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Examines electricity consumption by office and network equipment in the U.S. at the end of 1999. Types of network equipment; Calculation of annual energy consumption; Power management.

The National Library of Medicine (NLM)'s Value Set Authority Center (VSAC) is a crowd-sourced repository with a potential for substantial discrepancy among value sets for the same clinical concepts. To characterize this potential problem, we identified the most common chronic conditions affecting US adults and assessed for discrepancy among VSAC ICD-10-CM value sets for these conditions. An analysis of 32 value sets for 12 conditions identified that a median of 45% of codes for a given condition were potentially problematic (included in at least one, but not all, theoretically equivalent value sets). These problematic codes were used to document clinical care for potentially over 20 million patients in a data warehouse of approximately 150 million US adults. Users of VSAC diagnosis value sets should be cognizant of the prevalence of these discrepancies and take proactive steps to mitigate their impact. Further research is warranted to characterize and address this issue., (©2023 AMIA - All rights reserved.)

Background: We examined neighborhood characteristics concerning breast cancer screening annual adherence during the COVID-19 pandemic., Methods: We analyzed 6673 female patients aged 40 or older at increased inherited cancer risk in 2 large health care systems (NYU Langone Health [NYULH] and the University of Utah Health [UHealth]). Multinomial models were used to identify predictors of mammogram screening groups (non-adherent, pre-pandemic adherent, pandemic period adherent) in comparison to adherent females. Potential determinants included sociodemographic characteristics and neighborhood factors., Results: Comparing each cancer group in reference to the adherent group, a reduced likelihood of being non-adherent was associated with older age (OR: 0.97, 95% CI: 0.95, 0.99), a greater number of relatives with cancer (OR: 0.80, 95% CI: 0.75, 0.86), and being seen at NYULH study site (OR: 0.42, 95% CI: 0.29, 0.60). More relatives with cancer were correlated with a lesser likelihood of being pandemic period adherent (OR: 0.89, 95% CI: 0.81, 0.97). A lower likelihood of being pre-pandemic adherent was seen in areas with less education (OR: 0.77, 95% CI: 0.62, 0.96) and NYULH study site (OR: 0.35, 95% CI: 0.22, 0.55). Finally, greater neighborhood deprivation (OR: 1.47, 95% CI: 1.08, 2.01) was associated with being non-adherent., Conclusion: Breast screening during the COVID-19 pandemic was associated with being older, having more relatives with cancer, residing in areas with less educational attainment, and being seen at NYULH; non-adherence was linked with greater neighborhood deprivation. These findings may mitigate risk of clinically important screening delays at times of disruptions in a population at greater risk for breast cancer., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Kensaku Kawamoto reports honoraria, consulting, sponsored research, licensing, or co-development outside the submitted work in the past 3 years with Hitachi, Pfizer, RTI International, the University of California at San Francisco, Indiana University, the Korean Society of Medical Informatics, the University of Nebraska, NORC at the University of Chicago, the Regenstrief Foundation, Elsevier, the University of Pennsylvania, MD Aware, Security Risk Solutions, Custom Clinical Decision Support, and Yale University in the area of health information technology. Kensaku Kawamoto was also an unpaid board member of the non-profit Health Level 7 International health IT standard development organization, he is an unpaid member of the U.S. Health Information Technology Advisory Committee, and he has helped develop a number of health IT tools which may be commercialized to enable wider impact. None of these relationships have direct relevance to the manuscript but are reported in the interest of full disclosure.

