Guidelines recommend at least annual testing. It is not known how frequently patients with OAG across the United States receive visual field testing and how patient characteristics and circumstances influence this frequency. If US patients with OAG do not receive visual field tests frequently enough, interventions to increase this frequency or to develop other forms of testing visual function may reduce unidentified OAG vision loss.Retrospective cohort study.The TruvenHealth MarketScan Commercial Claims Database (IBM) contains demographic and claims data for160 million individuals across the United States from 2008 to 2017. We identified enrollees in the database with a recorded diagnosis of OAG (International Classification of Diseases, Ninth Revision, Clinical Modification and International Classification of Diseases, Tenth Revision, Clinical Modification codes 356.1x and H40.1x, respectively). We excluded those aged40 years at the time of their first OAG diagnosis, those without at least 1 confirmatory OAG diagnosis at a subsequent visit, and those with4 years of follow-up data after OAG diagnosis.We calculated the number of visual field tests that each enrollee with OAG underwent per year and categorized the enrollees based on that number (0,0 to0.9, ≥ 0.9 to ≤ 1.1,1.1 to ≤ 2.1, and2.1). We used negative binomial regression to investigate the demographic or health variables that were associated with the frequency of visual field tests that enrollees with OAG received.Frequency of visual field testing among enrollees with OAG.Of the 380 029 enrollees included in the study, 33 267 (8.8%) did not receive a visual field test during the study period, 259 349 (68.2%) underwent0 to0.9 visual field tests per year, 42 129 (11.1%) underwent ≥ 0.9 to ≤ 1.1 visual field tests per year, 42 301 (11.1%) underwent1.1 to ≤ 2.1 visual field tests per year, and 2983 (0.8%) underwent ≥ 2.1 visual field tests per year. The median number of visual field tests per year was 0.63 (interquartile range, 0.33-0.88; mean, 0.65).More than 75% of enrollees with OAG received1 visual field test per year and, thus, did not receive guideline-adherent glaucoma monitoring.

Family history is an important tool for assessing disease risk, and tailoring recommendations for screening and genetic services referral. This study explored barriers to family history collection with Spanish-speaking patients.This qualitative study was conducted in two US healthcare systems. We conducted semi-structured interviews with medical assistants, physicians, and interpreters with experience collecting family history for Spanish-speaking patients.The most common patient-level barrier was the perception that some Spanish-speaking patients had limited knowledge of family history. Interpersonal communication barriers related to dialectical differences and decisions about using formal interpreters vs. Spanish-speaking staff. Organizational barriers included time pressures related to using interpreters, and ad hoc workflow adaptations for Spanish-speaking patients that might leave gaps in family history collection.This study identified multi-level barriers to family history collection with Spanish-speaking patients in primary care. Findings suggest that a key priority to enhance communication would be to standardize processes for working with interpreters.To improve communication with and care provided to Spanish-speaking patients, there is a need to increase healthcare provider awareness about implicit bias, to address ad hoc workflow adjustments within practice settings, to evaluate the need for professional interpreter services, and to improve digital tools to facilitate family history collection.

Despite advances in interoperability standards, it remains challenging and often costly to share clinical decision support (CDS) across healthcare organizations. This is due in part to limited coordination among CDS components. To improve coordination of CDS components, Health Level 7 (HL7) has developed a suite of interoperability standards with Fast Health Interoperability Resources (FHIR) specification as a common information model. Evidence is needed to determine the feasibility of implementing these CDS components; therefore, the objective of this study was to investigate the coordination of emerging HL7 standards with modular CDS architecture components.We used a modular, standards-based architecture consisting of four components: data, logic, services, and applications. The implementation use-case was an application to support shared decision making in the context of drug-drug interactions (DDInteract).DDInteract uses FHIR as the data representation model, Clinical Quality Language for logic representation, CDS Hooks for the services layer, and Substitutable Medical Apps Reusable Technologies for application integration. DDInteract was first implemented in a sandbox environment and then in an electronic health record (Epic®) test environment. DDInteract can be integrated in clinical workflows through on-demand access from a menu or through CDS Hooks upon opening a patient's record or placing a medication order.In the context of drug interactions, DDInteract is the first application to leverage a full stack of emerging interoperability standards for each component of modular CDS architecture. The demonstrated feasibility of interoperable components can be generalized to other modular CDS applications.

