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Background: Practices to detect and manage hyperbilirubinemia in newborn nurseries are highly variable. American Academy of Pediatrics guidelines in 1999, 2004, and 2009 have generated, perhaps unintentionally, divergent practices that might not all be of equivalent value. Evidence-based progress is needed to define less invasive, less expensive, uniform, and safe methods to reduce ER visits and hospital readmissions for jaundice treatment and bilirubin encephalopathy. Objectives: This research briefing is intended to inform readers of a new prospective quality improvement program aimed at testing the value of specific changes in newborn nursery hyperbilirubinemia detection and management. This new program includes predetermined means of assessing those specific changes, which relate to diagnosis, safety, outcomes, and cost. Methods: In this briefing, we present the perceived problems in our present bilirubin management system, as voiced by stakeholders. We report our proposed means to test minimization of those problems utilizing already acquired data on approximately 400,000 well babies in the Intermountain Healthcare system of hospitals in the western USA. We then describe our methods of assessing specific outcomes in a pre- versus postpractice change analysis. Results and Conclusions: The University of Utah Newborn Nursery will implement a quality improvement project in bilirubin management during 2020 to test the feasibility and effectiveness of several changes to our current bilirubin management program. We maintain that the improved understanding generated by this project will be a step toward new evidence-based strategies for reducing ER visits and hospital readmissions for jaundice treatment and preventing bilirubin encephalopathy.

Key Points Question Is an electronic health record add-on app for neonatal bilirubin management associated with time savings for clinicians and improved quality of care? Findings In this quality improvement study, an electronic health record add-on app for neonatal bilirubin management saved clinicians a mean of 66 seconds for bilirubin management tasks compared with a commonly used tool. In a retrospective pre-post analysis, the odds of clinically appropriate phototherapy orders during hospitalization increased significantly by 84%. Meaning These findings suggest that well-designed electronic health record add-on apps may be associated with time savings for physicians and improvements in patient care., Importance The usefulness of electronic health record (EHR) systems could be significantly enhanced by innovative, third-party EHR add-on apps. Objective To evaluate whether an EHR add-on app for neonatal bilirubin management can save clinicians time and improve patient care. Design, Setting, and Participants This quality improvement study was conducted at the University of Utah Health Well Baby nursery and outpatient clinics and consisted of 4 substudies: (1) time savings were estimated in an experimental task-timing study comparing the time required for physicians to manage newborns’ bilirubin levels with and without the add-on app, (2) app use was estimated from app logs, (3) health care use measures and guideline compliance were compared retrospectively before and after the intervention, and (4) clinician-perceived usability was measured through System Usability Scale surveys. The study took place between April 1, 2016, and September 3, 2019. Data analyses were conducted from October 30, 2018, to September 23, 2019. Interventions At baseline, clinicians used a manual approach to ensure compliance with an evidence-based clinical guideline for neonatal bilirubin management. To facilitate guideline compliance, an EHR add-on app that automatically retrieves, organizes, and visualizes relevant patient data was developed. The app provides patient-specific assessments and recommendations, including the risk of rebound hyperbilirubinemia following phototherapy based on a predictive model. The add-on app was integrated with the University of Utah Health EHR on April 12, 2017. Main Outcomes and Measures Clinician time savings, app use, health care use measures, guideline-compliant phototherapy ordering, and perceived usability as measured by the System Usability Scale survey. The survey is composed of 10 statements with responses ranging from 1 (strongly disagree) to 5 (strongly agree). The survey results in a single score ranging from 0 to 100, with ratings described as worst imaginable (mean System Usability Scale score, 12.5), awful (20.3), poor (35.7), okay (50.9), good (71.4), excellent (85.5), and best imaginable (90.9). Results In 2018, the application was used 20 516 times by clinicians for 91.84% of eligible newborns. Use of the app saved 66 seconds for bilirubin management tasks compared with a commonly used tool (95% CI, 53-79 seconds; P, This quality improvement study evaluates whether an electronic health record add-on app for neonatal bilirubin management is associated with clinician time savings and improvements in patient care.