Indiscriminate use of predictive models incorporating race can reinforce biases present in source data and lead to an exacerbation of health disparities. In some countries, such as the United States, there is therefore a push to remove race from prediction models; however, there are still many prediction models that use race as an input. Biomedical informaticists who are given the responsibility of using these predictive models in healthcare environments are likely to be faced with questions like how to deal with race covariates in these models. Thus, there is a need for a pragmatic framework to help model users think through how to include race in their chosen model so as to avoid inadvertently exacerbating disparities. In this paper, we use the case study of lung cancer screening to propose a simple framework to guide how model users can approach the use (or non-use) of race inputs in the predictive models they are tasked with leveraging in electronic health records and clinical workflows., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Elizabeth Stevens reports financial support was provided by National Institutes of Health. Kensaku Kawamoto reports financial support was provided by Agency for Healthcare Research and Quality., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Although low-dose CT (LDCT) scan imaging lung cancer screening (LCS) can reduce lung cancer mortality, it remains underused. Shared decision-making (SDM) is recommended to assess the balance of benefits and harms for each patient., Research Question: Do clinician-facing electronic health record (EHR) prompts and an EHR-integrated everyday SDM tool designed to support routine incorporation of SDM into primary care improve LDCT scan imaging ordering and completion?, Study Design and Methods: A preintervention and postintervention analysis was conducted in 30 primary care and four pulmonary clinics for visits with patients who met United States Preventive Services Task Force criteria for LCS. Propensity scores were used to adjust for covariates. Subgroup analyses were conducted based on the expected benefit from screening (high benefit vs intermediate benefit), pulmonologist involvement (ie, whether the patient was seen in a pulmonary clinic in addition to a primary care clinic), sex, and race and ethnicity., Results: In the 12-month preintervention phase among 1,090 eligible patients, 77 patients (7.1%) had LDCT scan imaging orders and 48 patients (4.4%) completed screenings. In the 9-month intervention phase among 1,026 eligible patients, 280 patients (27.3%) had LDCT scan imaging orders and 182 patients (17.7%) completed screenings. Adjusted ORs were 4.9 (95% CI, 3.4-6.9; P < .001) and 4.7 (95% CI, 3.1-7.1; P < .001) for LDCT imaging ordering and completion, respectively. Subgroup analyses showed increases in ordering and completion for all patient subgroups. In the intervention phase, the SDM tool was used by 23 of 102 ordering providers (22.5%) and for 69 of 274 patients (25.2%) for whom LDCT scan imaging was ordered and who needed SDM at the time of ordering., Interpretation: Clinician-facing EHR prompts and an EHR-integrated everyday SDM tool are promising approaches to improving LCS in the primary care setting. However, room for improvement remains. As such, further research is warranted., Trial Registry: ClinicalTrials.gov; No.: NCT04498052; URL: www., Clinicaltrials: gov., (Copyright © 2023 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Purpose: 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., Design: Retrospective cohort study., Participants: The TruvenHealth MarketScan Commercial Claims Database (IBM) contains demographic and claims data for > 160 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 aged < 40 years at the time of their first OAG diagnosis, those without at least 1 confirmatory OAG diagnosis at a subsequent visit, and those with < 4 years of follow-up data after OAG diagnosis., Methods: 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 to < 0.9, ≥ 0.9 to ≤ 1.1, > 1.1 to ≤ 2.1, and > 2.1). 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., Main Outcome Measures: Frequency of visual field testing among enrollees with OAG., Results: 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%) underwent > 0 to < 0.9 visual field tests per year, 42 129 (11.1%) underwent ≥ 0.9 to ≤ 1.1 visual field tests per year, 42 301 (11.1%) underwent > 1.1 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)., Conclusions: More than 75% of enrollees with OAG received < 1 visual field test per year and, thus, did not receive guideline-adherent glaucoma monitoring., (Copyright © 2022 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

