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How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Health care organizations face growing pressure to improve their patients' social conditions, such as housing, food, and economic insecurity. Little is known about the motivations and concerns of health care organizations when implementing activities aimed at improving patients' social conditions. We used semi-structured interviews with 29 health care organizations to explore their motivations and tensions around social care. Administrators described an interwoven set of motivations for delivering social care: (a) doing the right thing for their patients, (b) improving health outcomes, and (c) making the business case. Administrators expressed tensions around the optimal role for health care in social care including uncertainty around (a) who should be responsible, (b) whether health care has the needed capacity/skills, and (c) sustainability of social care activities. Health care administrators could use guidance and support from policy makers on how to effectively prioritize social care activities, partner with other sectors, and build the needed workforce.

Introduction: COVID-19 has created additional challenges for the analysis of non-randomized interventions in health system settings. Our objective is to evaluate these challenges and identify lessons learned from the analysis of a medically tailored meals (MTM) intervention at Kaiser Permanente Northwest (KPNW) that began in April 2020., Methods: We identified both a historical and concurrent comparison group. The historical comparison group included patients living in the same area as the MTM recipients prior to COVID-19. The concurrent comparison group included patients admitted to contracted non-KPNW hospitals or admitted to a KPNW facility and living outside the service area for the intervention but otherwise eligible. We used two alternative propensity score methods in response to the loss of sample size with exact matching to evaluate the intervention., Results: We identified 452 patients who received the intervention, 3873 patients in the historical comparison group, and 5333 in the concurrent comparison group. We were able to mostly achieve balance on observable characteristics for the intervention and the two comparison groups., Conclusions: Lessons learned included: (a) The use of two different comparison groups helped to triangulate results; (b) the meaning of utilization measures changed pre- and post-COVID-19; and (c) that balance on observable characteristics can be achieved, especially when the comparison groups are meaningfully larger than the intervention group. These findings may inform the design for future evaluations of interventions during COVID-19., Competing Interests: The authors declare no potential conflict of interest., (© 2022 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.)

Introduction: The learning health system (LHS) aligns science, informatics, incentives, stakeholders, and culture for continuous improvement and innovation. The Agency for Healthcare Research and Quality and the Patient-Centered Outcomes Research Institute designed a K12 initiative to grow the number of LHS scientists. We describe approaches developed by 11 funded centers of excellence (COEs) to promote partnerships between scholars and health system leaders and to provide mentored research training., Methods: Since 2018, the COEs have enlisted faculty, secured institutional resources, partnered with health systems, developed and implemented curricula, recruited scholars, and provided mentored training. Program directors for each COE provided descriptive data on program context, scholar characteristics, stakeholder engagement, scholar experiences with health system partnerships, roles following program completion, and key training challenges., Results: To date, the 11 COEs have partnered with health systems to train 110 scholars. Nine (82%) programs partner with a Veterans Affairs health system and 9 (82%) partner with safety net providers. Clinically trained scholars (n = 87; 79%) include 70 physicians and 17 scholars in other clinical disciplines. Non-clinicians (n = 29; 26%) represent diverse fields, dominated by population health sciences. Stakeholder engagement helps scholars understand health system and patient/family needs and priorities, enabling opportunities to conduct embedded research, improve outcomes, and grow skills in translating research methods and findings into practice. Challenges include supporting scholars through roadblocks that threaten to derail projects during their limited program time, ranging from delays in access to data to COVID-19-related impediments and shifts in organizational priorities., Conclusions: Four years into this novel training program, there is evidence of scholars' accomplishments, both in traditional academic terms and in terms of moving along career trajectories that hold the potential to lead and accelerate transformational health system change. Future LHS training efforts should focus on sustainability, including organizational support for scholar activities., Competing Interests: With the exception of Francis D. Chesley, Jr., all authors are grantees of the program that is the subject of this paper., (© 2022 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.)