Background Prostate-specific antigen (PSA) testing for early detection of prostate cancer is low-value when it is not indicated by guidelines and the harms outweigh the benefits. In this retrospective cohort study, we identify provider and patient factors associated with PSA testing, particularly in situations where testing would be low-value. Methods We used electronic health record data from 2011 to 2018 representing 1,738,021 health system encounters in the United States. Using logistic generalized estimating equation models, we examined patient factors (age, comorbid illness, family history, race and prior PSA results), provider factors (gender, specialty, graduation year and medical school rank), and overall time trends associated with PSA testing in low-value and appropriate settings. Results Comorbid illness (odds ratio (OR) 0.0 for 3+ conditions vs none) and no prior PSA testing (OR 0.2) were associated with a lower likelihood of PSA testing in low-value situations, while family history of prostate cancer (OR 1.6) and high prior PSA test results (OR 2.2 for PSA > 6 vs 0–1) were associated with a greater likelihood. Men aged 55–65 years were at greatest risk for PSA testing in low-value situations. The provider factor associated with PSA testing in low-value situations was specialty, with urologists being most likely (OR 2.3 versus advanced practice providers). Internal medicine physicians were more likely to perform PSA testing during low-value situations (OR 1.3 versus advanced practice providers) but much more likely to order a PSA test where appropriate (OR 2.2). All PSA testing decreased since 2011. Conclusion We identified several patient and provider factors associated with PSA testing in low-value settings. Some aspects suggest attention to relevant factors for PSA testing in low-value settings (e.g. comorbid illness), while others may encourage PSA testing in low-value settings (e.g. family history). The greatest likelihood of PSA testing in low-value settings is among men within the age range most commonly recommended by guidelines.

Objective The study sought to describe key features of clinical concepts and data required to implement clinical practice recommendations as clinical decision support (CDS) tools in electronic health record systems and to identify recommendation features that predict feasibility of implementation. Materials and Methods Using semistructured interviews, CDS implementers and clinician subject matter experts from 7 academic medical centers rated the feasibility of implementing 10 American College of Emergency Physicians Choosing Wisely Recommendations as electronic health record–embedded CDS and estimated the need for additional data collection. Ratings were combined with objective features of the guidelines to develop a predictive model for technical implementation feasibility. Results A linear mixed model showed that the need for new data collection was predictive of lower implementation feasibility. The number of clinical concepts in each recommendation, need for historical data, and ambiguity of clinical concepts were not predictive of implementation feasibility. Conclusions The availability of data and need for additional data collection are essential to assess the feasibility of CDS implementation. Authors of practice recommendations and guidelines can enable organizations to more rapidly assess data availability and feasibility of implementation by including operational definitions for required data.

BACKGROUND AND OBJECTIVES: Poor identification of individuals with CKD is a major barrier to research and appropriate clinical management of the disease. We aimed to develop and validate a pragmatic electronic (e-) phenotype to identify patients likely to have CKD. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The e-phenotype was developed by an expert working group and implemented among adults receiving in- or outpatient care at five healthcare organizations. To determine urine albumin (UA) dipstick cutoffs for CKD to enable use in the e-phenotype when lacking urine albumin-to-creatinine ratio (UACR), we compared same day UACR and UA results at four sites. A sample of patients, spanning no CKD to ESKD, was randomly selected at four sites for validation via blinded chart review. RESULTS: The CKD e-phenotype was defined as most recent eGFR 90 days prior and/or a UACR of ≥30 mg/g in the most recent test with at least one positive value >90 days prior. Dialysis and transplant were identified using diagnosis codes. In absence of UACR, a sensitive CKD definition would consider negative UA results as normal to mildly increased (KDIGO A1), trace to 1+ as moderately increased (KDIGO A2), and ≥2+ as severely increased (KDIGO A3). Sensitivity, specificity, and diagnostic accuracy of the CKD e-phenotype were 99%, 99%, and 98%, respectively. For dialysis sensitivity was 94% and specificity was 89%. For transplant, sensitivity was 97% and specificity was 91%. CONCLUSIONS: The CKD e-phenotype provides a pragmatic and accurate method for EHR-based identification of patients likely to have CKD.

Background. Family health history (FHH) can be used to identify individuals at elevated risk for familial cancers. Risk criteria for common cancers rely on age of onset, which is documented inconsistently as structured and unstructured data in electronic health records (EHRs). Objective. To investigate a natural language processing (NLP) approach to extract age of onset and age of death from free-text EHR fields. Methods. Using 474,651 FHH entries from 89,814 patients, we investigated two methods – frequent patterns (baseline) and NLP classifier. Results. For age of onset, the NLP classifier outperformed the baseline in precision (96% vs. 83%; 95% CI [94, 97] and [80, 86]) with equivalent recall (both 93%; 95% CI [91, 95]). When applied to the full dataset, the NLP approach increased the percentage of FHH entries for which cancer risk criteria could be applied from 10% to 15%. Conclusion. NLP combined with structured data may improve the computation of familial cancer risk criteria for various use cases.