Objectives: To review current studies about designing and implementing clinician-facing clinical decision support (CDS) integrated or interoperable with an electronic health record (EHR) to improve health care for populations facing disparities., Methods: We searched PubMed to identify studies published between January 1, 2011 and October 22, 2021 about clinician-facing CDS integrated or interoperable with an EHR. We screened abstracts and titles and extracted study data from articles using a protocol developed by team consensus. Extracted data included patient population characteristics, clinical specialty, setting, EHR, clinical problem, CDS type, reported user-centered design, implementation strategies, and outcomes., Results: There were 28 studies (36 articles) included. Most studies were performed at safety net institutions (14 studies) or Indian Health Service sites (6 studies). CDS tools were implemented in primary care outpatient settings in 24 studies (86%) for screening or treatment. CDS included point-of-care alerts (93%), order facilitators (46%), workflow support (39%), relevant information display (36%), expert systems (11%), and medication dosing support (7%). Successful outcomes were reported in 19 of 26 studies that reported outcomes (73%). User-centered design was reported during CDS planning (39%), development (32%), and implementation phase (25%). Most frequent implementation strategies were education (89%) and consensus facilitation (50%)., Conclusions: CDS tools may improve health equity and outcomes for patients who face disparities. The present review underscores the need for high-quality analyses of CDS-associated health outcomes, reporting of user-centered design and implementation strategies used in low-resource settings, and methods to disseminate CDS created to improve health equity., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Importance: Lung cancer incidence and mortality disproportionately affect women and racial/ethnic minority populations, yet screening guidelines for the past several years were derived from clinical trials of predominantly White men. To reflect current evidence, the US Preventive Services Task Force (USPSTF) has revised the eligibility criteria, which may help to ameliorate sex- and race/ethnicity-related disparities in lung cancer screening., Objective: To determine the changes associated with the revised USPSTF guideline for lung cancer screening eligibility among female, Black, and Hispanic populations using a large nationwide survey., Design, Setting, and Participants: This cross-sectional study included respondents to the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System who were 50 to 80 years of age with a smoking history in 19 states that used the optional lung cancer screening module. The change in eligibility among female, male, Black, Hispanic, and White participants was examined. Eligibility by sex and race/ethnicity was compared with a reference population. Data were collected from January 1, 2017, to December 31, 2018, and analyzed from May 8 to June 11, 2020., Exposures: Self-reported sex, race/ethnicity, age, and smoking history., Main Outcomes and Measures: Lung cancer screening eligibility using the revised USPSTF criteria. The previous criteria included current or past smokers (within 15 years) who were 55 to 80 years of age and had a smoking history of more than 30 pack-years. In the revised criteria, age was modified to 50 to 80 years; smoking history, to 20 pack-years., Results: Among 40 869 respondents aged 50 to 80 years with a smoking history, 21 265 (52.0%) were women, 3430 (8.4%) were Black, and 1226 (30.0%) were Hispanic (mean [SD] age, 65.6 [7.9] years). The revised criteria increased eligibility for the following populations: men (29.4% to 38.3% [8.9% difference]; P < .001), women (25.9% to 36.4% [10.5% difference]; P < .001), White individuals (31.1% to 40.9% [9.8% difference]; P < .001), Black individuals (16.3% to 28.8% [12.5% difference]; P < .001), and Hispanic individuals (10.5% to 18.7% [8.2% difference]; P < .001). The odds of eligibility were lower for women compared with men (adjusted odds ratio [AOR], 0.88; 95% CI, 0.79-0.99; P = .04) and for Black (AOR, 0.43; 95% CI, 0.33-0.56; P < .001) and Hispanic populations (AOR, 0.70; 95% CI, 0.62-0.80; P < .001) compared with the White population., Conclusions and Relevance: The revised USPSTF guideline may likely increase lung cancer screening rates for female, Black, and Hispanic populations. However, despite these potential improvements, lung cancer screening inequities may persist without tailored eligibility criteria.

Purpose: Patient-oriented eHealth practices (e.g., scheduling appointments on the Internet, communicating with providers online) are an integral part of modern healthcare delivery. It is not known what proportion of United States patients with glaucoma use eHealth and how this use compares with those without glaucoma., Design: Cross-sectional study., Participants: Participants in the 2016 through 2017 National Health Interview Survey (NHIS), a cross-sectional survey regarding health topics that is administered annually to a nationally representative sample of noninstitutionalized adults in the United States., Methods: Based on the NHIS complex survey design, we calculated the proportion of participants with and without glaucoma who used 5 different eHealth practices (looking up health information on the Internet, requesting or ordering a prescription medication on the Internet, scheduling a medical appointment on the Internet, communicating with healthcare provider online, and using online groups to learn about health topics) during the 12 months before the survey. We performed univariate and multivariate logistic regression to evaluate the association between self-reported glaucoma diagnosis and use of the eHealth practices., Main Outcome Measures: Proportion using eHealth practices and odds ratios (ORs)., Results: Forty thousand four hundred sixty-five survey participants were included in the analysis; 1932 had self-reported glaucoma and 38 533 did not. For those with glaucoma, 46.2% (95% confidence interval [CI], 43.3%-49.0%) used at least one of the eHealth practices in the 12 months before the survey compared with 52.5% (95% CI, 51.5%-53.5%) of those without glaucoma (P < 0.001). After adjusting for confounders, participants with glaucoma were more likely to report use of at least 1 of the eHealth practices compared with those without glaucoma (adjusted OR, 1.22; 95% CI, 1.06-1.40; P = 0.005)., Conclusions: In this nationally representative United States population, nearly half of people with glaucoma reported using eHealth practices. Although a lower proportion of people with glaucoma used eHealth practices compared with those without glaucoma, this relationship actually reversed after adjusting for confounders. Because many patients with glaucoma are capable of and already using e-Health, further investment in these technologies may be beneficial., (Copyright © 2020 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