Objective: To characterize emerging and current practice models to more effectively treat and support patients with multiple chronic conditions (MCC)., Data Sources/study Setting: We conducted a rapid literature scoping augmented by key informant interviews with clinicians knowledgeable about MCC care from a broad spectrum of US delivery systems and feedback from multidisciplinary experts at two virtual meetings., Study Design: Literature findings were triangulated with data from semi-structured interviews with clinical experts. Reflections on early results were obtained from policy, research, clinical, advocacy, and patient representatives at two virtual meetings sponsored by the Agency for Healthcare Research and Quality. Emergent themes addressed were as follows: (1) more timely strategies for MCC care; and (2) trends not previously represented in the peer-reviewed literature., Data Collection/extraction Methods: The rapid literature scoping relied on Ovid MEDLINE(R) and Epub Ahead of Print databases for the most recent 5-year period. Qualitative interviews were conducted by telephone. Virtual meetings provided oral and written (chat) captured inputs., Principal Findings: Although the literature scoping did not identify a specific set of evidence-based care models, key informant discussions identified eight themes reflecting emerging approaches to population-based MCC care. For example, addressing the needs of individuals with MCC through a complexity lens by assessing and addressing social risk factors; extending the care continuum with home-based care; understanding how to address ongoing patient and caregiver supports outside of clinical encounters; and engaging available community resources., Conclusions: Integrating care for MCC patient populations requires processes for determining different subpopulation needs in various settings and lived experiences. Innovation should be anchored at the nexus of payment systems, social risks, medical needs, and community-based resources. Our learnings suggest a need for an ongoing MCC care research agenda to inform new approaches to care delivery incorporating innovations in technology and home-based supports for patients and caregivers., (© 2021 Health Research and Educational Trust.)

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention-the starting point for delivery of "All the right care, but only the right care," an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Trust in EHR data is becoming increasingly important as a greater share of clinical and health services research use EHR data. We discuss reasons for distrust and acknowledge limitations. Researchers continue to use EHR data because of strengths including greater clinical detail than sources like administrative billing claims. Further, many limitations are addressable with existing methods including data quality checks and common data frameworks. We discuss how to build greater trust in the use of EHR data for research, including additional transparency and research priority areas that will both enhance existing strengths of the EHR and mitigate its limitations., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Objective: To determine whether rapid administration of a crystalloid bolus of 30 mL/kg within 3 hours of presentation harms or benefits hypotensive patients with sepsis with a history of congestive heart failure (CHF)., Patients and Methods: A retrospective cohort study using Medicare claims data enhanced by medical record data from members of the High Value Healthcare Collaborative from July 1, 2013, to June 30, 2015, examining patients with a history of CHF who did (fluid bundle compliant [FBC]) or did not (NFBC) receive a volume bolus of 30 mL/kg within 3 hours of presentation to the emergency department. A proportional Cox hazard model was used to evaluate the association of FBC with 1-year survival., Results: Of the 211 patients examined, 190 were FBC and 21 were NFBC. The FBC patients had higher average hierarchical condition category scores but were otherwise similar to NFBC patients. The NFBC patients had higher adjusted in-hospital and postdischarge mortality rates. The risk-adjusted 1-year mortality rate was higher for NFBC patients (hazard ratio, 2.18; 95% CI, 1.2 to 4.0; P =.01) than for FBC patients., Conclusion: In a retrospective claim data-based study of elderly patients with a history of CHF presenting with severe sepsis or septic shock, there is an association of improved mortality with adherence to the initial fluid resuscitation guidelines as part of the 3-hour sepsis bundle., (© 2020 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc.)

Background: Shared decision making (SDM) research has emphasized the role of decision aids (DAs) for helping patients make treatment decisions reflective of their preferences, yet there have been few collaborative multi-institutional efforts to integrate DAs in orthopedic consultations and primary care encounters., Objective: In the context of routine DA implementation for SDM, we investigate which patient-level characteristics are associated with patient preferences for surgery versus medical management before and after exposure to DAs. We explored whether DA implementation in primary care encounters was associated with greater shifts in patients' treatment preferences after exposure to DAs compared to DA implementation in orthopedic consultations., Design: Retrospective cohort study., Setting: 10 High Value Healthcare Collaborative (HVHC) health systems., Study Participants: A total of 495 hip and 1343 adult knee osteoarthritis patients who were exposed to DAs within HVHC systems between July 2012 to June 2015., Results: Nearly 20% of knee patients and 17% of hip patients remained uncertain about their treatment preferences after viewing DAs. Older patients and patients with high pain levels had an increased preference for surgery. Older patients receiving DAs from three HVHC systems that transitioned DA implementation from orthopedics into primary care had lower odds of preferring surgery after DA exposure compared to older patients in seven HVHC systems that only implemented DAs for orthopedic consultations., Conclusion: Patients' treatment preferences were largely stable over time, highlighting that DAs for SDM largely do not necessarily shift preferences. DAs and SDM processes should be targeted at older adults and patients reporting high pain levels. Initiating treatment conversations in primary versus specialty care settings may also have important implications for engagement of patients in SDM via DAs., Competing Interests: The authors report no conflicts of interest in this work., (© 2020 Hurley et al.)