BACKGROUND AND OBJECTIVES: An early-onset sepsis (EOS) risk calculator tool to guide evaluation and treatment of infants at risk for sepsis has reduced antibiotic use without increased adverse outcomes. We performed an electronic health record (EHR)–driven quality improvement intervention to increase calculator use for infants admitted to a newborn nursery and reduce antibiotic treatment of infants at low risk for sepsis. METHODS: This 2-phase intervention included programming (1) an EHR form containing calculator fields that were external to the infant’s admission note, with nonautomatic access to the calculator, education for end-users, and reviewing risk scores in structured bedside rounds and (2) discrete data entry elements into the EHR admission form with a hyperlink to the calculator Web site. We used statistical process control to assess weekly entry of risk scores and antibiotic orders and interrupted time series to assess trend of antibiotic orders. RESULTS: During phase 1 (duration, 14 months), a mean 59% of infants had EOS calculator scores entered. There was wide variability around the mean, with frequent crossing of weekly means beyond the 3σ control lines, indicating special-cause variation. During phase 2 (duration, 2 years), mean frequency of EOS calculator use increased to 85% of infants, and variability around the mean was within the 3σ control lines. The frequency of antibiotic orders decreased from preintervention (7%) to the final 6 months of phase 2 (1%, P < .001). CONCLUSIONS: An EHR-driven quality improvement intervention increased EOS calculator use and reduced antibiotic orders, with no increase in adverse events.

Background: Oral anticoagulants reduce stroke risk among atrial fibrillation (AF) patients, yet treatment rates remain low. A technology known as SMART on FHIR allows third party apps to integrate with electronic medical records (EMR) and provide decision support tools. University of Utah Health implemented an alpha version of an integrated MDCalc app - MDCalc on FHIR (MoF) - in the Epic EHR using a SMART on FHIR interface to enable multiple calculations, one of which was the CHA2DS2-VASc calculation. While MDCalc on FHIR is designed to automate inputs that are then reviewed by the practicing clinician with an opportunity to override with clinical judgment, we prospectively compared the automated app score - without physician oversight - with the clinician score, as an early measurement to evaluate accuracy and identify areas for improvement. Methods: We identified outpatient AF patients who were seen in the University of Utah’s cardiovascular clinics between 11/5/2018 and 12/7/2018. We used the MoF app to automatically calculate the CHA2DS2-VASc score within 24 hours of the identified clinic visit - without the MoF feature of allowing clinician interaction - and compared these values to the score documented by the clinician. We also categorized patients as either low risk of stroke or high risk of stroke and calculated the net reclassification index (NRI) using the app compared to documentation. Patients with an app score ≥2 were considered high risk, while patients with a documented score of ≥2 or were prescribed an oral anticoagulant were considered high risk. Results: We identified 200 AF patients, of whom 111 had a documented clinician score. The mean MoF app score was 3.79 (SD 1.86) compared to a mean clinician score of 3.25 (SD 1.63; p=0.02). The NRI was 13.5% (27 of 200) using the app compared to documentation. Ten percent (19 of 200) of patients were “up-classified” by the app, meaning they were high risk by app and low risk by the clinician. Four percent (8 of 200) of patients were “down-classified” by the app, meaning they were low risk by the app and high risk by the clinician. Upon review of these cases, and after accounting for patients who were or were not anticoagulated for a clinically relevant reason (history of bleeding, recent cardioversion, or patient preference), we found that three percent (5 of 200) of patients were “up-classified” by the app, and two percent (3 of 200) of patients were “down-classified” by the app, making the adjusted NRI 4% (8 of 200). Conclusion: SMART on FHIR enables third party vendors to create EMR-based apps, which could provide decision support and improve care. We found that our FHIR-app based approach tended to identify more comorbidities by using medication data to assign conditions, resulting in a higher CHA2DS2-VASc score compared to clinicians. These differences would have had a 4% effect on the actual decision to anticoagulate.

Background: Effective care coordination of children and youth with special health care needs (CYSHCN) is critical but challenging. Objective: To investigate clinicians’ information-gathering strategies while preparing for visits with CYSHCN. Methods: Critical incident interviews with primary care physicians and care coordinators. Results: Six themes emerged indicating 1) substantial reliance on the electronic health record; 2) a central role of the problem list in organizing and summarizing information; 3) Medical Home’s central role in organizing clinical documentation; 4) universal need to integrate information from external records; 5) lack of well-organized and labeled encounter documentation; and 6) lack of tools reconcile medication lists. Conclusion: Our findings have important implications to the design of informatics tools to support information-gathering in the care of CYSHCN.

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.

Background/AimsTo investigate racial differences in the variability of longitudinal visual field testing in a ‘real-world’ clinical population, evaluate how these differences are influenced by socioeconomic status, and estimate the impact of differences in variability on the time to detect visual field progression.MethodsThis retrospective observational cohort study used data from 1103 eyes from 751 White individuals and 428 eyes from 317 black individuals. Linear regression was performed on the standard automated perimetry mean deviation values for each eye over time. The SD of the residuals from the trend lines was calculated and used as a measure of variability for each eye. The association of race with the SD of the residuals was evaluated using a multivariable generalised estimating equation model with an interaction between race and zip code income. Computer simulations were used to estimate the time to detect visual field progression in the two racial groups.ResultsBlack patients had larger visual field variability over time compared with white patients, even when adjusting for zip code level socioeconomic variables (SD of residuals for Black patients=1.53 dB (95% CI 1.43 to 1.64); for white patients=1.26 dB (95% CI 1.14 to 1.22); mean difference: 0.28 (95% CI 0.15 to 0.41); pConclusionBlack patients had larger visual field variability compared with white patients. This relationship was strongly influenced by socioeconomic status and may partially explain racial disparities in glaucoma outcomes.