With the advent of interoperability standards such as FHIR, SMART, CDS Hooks, and CQL, interoperable clinical decision support (CDS) holds great promise for improving healthcare. In 2018, the Agency for Healthcare Research and Quality (AHRQ)-sponsored Patient-Centered CDS Learning Network (PCCDS LN) chartered a Technical Framework Working Group (TechFWG) to identify barriers, facilitators, and potential solutions for interoperable CDS, with a specific focus on addressing the opioid epidemic. Through an open, multi-stakeholder process that engaged 54 representatives from healthcare, industry, and academia, the TechFWG identified barriers in 6 categories: regulatory environment, data integration, scalability, business case, effective and useful CDS, and care planning and coordination. Facilitators and key recommendations were also identified for overcoming these barriers. The key insights were also extrapolated to CDS-facilitated care improvement outside of the specific opioid use case. If applied broadly, the recommendations should help advance the availability and impact of interoperable CDS delivered at scale., (©2019 AMIA - All rights reserved.)

Objective: To design and assess a method to leverage individuals' temporal data for predicting their healthcare cost. To achieve this goal, we first used patients' temporal data in their fine-grain form as opposed to coarse-grain form. Second, we devised novel spike detection features to extract temporal patterns that improve the performance of cost prediction. Third, we evaluated the effectiveness of different types of temporal features based on cost information, visit information and medical information for the prediction task., Materials and Methods: We used three years of medical and pharmacy claims data from 2013 to 2016 from a healthcare insurer, where the first two years were used to build the model to predict the costs in the third year. To prepare the data for modeling and prediction, the time series data of cost, visit and medical information were extracted in the form of fine-grain features (i.e., segmenting each time series into a sequence of consecutive windows and representing each window by various statistics such as sum). Then, temporal patterns of the time series were extracted and added to fine-grain features using a novel set of spike detection features (i.e., the fluctuation of data points). Gradient Boosting was applied on the final set of extracted features. Moreover, the contribution of each type of data (i.e., cost, visit and medical) was assessed. We benchmarked the proposed predictors against extant methods including those that used coarse-grain features which represent each time series with various statistics such as sum and the most recent portion of the values in the entire series. All prediction performances were measured in terms of Mean Absolute Percentage Error (MAPE)., Results: Gradient Boosting applied on fine-grain predictors outperformed coarse-grain predictors with a MAPE of 3.02 versus 8.14 (p < 0.01). Enhancing the fine-grain features with the temporal pattern extraction features (i.e., spike detection features) further improved the MAPE to 2.04 (p < 0.01). Removing cost, visit and medical status data resulted in MAPEs of 10.24, 2.22 and 2.07 respectively (p < 0.01 for the first two comparisons and p = 0.63 for the third comparison)., Conclusions: Leveraging fine-grain temporal patterns for healthcare cost prediction significantly improves prediction performance. Enhancing fine-grain features with extraction of temporal cost and visit patterns significantly improved the performance. However, medical features did not have a significant effect on prediction performance. Gradient Boosting outperformed all other prediction models., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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., (Copyright © 2018 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)