Electronic health record (EHR) data are increasingly used to develop prediction models to support clinical care, including the care of patients with common chronic conditions. A key challenge for individual healthcare systems in developing such models is that they may not be able to achieve the desired degree of robustness using only their own data. A potential solution-combining data from multiple sources-faces barriers such as the need for data normalization and concerns about sharing patient information across institutions. To address these challenges, we evaluated three alternative approaches to using EHR data from multiple healthcare systems in predicting the outcome of pharmacotherapy for type 2 diabetes mellitus(T2DM). Two of the three approaches, named Selecting Better (SB) and Weighted Average(WA), allowed the data to remain within institutional boundaries by using pre-built prediction models; the third, named Combining Data (CD), aggregated raw patient data into a single dataset. The prediction performance and prediction coverage of the resulting models were compared to single-institution models to help judge the relative value of adding external data and to determine the best method to generate optimal models for clinical decision support. The results showed that models using WA and CD achieved higher prediction performance than single-institution models for common treatment patterns. CD outperformed the other two approaches in prediction coverage, which we defined as the number of treatment patterns predicted with an Area Under Curve of 0.70 or more. We concluded that 1) WA is an effective option for improving prediction performance for common treatment patterns when data cannot be shared across institutional boundaries and 2) CD is the most effective approach when such sharing is possible, especially for increasing the range of treatment patterns that can be predicted to support clinical decision making.

Reports of low-value prostate-specific antigen (PSA) testing (testing in which the harms outweigh the benefits) generally employ population level data sources. While such results may be generalizable, they often lack the detail necessary to understand provider clinical decision making and guideline concordance. Using a retrospective study of PSA testing at our institution we intend to characterize the frequency and patterns associated with low-value PSA testing.We leveraged the electronic health record to determine guideline-defined low-value testing in our health system from 07/01/2012 to 06/30/2017. Secondarily, we measured the between-testing interval for repeat tests and the rates of prostate cancer risk factors and comorbidities among men receiving screening.Overall, 21,145 PSA tests were performed on 12,303 men. The rate of low-value testing ranged from 23.4 to 56.8%, depending upon the specific guideline. For repeat tests, the median between-testing interval was 12.6 months. Risk factors for prostate cancer were uncommon, but more frequent in men age55 years compared to men age 55-69 years (17.6% vs. 13.5%, p 0.001). Screened older men (age70 years) were more likely to have a Charlson Comorbidity Index ≥ 3, compared to the 55-69 reference group (31.4% vs. 17.3%, p 0.001).Low-value prostate cancer testing is prevalent. Between-testing intervals were often times shorter than recommended. Screening among younger men was frequent despite low rates of risk factors. High rates of comorbidity may limit life expectancy among older men receiving screening. These findings highlight the need for improved guidance with prostate cancer screening.

Background Clinical decision support (CDS) systems can help investigators use best practices when responding to outbreaks, but variation in guidelines between jurisdictions can make such systems hard to develop and implement. This study aimed to identify (1) the extent to which state-level guidelines adhere to national recommendations for norovirus outbreak response in health care settings and (2) the impact of variation between states on outbreak outcomes. Methods State guidelines were obtained from Internet searches and direct contact with state public health officials in early 2016. Outcomes from norovirus outbreaks that occurred in 2015 were compared using data from the National Outbreak Reporting System. Results Guidelines were obtained from 41 of 45 (91%) state health departments that responded to queries or had guidelines available on their Web sites. Most state guidelines addressed each of the national recommendations, but specific guidance varied considerably. For example, among 36 states with guidance on numbers of stool specimens to collect, there were 21 different recommendations. Furthermore, having guidelines consistent with national recommendations was associated with fewer outbreaks reported and more outbreaks with confirmed etiology. Conclusions This study identified substantial variation in state health care–associated norovirus outbreak response guidelines, which must be considered when developing related CDS systems. More research is needed to understand why this variation exists, how it impacts outbreak outcomes, and where improvements in evidence-based recommendations and communication of national guidance are needed.