Key Points Question What change will be associated with the revised US Preventive Services Task Force (USPSTF) lung cancer screening guideline for screening eligibility among female, Black, and Hispanic populations? Findings In this cross-sectional study of the Behavioral Risk Factor Surveillance System for 2017 and 2018, the proportion eligible for screening among current and former smokers increased by 30.3% for men, 40.5% for women, and 31.9% for White, 76.7% for Black, and 78.1% for Hispanic populations. Compared with men, women had lower odds of eligibility, and compared with White, Black and Hispanic individuals had lower odds of eligibility. Meaning These findings suggest that screening disparities may persist among women and racial/ethnic minority populations with the revised guideline., This cross-sectional study assesses the changes associated with the revised US Preventive Services Task Force guideline with lung cancer screening eligibility among female, Black, and Hispanic populations using a large nationwide survey., 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

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.

BACKGROUND Inappropriate laboratory testing is a contributor to waste in healthcare. OBJECTIVE To evaluate the impact of a multifaceted laboratory reduction intervention on laboratory costs. DESIGN A retrospective, controlled, interrupted time series (ITS) study. SETTING University of Utah Health Care, a 500-bed academic medical center in Salt Lake City, Utah. POPULATION All patients 18 years or older admitted to the hospital to a service other than obstetrics, rehabilitation, or psychiatry. INTERVENTION Multifaceted quality-improvement initiative in a hospitalist service including education, process change, cost feedback, and financial incentive. MEASUREMENTS Primary outcomes of lab cost per day and per visit. Secondary outcomes of number of basic metabolic panel (BMP), comprehensive metabolic panel (CMP), complete blood count (CBC), and prothrombin time/international normalized ratio tests per day; length of stay (LOS); and 30-day readmissions. RESULTS A total of 6310 hospitalist patient visits (intervention group) were compared to 25,586 nonhospitalist visits (control group). Among the intervention group, the unadjusted mean cost per day was reduced from $138 before the intervention to $123 after the intervention (P < 0.001), and the unadjusted mean cost per visit decreased from $618 to $558 (P = 0.005). The ITS analysis showed significant reductions in cost per day, cost per visit, and the number of BMP, CMP, and CBC tests per day (P = 0.034, 0.02

An important informatics tool for controlling healthcare costs is accurately predicting the likely future healthcare costs of individuals. To address this important need, we conducted a systematic literature review and identified five methods for predicting healthcare costs. To enable a direct comparison of these different approaches, we empirically evaluated the predictive performance of each reported approach, as well as other state-of-the-art supervised learning methods, using data from University of Utah Health Plans for October 2013 through October 2016. The data set consisted of approximately 90,000 individuals, 6.3 million medical claims and 1.2 million pharmacy claims. In this comparative analysis, gradient boosting had the best predictive performance overall and for low to medium cost individuals. For high cost individuals, Artificial Neural Network (ANN) and the Ridge regression model, which have not been previously reported for use in healthcare cost prediction, had the highest performance.

This commentary introduces the Patient-Centered Clinical Decision Support (PCCDS) Learning Network, which is collaborating with AcademyHealth to publish “Better Decisions Together” as part of eGEMs. Patient-centered clinical decision support (CDS) is an important vehicle to address broad issues in the U.S. health care system regarding quality and safety while also achieving better outcomes and better patient and provider satisfaction. Defined as CDS that supports individual patients and their care givers and/or care teams in health-related decisions and actions, PCCDS is an important step forward in advancing endeavors to move patient-centered care forward. The PCCDS Learning Network has developed a framework, referred to as the Analytic Framework for Action (AFA), to organize thinking and activities around PCCDS. A wide array of activities the PCCDS Learning Network is engaging in to inform and connect stakeholders is discussed.

BACKGROUND Unnecessary telemetry monitoring contributes to healthcare waste. OBJECTIVE To evaluate the impact of 2 interventions to reduce telemetry utilization. DESIGN, SETTING, PATIENTS A 2-group retrospective, observational pre- to postintervention study of 35,871 nonintensive care unit (ICU) patients admitted to 1 academic medical center. INTERVENTION On the hospitalist service, we implemented a telemetry reduction intervention including education, process change, routine feedback, and a financial incentive between January 2015 and June 2015. In July 2015, a system-wide change to the telemetry ordering process was introduced. MEASUREMENTS The primary outcome was telemetry utilization, measured as the percentage of daily room charges for telemetry. Secondary outcomes were mortality, escalation of care, code event rate, and appropriateness of telemetry utilization. Generalized linear models were used to evaluate changes in outcomes while adjusting for patient factors. RESULTS Among hospitalist service patients, telemetry utilization was reduced by 69% (95% confidence interval [CI], -72% to -64%; P < .001), whereas on other services the reduction was a less marked 22% (95% CI, -27% to -16%; P < .001). There were no significant increases in mortality, code event rates, or care escalation, and there was a trend toward improved utilization appropriateness. CONCLUSIONS Although electronic telemetry ordering changes can produce decreases in hospital-wide telemetry monitoring, a multifaceted intervention may lead to an even larger decline in utilization rates. Whether these changes are durable cannot be ascertained from our study.