Background and Objectives: The translation of research findings into routine care remains slow and challenging. We previously reported successful implementation of an asthma evidence-based care process model (EB-CPM) at 8 (1 tertiary care and 7 community) hospitals, leading to a high health care provider (HCP) adherence with the EB-CPM and improved outcomes. In this study, we explore contextual factors perceived by HCPs to facilitate successful EB-CPM implementation., Methods: Structured and open-ended questions were used to survey HCPs ( n = 260) including physicians, nurses, and respiratory therapists, about contextual factors perceived to facilitate EB-CPM implementation. Quantitative analysis was used to identify significant factors (correlation coefficient ≥0.5; P ≤ .05) and qualitative analysis to assess additional facilitators., Results: Factors perceived by HCPs to facilitate EB-CPM implementation were related to (1) inner setting (leadership support, adequate resources, communication and/or collaboration, culture, and previous experience with guideline implementation), (2) intervention characteristics (relevant and applicable to the HCP's practice), (3) individuals (HCPs) targeted (agreement with the EB-CPM and knowledge of supporting evidence), and (4) implementation process (participation of HCPs in implementation activities, teamwork, implementation team with a mix of expertise and professional's input, and data feedback). Additional facilitators included (1) having appropriate preparation and (2) providing education and training., Conclusions: Multiple factors were associated with successful EB-CPM implementation and may be used by others as a guide to facilitate implementation and dissemination of evidence-based interventions for pediatric asthma and other chronic diseases in the hospital setting., Competing Interests: POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose., (Copyright © 2019 by the American Academy of Pediatrics.)

Artificial intelligence (AI) is becoming ubiquitous in health care, largely through machine learning and predictive analytics applications. Recent applications of AI to common health care scenarios, such as screening and diagnosing, have fueled optimism about the use of advanced analytics to improve care. Careful and objective considerations need to be made before implementing an advanced analytics solution. Critical evaluation before, during, and after its implementation will ensure safe care, good outcomes, and the elimination of waste. In this commentary we offer basic practical considerations for developing, implementing, and evaluating such solutions based on many years of experience.

Due to the increasing amount of available published evidence and the continual need to apply and update evidence in practice, we propose a shift in the way evidence generated by learning health systems can be integrated into more traditional evidence reviews. This paper discusses two main mechanisms to close the evidence-to-practice gap: (1) integrating Learning Health System (LHS) results with existing systematic review evidence and (2) providing this combined evidence in a standardized, computable data format. We believe these efforts will better inform practice, thereby improving individual and population health.

The Hospital Readmissions Reduction Program reduces Medicare prospective payments for hospitals with excess readmissions for selected diagnoses. By comparing data for patients who were readmitted or placed on observation status immediately before and immediately after the thirty-day cutoff for penalties, we sought to determine whether hospitals have responded to the program by shifting readmissions for heart failure to observation status. We used regression discontinuity, taking advantage of the cutoff to generate unbiased estimates of treatment effects. Overall, we found no evidence that the program has affected the use of observation stays. However, for nonpenalized hospitals, the use of observation status was 5.4 percent higher for patients returning to the hospital immediately before the thirty-day cutoff than for patients returning immediately after the cutoff, which suggests that some hospitals may have used observation status to help avoid penalties. Because differences in the cost-sharing rules may lead to higher out-of-pocket expenses for Medicare patients placed on observation status, the program could have an inequitable financial impact.