PURPOSE: Studies suggest that “on-demand” radiography is equivalent to daily routine with regard to adverse events. In these studies, provider behavior is controlled. Pragmatic implementation has not been studied. MATERIALS AND METHODS: This was a quasi-experimental, pre-post intervention study. Medical directors of two intervention ICUs requested pCXRs be ordered on an on-demand basis at one time point, without controlling or monitoring behavior or providing follow-up. RESULTS: A total of 11,994 patient days over 18 months were included. Combined characteristics: Age: 56.7, 66% male, 96% survival, APACHE II 14 (IQR: 11–19), mechanical ventilation (MV) (occurrences)/patient admission: mean 0.7 (SD: 0.6; range: 0–5), duration (hours) of MV: 21.7 (IQR: 9.8–81.4) and ICU LOS (days): 2.8 (IQR: 1.8–5.6). Average pCXR rate/patient/day before was 0.93 (95% CI: 0.89–0.96), and 0.73 (95% CI: 0.69–0.77) after. Controlling for severity, daily pCXR rate decreased by 21.7% (p < 0.001), then increased by about 3%/month (p = 0.044). There was no change in APACHE II, mortality, and occurrences or duration of MV, unplanned re-intubations, ICU LOS. CONCLUSIONS: In critically ill adults, pCXR reduction can be achieved in cardiothoracic and trauma/surgical patients with a pragmatic intervention, without adversely affecting patient care, outside a controlled study.

Computer-based clinical decision support (CDS) has been pursued for more than five decades. Despite notable accomplishments and successes, wide adoption and broad use of CDS in clinical practice has not been achieved. Many issues have been identified as being partially responsible for the relatively slow adoption and lack of impact, including deficiencies in leadership, recognition of purpose, understanding of human interaction and workflow implications of CDS, cognitive models of the role of CDS, and proprietary implementations with limited interoperability and sharing. To address limitations, many approaches have been proposed and evaluated, drawing on theoretical frameworks, as well as management, technical and other disciplines and experiences. It seems clear, because of the multiple perspectives involved, that no single model or framework is adequate to encompass these challenges. This Viewpoint paper seeks to review the various foci of CDS and to identify aspects in which theoretical models and frameworks for CDS have been explored or could be explored and where they might be expected to be most useful.

Highlights • CEDI research has the potential to improve health inequities. • QuitSMART Utah used a multi-component community engagement process. • The partnership had shared goals, resources, data, and mutual respect of expertise., Community engagement is critical to accelerate and improve implementation of evidence-based interventions to reduce health inequities. Community-engaged dissemination and implementation research (CEDI) emphasizes engaging stakeholders (e.g., community members, practitioners, community organizations, etc.) with diverse perspectives, experience, and expertise to provide tacit community knowledge regarding the local context, priorities, needs, and assets. Importantly, CEDI can help improve health inequities through incorporating unique perspectives from communities experiencing health inequities that have historically been left out of the research process. The community-engagement process that exists in practice can be highly variable, and characteristics of the process are often underreported, making it difficult to discern how engagement of community partners was used to improve implementation. This paper describes the community-engagement process for a multilevel, pragmatic randomized trial to increase the reach and impact of evidence-based tobacco cessation treatment among Community Health Center patients; describes how engagement activities and the resulting partnership informed the development of implementation strategies and improved the research process; and presents lessons learned to inform future CEDI research.

To determine, as part of our Utah Newborn Nursery Bilirubin Management Program, whether end-tidal carbon monoxide concentration (ETCOc) measurements in all newborns in our nursery receiving phototherapy were associated with outcomes related to the management of hyperbilirubinemia, including time (hours after birth) when phototherapy was initiated, total duration of phototherapy during the nursery stay, repeat phototherapy treatments, and hospital readmission for phototherapy.We performed a planned interim analysis of a component of our program in which we measured ETCOc noninvasively using CoSense on each newborn in our nursery receiving phototherapy and recorded specific outcomes related to phototherapy management.Of 1856 newborns admitted to our nursery in a 6-month period in 2020, 170 (9.8%) were treated with phototherapy. An ETCOc reading was successfully obtained in 145 of 151 attempts (96%). Higher ETCOc values were associated with earlier institution of phototherapy and longer duration of phototherapy. For every 1-ppm increase in ETCOc, phototherapy was started 9 hours earlier (95% CI, 3.3-14.8; P = .002) and was administered for an additional 9.3 hours (95% CI, 4.1-14.6; P .001). Three newborns were readmitted to the hospital for intensive phototherapy; while in the nursery, all 3 had an elevated ETCOc (2.2, 2.6, and 2.9 ppm).Our findings provide answers to questions raised in the 2004 American Academy of Pediatrics bilirubin guidelines. In our neonatal nursery, measuring ETCOc in all phototherapy recipients was feasible and safe, and the results were associated with multiple aspects of phototherapy management. Higher ETCOc values predicted earlier and longer phototherapy courses.