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.

BACKGROUND Cellulitis is a common infection with wide variation of clinical care. OBJECTIVE To implement an evidence-based care pathway and evaluate changes in process metrics, clinical outcomes, and cost for cellulitis. DESIGN A retrospective observational pre-/postintervention study was performed. SETTING University of Utah Health Care, a 500-bed academic medical center in Salt Lake City, Utah. PATIENTS All patients 18 years or older admitted to the emergency department observation unit or hospital with a primary diagnosis of cellulitis. INTERVENTION Development of an evidence-based care pathway for cellulitis embedded into the electronic medical record with education for all emergency and internal medicine physicians. MEASUREMENTS Primary outcome of broad-spectrum antibiotic use. Secondary outcomes of computed tomography/magnetic resonance imaging orders, length of stay (LOS), 30-day readmission, and pharmacy, lab, imaging, and total facility costs. RESULTS A total of 677 visits occurred, including 370 visits where order sets were used. Among all patients, there was a 59% decrease in the odds of ordering broad-spectrum antibiotics (P < 0.001), 23% decrease in pharmacy cost (P = 0.002), and 13% decrease in total facility cost (P = 0.006). Compared to patients for whom order sets were not used, patients for whom order sets were used had a 75%, 13%, and 25% greater decrease in the odds of ordering broad-spectrum antibiotics (P < 0.001), clinical LOS (P = 0.041), and pharmacy costs (P = 0.074), respectively. CONCLUSION The evidence-based care pathway for cellulitis improved care at an academic medical center by reducing broad-spectrum antibiotic use, pharmacy costs, and total facility costs without an adverse change in LOS or 30-day readmissions. Journal of Hospital Medicine 2015;10:780–786. © 2015 Society of Hospital Medicine

To determine the impact of systemwide charge display on laboratory utilization.This was a randomized controlled trial with a baseline period and an intervention period. Tests were randomized to a control arm or an active arm. The maximum allowable Medicare reimbursement rate was displayed for tests in the active arm during the intervention period. Total volume of tests in the active arm was compared with those in the control arm. Residents were surveyed before and after the intervention to assess charge awareness.Charge display had no effect on order behavior. This result held for patient type (inpatient vs outpatient) and for insurance category (commercial, government, self-pay). Residents overestimated the charges of tests both before and after the intervention. Many residents failed to notice the charge display in the computerized order entry system.The impact of charge display depends on context. Charge display is not always effective.

Well into the electronic health record (EHR) era, interface design issues remain unresolved. When developing EHR displays, human-centered design techniques are often ignored; this results in a cognitive burden on users. Critical care is demanding. Clinicians' cognitive resources (e.g., short-term memory) should be reserved for tasks requiring expertise, and not tasks of sifting and aggregating data. Excessive workload associated with poor interface design, can place critically-ill patients in danger. In this paper we describe the process of designing an information display with human-centered design principles, and knowledge elicitation through card sorting and subject matter expert interviews. Throughout three integrated phases we emphasized design to support target users. The phases included: 1) Defining Data Elements and Clinical Concepts, 2) Preliminary Design, and 3) Prototype Iterations. Our approach produced in an information display design for clinicians in the cardiovascular intensive care unit.

Physicians often fail to communicate well with patients. The objective of this retrospective controlled interrupted time series study was to evaluate the impact of a standardized communication intervention to improve physician communication. All patients ages 18 years or older (N = 7739 visits) admitted to University of Utah Health Care in Salt Lake City, Utah, from July 1, 2012, to June 31, 2014, were included. Obstetrics, rehabilitation, and psychiatric patients were excluded. The primary outcome was the percentage of patients who answered “Always” to all HCAHPS questions regarding physician-patient communication. Among the intervention group, the primary outcome increased from 56% to 63% ( P = .014, N = 1021) while remaining stable for the control group (65% to 66%, P = .6, N = 6718). The downward trend reversed after the intervention (−0.6% to +1.7% per month, P < .001). Standardized communication was associated with improvement in physician communication HCAHPS scores.