Importance: Transformation of US health care from volume to value requires meaningful quantification of costs and outcomes at the level of individual patients., Objective: To measure the association of a value-driven outcomes tool that allocates costs of care and quality measures to individual patient encounters with cost reduction and health outcome optimization., Design, Setting, and Participants: Uncontrolled, pre-post, longitudinal, observational study measuring quality and outcomes relative to cost from 2012 to 2016 at University of Utah Health Care. Clinical improvement projects included total hip and knee joint replacement, hospitalist laboratory utilization, and management of sepsis., Exposures: Physicians were given access to a tool with information about outcomes, costs (not charges), and variation and partnered with process improvement experts., Main Outcomes and Measures: Total and component inpatient and outpatient direct costs across departments; cost variability for Medicare severity diagnosis related groups measured as coefficient of variation (CV); and care costs and composite quality indexes., Results: From July 1, 2014, to June 30, 2015, there were 1.7 million total patient visits, including 34 000 inpatient discharges. Professional costs accounted for 24.3% of total costs for inpatient episodes ($114.4 million of $470.4 million) and 41.9% of total costs for outpatient visits ($231.7 million of $553.1 million). For Medicare severity diagnosis related groups with the highest total direct costs, cost variability was highest for postoperative infection (CV = 1.71) and sepsis (CV = 1.37) and among the lowest for organ transplantation (CV ≤ 0.43). For total joint replacement, a composite quality index was 54% at baseline (n = 233 encounters) and 80% 1 year into the implementation (n = 188 encounters) (absolute change, 26%; 95% CI, 18%-35%; P < .001). Compared with the baseline year, mean direct costs were 7% lower in the implementation year (95% CI, 3%-11%; P < .001) and 11% lower in the postimplementation year (95% CI, 7%-14%; P < .001). The hospitalist laboratory testing mean cost per day was $138 (median [IQR], $113 [$79-160]; n = 2034 encounters) at baseline and $123 (median [IQR], $99 [$66-147]; n = 4276 encounters) in the evaluation period (mean difference, -$15; 95% CI, -$19 to -$11; P < .001), with no significant change in mean length of stay. For a pilot sepsis intervention, the mean time to anti-infective administration following fulfillment of systemic inflammatory response syndrome criteria in patients with infection was 7.8 hours (median [IQR], 3.4 [0.8-7.8] hours; n = 29 encounters) at baseline and 3.6 hours (median [IQR], 2.2 [1.0-4.5] hours; n = 76 encounters) in the evaluation period (mean difference, -4.1 hours; 95% CI, -9.9 to -1.0 hours; P = .02)., Conclusions and Relevance: Implementation of a multifaceted value-driven outcomes tool to identify high variability in costs and outcomes in a large single health care system was associated with reduced costs and improved quality for 3 selected clinical projects. There may be benefit for individual physicians to understand actual care costs (not charges) and outcomes achieved for individual patients with defined clinical conditions.

Genome-wide association studies, DNA sequencing studies, and other genomic studies are finding an increasing number of genetic variants associated with clinical phenotypes that may be useful in developing diagnostic, preventive, and treatment strategies for individual patients. However, few variants have been integrated into routine clinical practice. The reasons for this are several, but two of the most significant are limited evidence about the clinical implications of the variants and a lack of a comprehensive knowledge base that captures genetic variants, their phenotypic associations, and other pertinent phenotypic information that is openly accessible to clinical groups attempting to interpret sequencing data. As the field of medicine begins to incorporate genome-scale analysis into clinical care, approaches need to be developed for collecting and characterizing data on the clinical implications of variants, developing consensus on their actionability, and making this information available for clinical use. The National Human Genome Research Institute (NHGRI) and the Wellcome Trust thus convened a workshop to consider the processes and resources needed to: (1) identify clinically valid genetic variants; (2) decide whether they are actionable and what the action should be; and (3) provide this information for clinical use. This commentary outlines the key discussion points and recommendations from the workshop., (© 2014 Wiley Periodicals, Inc.)