Display Omitted A single-reviewer validation process can be used in an operational setting.Detailed electronic results increase accuracy and reduces expense of chart review.We recommend using a mix of phenotype validation strategies. ObjectiveDevelop evidence-based recommendations for single-reviewer validation of electronic phenotyping results in operational settings. Material and methodsWe conducted a randomized controlled study to evaluate whether electronic phenotyping results should be used to support manual chart review during single-reviewer electronic phenotyping validation (N=3104). We evaluated the accuracy, duration and cost of manual chart review with and without the availability of electronic phenotyping results, including relevant patient-specific details. The cost of identification of an erroneous electronic phenotyping result was calculated based on the personnel time required for the initial chart review and subsequent adjudication of discrepancies between manual chart review results and electronic phenotype determinations. ResultsProviding electronic phenotyping results (vs not providing those results) was associated with improved overall accuracy of manual chart review (98.90% vs 92.46%, p

We are increasingly moving towards precision and personalized medicine for spine surgery. Predictive models have been developed to assist clinicians in predicting a patient's survival, discharge disposition and infection following surgery. However, the developed algorithms generally require the use of stand-alone tools that can be time-consuming and cumbersome to use. Incorporating these algorithms into the electronic health record and clinical workflows will therefore be critical for unlocking these algorithms’ potential. This review summarizes the current state of predictive algorithms in spine surgery and proposes a way forward for optimally integrating them into clinical care. The proposed improvements could also be translated to other subspecialties in orthopedics and neurosurgery.

Display Omitted There is a need to promote development of resources for genomic CDS.The proposed sandbox will be available pre-configured with CDS and genome tools.We present survey results to assess needs for a genomic CDS sandbox.Results show strong interest for a sandbox to test CDS and genome case studies.Use of genomic CDS sandbox will enhance genomically-guided medicine. Genomics is a promising tool that is becoming more widely available to improve the care and treatment of individuals. While there is much assertion, genomics will most certainly require the use of clinical decision support (CDS) to be fully realized in the routine clinical setting. The National Human Genome Research Institute (NHGRI) of the National Institutes of Health recently convened an in-person, multi-day meeting on this topic. It was widely recognized that there is a need to promote the innovation and development of resources for genomic CDS such as a CDS sandbox. The purpose of this study was to evaluate a proposed approach for such a genomic CDS sandbox among domain experts and potential users. Survey results indicate a significant interest and desire for a genomic CDS sandbox environment among domain experts. These results will be used to guide the development of a genomic CDS sandbox.

To develop an effective and scalable individual-level patient cost prediction method by automatically learning hidden temporal patterns from multivariate time series data in patient insurance claims using a convolutional neural network (CNN) architecture.We used three years of medical and pharmacy claims data from 2013 to 2016 from a healthcare insurer, where data from the first two years were used to build the model to predict costs in the third year. The data consisted of the multivariate time series of cost, visit and medical features that were shaped as images of patients' health status (i.e., matrices with time windows on one dimension and the medical, visit and cost features on the other dimension). Patients' multivariate time series images were given to a CNN method with a proposed architecture. After hyper-parameter tuning, the proposed architecture consisted of three building blocks of convolution and pooling layers with an LReLU activation function and a customized kernel size at each layer for healthcare data. The proposed CNN learned temporal patterns became inputs to a fully connected layer. We benchmarked the proposed method against three other methods: (1) a spike temporal pattern detection method, as the most accurate method for healthcare cost prediction described to date in the literature; (2) a symbolic temporal pattern detection method, as the most common approach for leveraging healthcare temporal data; and (3) the most commonly used CNN architectures for image pattern detection (i.e., AlexNet, VGGNet and ResNet) (via transfer learning). Moreover, we assessed the contribution of each type of data (i.e., cost, visit and medical). Finally, we externally validated the proposed method against a separate cohort of patients. All prediction performances were measured in terms of mean absolute percentage error (MAPE).The proposed CNN configuration outperformed the spike temporal pattern detection and symbolic temporal pattern detection methods with a MAPE of 1.67 versus 2.02 and 3.66, respectively (p 0.01). The proposed CNN outperformed ResNet, AlexNet and VGGNet with MAPEs of 4.59, 4.85 and 5.06, respectively (p 0.01). Removing medical, visit and cost features resulted in MAPEs of 1.98, 1.91 and 2.04, respectively (p 0.01).Feature learning through the proposed CNN configuration significantly improved individual-level healthcare cost prediction. The proposed CNN was able to outperform temporal pattern detection methods that look for a pre-defined set of pattern shapes, since it is capable of extracting a variable number of patterns with various shapes. Temporal patterns learned from medical, visit and cost data made significant contributions to the prediction performance. Hyper-parameter tuning showed that considering three-month data patterns has the highest prediction accuracy. Our results showed that patients' images extracted from multivariate time series data are different from regular images, and hence require unique designs of CNN architectures. The proposed method for converting multivariate time series data of patients into images and tuning them for convolutional learning could be applied in many other healthcare applications with multivariate time series data.