Medicaid beneficiaries in 6 North Carolina counties were randomly assigned to 1 of 3 clinical decision support (CDS) care transition strategies: (1) usual care (Control), (2) CDS messaging to patients and their medical homes (Reports), or (3) CDS messaging to patients, their medical homes, and their care managers (Reports+). We included 7146 Medicaid patients and evaluated transitions from specialist visit, ER and hospital encounters back to the patient's medical home. Patients enrolled in Medicare and Medicaid were not eligible. The number of care manager contacts was greater for patients in the Reports+ Group than in the Control Group. However, there were no treatment-related differences in emergency department (ED) encounter rates, or in the secondary outcomes of outpatient and hospital encounter rates and medical costs. Study monitors found study intervention documentation in approximately 60% of patient charts. These results highlight the importance of effectively integrating information interventions into healthcare delivery workflow systems.

Whole genome sequence (WGS) information may soon be widely available to help clinicians personalize the care and treatment of patients. However, considerable barriers exist, which may hinder the effective utilization of WGS information in a routine clinical care setting. Clinical decision support (CDS) offers a potential solution to overcome such barriers and to facilitate the effective use of WGS information in the clinic. However, genomic information is complex and will require significant considerations when developing CDS capabilities. As such, this manuscript lays out a conceptual framework for a CDS architecture designed to deliver WGS-guided CDS within the clinical workflow. To handle the complexity and breadth of WGS information, the proposed CDS framework leverages service-oriented capabilities and orchestrates the interaction of several independently-managed components. These independently-managed components include the genome variant knowledge base, the genome database, the CDS knowledge base, a CDS controller and the electronic health record (EHR). A key design feature is that genome data can be stored separately from the EHR. This paper describes in detail: (1) each component of the architecture; (2) the interaction of the components; and (3) how the architecture attempts to overcome the challenges associated with WGS information. We believe that service-oriented CDS capabilities will be essential to using WGS information for personalized medicine.

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 common 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.

e13137 Background: Identifying individuals with inherited cancer susceptibility is critical for optimal cancer prevention and screening. Family history is a significant predictor of inherited risk. Algorithms to extract and analyze family histories in electronic health records (EHRs) provide an opportunity for enhanced identification. Methods: Algorithms were developed to screen existing family history information in the EHR and identify patients meeting genetic evaluation guidelines for hereditary breast, ovarian, and/or colorectal cancer. In a pilot implementation, the patients of 5 primary care physicians at 2 community clinics were screened with the algorithms. Patients for genetic counseling were notified through the patient portal or mail. Follow-up phone calls were made to non-responders. Results: The algorithms identified 40 patients meeting criteria. Fifteen (38%) scheduled an appointment, 9 (22%) declined/will think about it, and 16 (40%) did not respond and were unable to be reached by phone. To date, 9 patients completed appointments. The family history used by the algorithm was accurate in 8 of 9 cases. For one case, the EHR family history was for adoptive relatives. Six of the 9 patients had additional family history not in the EHR, including 2 patients at-risk for familial mutations in ATM and ACVRL1. Patients who responded had their last visit within the healthcare system an average of 0.25 years ago versus 1.13 years for non-responders (p = 0.0002). Conclusions: Algorithms for screening existing family history in the EHR can identify primary care patients for hereditary cancer evaluation. While this approach has a low rate of false positives, it will likely miss appropriate individuals as 78% of patients had incomplete EHR-based family history as compared to a gold-standard genetic counselor obtained pedigree. However, use of automated algorithms will allow healthcare system to begin offering genetic services to those who can be identified. Ongoing efforts include use of natural language processing to extract family history from narrative data and use of a standards-based approach for replicability across EHRs.

Whole genome sequencing (WGS) is rapidly approaching widespread clinical application. Technology advancements over the past decade, since the first human genome was decoded, have made it feasible to use WGS for clinical care. Future advancements will likely drive down the price to the point wherein WGS is routinely available for care. However, were this to happen today, most of the genetic information available to guide clinical care would go unused due to the complexity of genetics, limited physician proficiency in genetics, and lack of genetics professionals in the clinical workforce. Furthermore, these limitations are unlikely to change in the future. As such, the use of clinical decision support (CDS) to guide genome-guided clinical decision-making is imperative. In this manuscript, we describe the barriers to widespread clinical application of WGS information, describe how CDS can be an important tool for overcoming these barriers, and provide clinical examples of how genome-enabled CDS can be used in the clinical setting.

Objective To review the literature on clinical decision support (CDS) for genetically guided personalized medicine (GPM). Materials and Methods MEDLINE and Embase were searched from 1990 to 2011. The manuscripts included were summarized, and notable themes and trends were identified. Results Following a screening of 3416 articles, 38 primary research articles were identified. Focal areas of research included family history-driven CDS, cancer management, and pharmacogenomics. Nine randomized controlled trials of CDS interventions for GPM were identified, seven of which reported positive results. The majority of manuscripts were published on or after 2007, with increased recent focus on genotype-driven CDS and the integration of CDS within primary clinical information systems. Discussion Substantial research has been conducted to date on the use of CDS to enable GPM. In a previous analysis of CDS intervention trials, the automatic provision of CDS as a part of routine clinical workflow had been identified as being critical for CDS effectiveness. There was some indication that CDS for GPM could potentially be effective without the CDS being provided automatically, but we did not find conclusive evidence to support this hypothesis. Conclusion To maximize the clinical benefits arising from ongoing discoveries in genetics and genomics, additional research and development is recommended for identifying how best to leverage CDS to bridge the gap between the promise and realization of GPM.