To determine whether a clinical decision support system can favorably impact the delivery of emergency department and hospital services. Randomized clinical trial of three clinical decision support delivery modalities: email messages to care managers (email), printed reports to clinic administrators (report) and letters to patients (letter) conducted among 20,180 Medicaid beneficiaries in Durham County, North Carolina with follow-up through 9 months. Patients in the email group had fewer low-severity emergency department encounters vs. controls (8.1 vs. 10.6/100 enrollees, p < 0.001) with no increase in outpatient encounters or medical costs. Patients in the letter group had more outpatient encounters and greater outpatient and total medical costs. There were no treatment-related differences for patients in the reports group. Among patients <18 years, those in the email group had fewer low severity (7.6 vs. 10.6/100 enrollees, p < 0.001) and total emergency department encounters (18.3 vs. 23.5/100 enrollees, p < 0.001), and lower emergency department ($63 vs. $89, p = 0.002) and total medical costs ($1,736 vs. $2,207, p = 0.009). Patients who were ≥18 years in the letter group had greater outpatient medical costs. There were no intervention-related differences in patient-reported assessments of quality of life and medical care received. The effectiveness of clinical decision support messaging depended upon the delivery modality and patient age. Health IT interventions must be carefully evaluated to ensure that the resultant outcomes are aligned with expectations as interventions can have differing effects on clinical and economic outcomes.

Objective: To identify key principles for establishing a national clinical decision support (CDS) knowledge sharing framework., Materials and Methods: As part of an initiative by the US Office of the National Coordinator for Health IT (ONC) to establish a framework for national CDS knowledge sharing, key stakeholders were identified. Stakeholders' viewpoints were obtained through surveys and in-depth interviews, and findings and relevant insights were summarized. Based on these insights, key principles were formulated for establishing a national CDS knowledge sharing framework., Results: Nineteen key stakeholders were recruited, including six executives from electronic health record system vendors, seven executives from knowledge content producers, three executives from healthcare provider organizations, and three additional experts in clinical informatics. Based on these stakeholders' insights, five key principles were identified for effectively sharing CDS knowledge nationally. These principles are (1) prioritize and support the creation and maintenance of a national CDS knowledge sharing framework; (2) facilitate the development of high-value content and tooling, preferably in an open-source manner; (3) accelerate the development or licensing of required, pragmatic standards; (4) acknowledge and address medicolegal liability concerns; and (5) establish a self-sustaining business model., Discussion: Based on the principles identified, a roadmap for national CDS knowledge sharing was developed through the ONC's Advancing CDS initiative., Conclusion: The study findings may serve as a useful guide for ongoing activities by the ONC and others to establish a national framework for sharing CDS knowledge and improving clinical care.

At present, there are no widely accepted, standard approaches for representing computer-based clinical decision support (CDS) intervention types and their structural components. This study aimed to identify key requirements for the representation of five widely utilized CDS intervention types: alerts and reminders, order sets, infobuttons, documentation templates/forms, and relevant data presentation. An XML schema was proposed for representing these interventions and their core structural elements (e.g., general metadata, applicable clinical scenarios, CDS inputs, CDS outputs, and CDS logic) in a shareable manner. The schema was validated by building CDS artifacts for 22 different interventions, targeted toward guidelines and clinical conditions called for in the 2011 Meaningful Use criteria. Custom style sheets were developed to render the XML files in human-readable form. The CDS knowledge artifacts were shared via a public web portal. Our experience also identifies gaps in existing standards and informs future development of standards for CDS knowledge representation and sharing.

Background: In recent years, the completion of the Human Genome Project and other rapid advances in genomics have led to increasing anticipation of an era of genomic and personalized medicine, in which an individual's health is optimized through the use of all available patient data, including data on the individual's genome and its downstream products. Genomic and personalized medicine could transform healthcare systems and catalyze significant reductions in morbidity, mortality, and overall healthcare costs., Discussion: Critical to the achievement of more efficient and effective healthcare enabled by genomics is the establishment of a robust, nationwide clinical decision support infrastructure that assists clinicians in their use of genomic assays to guide disease prevention, diagnosis, and therapy. Requisite components of this infrastructure include the standardized representation of genomic and non-genomic patient data across health information systems; centrally managed repositories of computer-processable medical knowledge; and standardized approaches for applying these knowledge resources against patient data to generate and deliver patient-specific care recommendations. Here, we provide recommendations for establishing a national decision support infrastructure for genomic and personalized medicine that fulfills these needs, leverages existing resources, and is aligned with the Roadmap for National Action on Clinical Decision Support commissioned by the U.S. Office of the National Coordinator for Health Information Technology. Critical to the establishment of this infrastructure will be strong leadership and substantial funding from the federal government., Summary: A national clinical decision support infrastructure will be required for reaping the full benefits of genomic and personalized medicine. Essential components of this infrastructure include standards for data representation; centrally managed knowledge repositories; and standardized approaches for leveraging these knowledge repositories to generate patient-specific care recommendations at the point of care.