Display Omitted Meta-regression can be used to identify important features of CDS interventions.However, this approach can lead to misleading conclusions.This paper provides a case study on this issue in the area of CDS success factors.Recommendations for addressing this issue are provided. Clinical decision support interventions are typically heterogeneous in nature, making it difficult to identify why some interventions succeed while others do not. One approach to identify factors important to the success of health information systems is the use of meta-regression techniques, in which potential explanatory factors are correlated with the outcome of interest. This approach, however, can result in misleading conclusions due to several issues. In this manuscript, we present a cautionary case study in the context of clinical decision support systems to illustrate the limitations of this type of analysis. We then discuss implications and recommendations for future work aimed at identifying success factors of medical informatics interventions. In particular, we identify the need for head-to-head trials in which the importance of system features is directly evaluated in a prospective manner.

Pharmacogenetic testing is one of the primary drivers of personalized medicine. The use of pharmacogenetic testing may provide a lifetime of benefits through tailoring drug dosing and selection of multiple medications to improve therapeutic outcomes and reduce adverse responses. We aimed to assess public interest and concerns regarding sharing and storage of pharmacogenetic test results that would facilitate the reuse of pharmacogenetic data across a lifetime of care.We conducted a random-digit-dial phone survey of a sample of the US public.We achieved an overall response rate of 42% (n = 1139). Most respondents indicated that they were extremely or somewhat comfortable allowing their pharmacogenetic test results to be shared with other doctors involved in their care management (90% ± 2.18%); significantly fewer respondents (74% ± 3.27%) indicated that they were extremely or somewhat comfortable sharing results with their pharmacist (P0.0001).Patients, pharmacists, and physicians will all be critical players in the pharmacotherapy process. Patients are supportive of sharing pharmacogenetic test results with physicians and pharmacists and personally maintaining their test results. However, further study is needed to understand which options are needed for sharing, appropriate storage, and patient education about the relevance of pharmacogenetic test results to promote consideration of this information by other prescribing practitioners.

It has been reported that Th1 to Th2 immune deviation effectively promotes peripheral tolerance in situations involving a limited T cell clone size, such as T cell-dependent autoimmunity and transplantation across minor, but not major, histocompatibility barriers. In this study, we tested the hypothesis that while Th1 to Th2 immune deviation fails to induce tolerance in the MHC-mismatched islet allograft model, it may promote a state that is permissive for tolerance induction. Here, we report that anti-IL-12 did not prevent acute rejection of islet allografts when administered alone. In conjunction with CTLA4/Fc, however, anti-IL-12 greatly facilitated long-term engraftment in three MHC-mismatched strain combinations. Similarly, while non-cytolytic IL-4/Fc, a long-lasting form of IL-4, did not prevent acute graft rejection when administered alone, a low, but not a high, dose of IL-4/Fc synergized with CTLA4/Fc in inducing significant levels of islet allograft tolerance. Moreover, by using a skin allograft adoptive transfer model, we show that these effects induced by anti-IL-12 and IL-4/Fc treatment were associated with an enhancement of the suppressive properties of CD4(+)CD25(+) regulatory T cells. Thus, anti-IL-12 and low-dose IL-4/Fc facilitate, but do not cause, islet allograft tolerance in mice by increasing the immunosuppressive potency of CD4(+)CD25(+) regulatory T cells.