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.

In recent years, the general IT community has been moving from a monolithic-type of software architecture to a service-oriented architecture that involves developing systems using independent, well-defined software services that are then coordinated to meet business needs. The main benefit of a service-oriented architecture is the ability to more easily and more rapidly implement needed business capabilities using independent software services. While lagging behind many industries, the healthcare industry has been moving towards a service-oriented architecture, including in the space of clinical decision support. In this chapter, we describe notable efforts in service-oriented clinical decision support and speculate on its potential evolution in the future.

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.

System-agnostic clinical decision support (CDS) services provide patient evaluation capabilities that are independent of specific CDS systems and system implementation contexts. While such system-agnostic CDS services hold great potential for facilitating the widespread implementation of CDS systems, little has been described regarding the benefits and challenges of their use. In this manuscript, the authors address this need by describing potential benefits and challenges of using a system-agnostic CDS service. This analysis is based on the authors’ formal assessments of, and practical experiences with, various approaches to developing, implementing, and maintaining CDS capabilities. In particular, the analysis draws on the authors’ experience developing and leveraging a system-agnostic CDS Web service known as SEBASTIAN. A primary potential benefit of using a system-agnostic CDS service is the relative ease and flexibility with which the service can be leveraged to implement CDS capabilities across applications and care settings. Other important potential benefits include facilitation of centralized knowledge management and knowledge sharing; the potential to support multiple underlying knowledge representations and knowledge resources through a common service interface; improved simplicity and componentization; easier testing and validation; and the enabling of distributed CDS system development. Conversely, important potential challenges include the increased effort required to develop knowledge resources capable of being used in many contexts and the critical need to standardize the service interface. Despite these challenges, our experiences to date indicate that the benefits of using a system-agnostic CDS service generally outweigh the challenges of using this approach to implementing and maintaining CDS systems.

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.

Context: The healthcare industry could achieve significant benefits through the adoption of a service-oriented architecture (SOA). The specification and adoption of standard software service interfaces will be critical to achieving these benefits. Objective: To develop a replicable, collaborative framework for standardizing the interfaces of software services important to healthcare. Design: Iterative, peer-reviewed development of a framework for generating interoperable service specifications that build on existing and ongoing standardization efforts. The framework was created under the auspices of the Healthcare Services Specification Project (HSSP), which was initiated in 2005 as a joint initiative between Health Level7 (HL7) and the Object Management Group (OMG). In this framework, known as the HSSP Service Specification Framework, HL7 identifies candidates for service standardization and defines normative Service Functional Models (SFMs) that specify the capabilities and conformance criteria for these services. OMG then uses these SFMs to generate technical service specifications as well as reference implementations. Measurements: The ability of the framework to support the creation of multiple, interoperable service specifications useful for healthcare. Results: Functional specifications have been defined through HL7 for four services: the Decision Support Service; the Entity Identification Service; the Clinical Research Filtered Query Service; and the Retrieve, Locate, and Update Service. Technical specifications and commercial implementations have been developed for two of these services within OMG. Furthermore, three additional functional specifications are being developed through HL7. Conclusions: The HSSP Service Specification Framework provides a replicable and collaborative approach to defining standardized service specifications for healthcare.

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.

Cellulitis is a common infection with wide variation of clinical care.To implement an evidence-based care pathway and evaluate changes in process metrics, clinical outcomes, and cost for cellulitis.A retrospective observational pre-/postintervention study was performed.University of Utah Health Care, a 500-bed academic medical center in Salt Lake City, Utah.All patients 18 years or older admitted to the emergency department observation unit or hospital with a primary diagnosis of cellulitis.Development of an evidence-based care pathway for cellulitis embedded into the electronic medical record with education for all emergency and internal medicine physicians.Primary outcome of broad-spectrum antibiotic use. Secondary outcomes of computed tomography/magnetic resonance imaging orders, length of stay (LOS), 30-day readmission, and pharmacy, lab, imaging, and total facility costs.A total of 677 visits occurred, including 370 visits where order sets were used. Among all patients, there was a 59% decrease in the odds of ordering broad-spectrum antibiotics (P0.001), 23% decrease in pharmacy cost (P = 0.002), and 13% decrease in total facility cost (P = 0.006). Compared to patients for whom order sets were not used, patients for whom order sets were used had a 75%, 13%, and 25% greater decrease in the odds of ordering broad-spectrum antibiotics (P0.001), clinical LOS (P = 0.041), and pharmacy costs (P = 0.074), respectively.The evidence-based care pathway for cellulitis improved care at an academic medical center by reducing broad-spectrum antibiotic use, pharmacy costs, and total facility costs without an adverse change in LOS or 30-day readmissions.