Background and Objectives: Care for advanced CKD patients is suboptimal. CKD practice guidelines aim to close gaps in care, but making providers aware of guidelines is an ineffective implementation strategy. The Institute of Medicine has endorsed the use of clinical decision support (CDS) for implementing guidelines. The authors' objective was to identify the requirements of an optimal CDS system for CKD management., Design, Setting, Participants, and Measurements: The aims of this study expanded on those of previous work that used the facilitated process improvement (FPI) methodology. In FPI, an expert workgroup develops a set of quality improvement tools that can subsequently be utilized by practicing physicians. The authors conducted a discussion with a group of multidisciplinary experts to identify requirements for an optimal CDS system., Results: The panel considered the process of patient identification and management, associated barriers, and elements by which CDS could address these barriers. The panel also discussed specific knowledge needs in the context of a typical scenario in which CDS would be used. Finally, the group developed a set of core requirements that will likely facilitate the implementation of a CDS system aimed at improving the management of any chronic medical condition., Conclusions: Considering the growing burden of CKD and the potential healthcare and resource impact of guideline implementation through CDS, the relevance of this systematic process, consistent with Institute of Medicine recommendations, cannot be understated. The requirements described in this report could serve as a basis for the design of a CKD-specific CDS.

Non-adherence to evidence-based pharmacotherapy is associated with increased morbidity and mortality. Claims data can be used to detect and intervene on such non-adherence, but existing claims-based approaches for measuring adherence to pharmacotherapy guidelines have significant limitations. In this manuscript, we describe a methodology for assessing adherence to pharmacotherapy guidelines that overcomes many of these limitations. To develop this methodology, we first reviewed the literature to identify prior work on potential strategies for overcoming these limitations. We then assembled a team of relevant domain experts to iteratively develop an improved methodology. This development process was informed by the use of the proposed methodology to assess adherence levels for 14 pharmacotherapy guidelines related to seven common diseases among approximately 36,000 Medicaid beneficiaries. Finally, we evaluated the ability of the methodology to overcome the targeted limitations. Based on this evaluation, we conclude that the proposed methodology overcomes many of the limitations associated with existing approaches.

Health information technology evaluators need to distinguish between intervention efficacy as assessed in the ideal circumstances of clinical trials and intervention effectiveness as assessed in the real world circumstances of actual practice. Because current evaluation study designs do not routinely allow for this distinction, we have developed a framework for evaluation of implementation fidelity that considers health information technologies as complex interventions and makes use of common intervention components as defined in the Oxford Implementation Index. We also propose statistical methods for the evaluation of interventions at the system and component level using the Rubin Causal Model. We then describe how to apply this framework to evaluate an ongoing clinical trial of three health information technology interventions currently implemented in a 17,000 patient community-based health network caring for Medicaid beneficiaries in Durham County, North Carolina.

Despite their demonstrated effectiveness, clinical decision support (CDS) systems are not widely used within the U.S. The Roadmap for National Action on Clinical Decision Support, published in June 2006 by the American Medical Informatics Association, identifies six strategic objectives for achieving widespread adoption of effective CDS capabilities. In this manuscript, we propose a Service-Oriented Architecture (SOA) for CDS that facilitates achievement of these six objectives. Within the proposed framework, CDS capabilities are implemented through the orchestration of independent software services whose interfaces are being standardized by Health Level 7 and the Object Management Group through their joint Healthcare Services Specification Project (HSSP). Core services within this framework include the HSSP Decision Support Service, the HSSP Common Terminology Service, and the HSSP Retrieve, Locate, and Update Service. Our experiences, and those of others, indicate that the proposed SOA approach to CDS could enable the widespread adoption of effective CDS within the U.S. health care system.