As with other areas of health care, a significant need exists for improving the quality of infectious diseases (ID) management. This is evidenced by reports of high rates of inadequate or inappropriate antimicrobial therapy in both the inpatient and outpatient settings. For example, 55% of the 41

Health care provides many opportunities in which the sharing of data between independent sites is highly desirable. Several standards are required to produce the functional and semantic interoperability necessary to support the exchange of such data: a common reference information model, a common set of data elements, a common terminology, common data structures, and a common transport standard. This paper addresses one component of that set of standards: the ability to create a document that supports the exchange of structured data components. Unfortunately, two different standards development organizations have produced similar standards for that purpose based on different information models: Health Level 7 (HL7)'s Clinical Document Architecture (CDA) and The American Society for Testing and Materials (ASTM International) Continuity of Care Record (CCR). The coexistence of both standards might require mapping from one standard to the other, which could be accompanied by a loss of information and functionality. This paper examines and compares the two standards, emphasizes the strengths and weaknesses of each, and proposes a strategy of harmonization to enhance future progress. While some of the authors are members of HL7 and/or ASTM International, the authors stress that the viewpoints represented in this paper are those of the authors and do not represent the official viewpoints of either HL7 or of ASTM International.

Enabling clinical decision support (CDS) across multiple electronic health record (EHR) systems has been a desired but largely unattained aim of clinical informatics, especially in commercial EHR systems. A potential opportunity for enabling such scalable CDS is to leverage vendor-supported, Web-based CDS development platforms along with vendor-supported application programming interfaces (APIs). Here, we propose a potential staged approach for enabling such scalable CDS, starting with the use of custom EHR APIs and moving towards standardized EHR APIs to facilitate interoperability. We analyzed three commercial EHR systems for their capabilities to support the proposed approach, and we implemented prototypes in all three systems. Based on these analyses and prototype implementations, we conclude that the approach proposed is feasible, already supported by several major commercial EHR vendors, and potentially capable of enabling cross-platform CDS at scale.

The use of a service-oriented architecture (SOA) has been identified as a promising approach for improving health care by facilitating reliable clinical decision support (CDS). A review of the literature through October 2013 identified 44 articles on this topic. The review suggests that SOA related technologies such as Business Process Model and Notation (BPMN) and Service Component Architecture (SCA) have not been generally adopted to impact health IT systems' performance for better care solutions. Additionally, technologies such as Enterprise Service Bus (ESB) and architectural approaches like Service Choreography have not been generally exploited among researchers and developers. Based on the experience of other industries and our observation of the evolution of SOA, we found that the greater use of these approaches have the potential to significantly impact SOA implementations for CDS.

This chapter examines in detail the bridge between the creation and delivery of CDS content – in other words, how knowledge resources can be integrated with clinical information systems (CISs) to enable CDS. While such integration can be relatively straightforward for a single instance – that is, the integration of a specific knowledge resource in a specific clinical information system – the challenge lies in the fact that both knowledge resources and clinical information systems are quite diverse. Consequently, there is no single knowledge integration architecture that can address all circumstances. However, there are several architectural patterns for knowledge integration that can, taken together, enable the effective integration of knowledge resources into applications. The primary purpose of this chapter, then, is to outline the main CDS knowledge integration architectures that are available and to detail the pros and cons of each approach. The appropriateness of a given architecture for a particular organization depends on a variety of factors, including the existing clinical information system infrastructure and the type of CDS capability involved (e.g. real-time vs. non-real-time applications. These various approaches are outlined here, with special attention being placed on knowledge integration architectures aligned with broad trends in the IT landscape, such as service-oriented architectures, cloud-based computing, and app-based software ecosystems. The chapter also discusses how CDS architectures must align with larger changes in the health care industry as a whole, as shifting health care reimbursement models are requiring continuity of care across multiple organizations and health IT systems centered around patients and populations rather than care episodes at individual care settings.

The use of genetics to personalize health care has been a goal and ambition of clinicians for several years. If used appropriately, such an approach could lead to improved quality of care and reduced costs. However, several barriers exist to the effective and efficient use of genetics in medicine which include the magnitude and complexity of genetic information, the limited training of clinicians in genetics, and the limited number of genetics experts. Many thought leaders are looking to health IT and in particular CDS to overcome these barriers to realize personalized health care. This chapter will review several aspects of personalized health care including family health history, genetic testing, pharmacogenomics, and whole genome sequencing, as well as how CDS has and can support personalized health care. The chapter will also describe efforts that are ongoing or required to make CDS for personalized health care available on a widespread scale.