Background: Local social determinants may act as effect modifiers for the impact of neighborhood material deprivation on patient-level healthcare outcomes. The objective of this study was to understand the mediating effect of local social determinants on neighborhood material deprivation and delivery outcomes in heart failure (HF) patients., Material and Methods: A retrospective cohort study was conducted using 4737 HF patients receiving inpatient care (n=6065 encounters) from an integrated healthcare delivery system from 2010 to 2014. Outcomes included post-discharge mortality, readmission risk and length of stay. Deprivation was measured using an area deprivation index by address of residence. Effect modifications measured included urban-rural residency and faith identification using generalized linear regression models. Patient-level data was drawn from the delivery system data warehouse., Results: Faith identification had a significant protective effect on HF patients from deprived areas, lowering 30-day mortality odds by one-third over patients who did not identify with a faith (OR 0.35 95%CI:0.12-0.98;p=0.05). Significant effects persisted at the 90 and 180-day timeframes. In rural areas, lack of faith identification had a multiplicative effect on 30-day mortality for deprived patients (OR 14.0 95%CI:1.47-132.7;p=0.02). No significant effects were noted for other healthcare outcomes., Conclusions: The lack of expected association between area deprivation and healthcare outcomes in some communities may be explained by the presence of effect modifiers., Implications: Understanding existing effect modifiers for area deprivation in local communities that delivery systems serve can inform targeted quality improvement. These factors should also be considered when comparing delivery system performance for reimbursement and in population health management., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Objectives: Collecting social determinants data is challenging. We assigned patients a neighborhood-level social determinant measure, the area of deprivation index (ADI), by using census data. We then assessed the association between neighborhood deprivation and asthma hospitalization outcomes and tested the influence of insurance coverage., Methods: A retrospective cohort study of children 2 to 17 years old admitted for asthma at 8 hospitals. An administrative database was used to collect patient data, including hospitalization outcomes and neighborhood deprivation status (ADI scores), which were grouped into quintiles (ADI 1, the least deprived neighborhoods; ADI 5, the most deprived neighborhoods). We used multivariable models, adjusting for covariates, to assess the associations and added a neighborhood deprivation status and insurance coverage interaction term., Results: A total of 2270 children (median age 5 years; 40.6% girls) were admitted for asthma. We noted that higher ADI quintiles were associated with greater length of stay, higher cost, and more asthma readmissions ( P < .05 for most quintiles). Having public insurance was independently associated with greater length of stay (β: 1.171; 95% confidence interval [CI]: 1.117-1.228; P < .001), higher cost (β: 1.147; 95% CI: 1.093-1.203; P < .001), and higher readmission odds (odds ratio: 1.81; 95% CI: 1.46-2.24; P < .001). There was a significant deprivation-insurance effect modification, with public insurance associated with worse outcomes and private insurance with better outcomes across ADI quintiles ( P < .05 for most combinations)., Conclusions: Neighborhood-level ADI measure is associated with asthma hospitalization outcomes. However, insurance coverage modifies this relationship and needs to be considered when using the ADI to identify and address health care disparities., Competing Interests: POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose., (Copyright © 2018 by the American Academy of Pediatrics.)

Given the movement towards value-based purchasing in the United States, health care leaders need methods to characterize and address the complex effect that social determinants have on health care outcomes. This systematic literature review was specifically designed to understand current research on the effect that patient material and social deprivation has on health care delivery outcomes and the potential benefit of clinical interventions designed to mediate this effect. A total of 310 studies were identified for review with 80 studies included in the final synthesis. Results highlight significant variation in the methods used to measure the effect of social determinants on health care outcomes and the need for common measurement standards. More robust identification of deprivation-sensitive diseases or conditions is needed to channel scarce program resources to effected conditions. Finally, further research is needed to evaluate the benefits of data-driven, tailored clinical interventions designed to serve the needs of materially-deprived patient populations.

The purpose of this special issue is to disseminate learning from the High Value Healthcare Collaborative (HVHC). The HVHC is a voluntary, member-led organization based on trusted, working relationships among delivery system leaders. HVHC's mission is to be a provider-based learning health system committed to improving healthcare value through data, evidence, and collaboration. We begin by describing the organization and structure of HVHC in order to lay the context for a series of papers that feature work from this learning health system. HVHC was awarded a grant from the John and Laura Arnold Foundation to develop a generalizable model for dissemination and implementation. Implementation of the 3-hour sepsis bundle was used as a prototypic, complex intervention with an in-depth mixed methods evaluation across 16 member sites. The first four articles in this issue describe, in detail, various data and methodological challenges encountered together with strategies for overcoming these (see Knowlton et al., von Recklinghausen et al., Welch et al., and Taenzer et al.). Next, we illustrate how the Data Trust can support emerging questions relevant to member organizations. The paper by Albritton et al., explores the impact of observation stays on readmission rates. Knighton et al., explore the use of an area-based measure for health literacy to assess risk in disadvantaged populations. Two final papers illustrate the importance of fundamental data sources needed to support advanced data science.

Objective: To evaluate the short-term effectiveness of the Intermountain Healthcare (IH) Diabetes Prevention Program (DPP) for patients with prediabetes (preDM) deployed within primary care clinics., Study Design: A quasi-experimental study design was used to deploy the DPP within the IH system to identify patients with preDM and target a primary goal of a 5% weight loss within 6-12 months of enrollment., Study Population: Adults (aged 18-75 years) who met the American Diabetes Association criteria for preDM were included for study. Patients who attended DPP counseling between August 2013 and July 2014 were considered as the intervention (or DPP) group. The DPP group was matched using propensity scores at a 1:4 ratio with a control group of patients with preDM who did not participate in DPP., Results: Of the 17,142 patients who met the inclusion criteria for preDM, 40% had an in-person office visit with their provider. On average, patients were 58 years old, and greater than 60% were women. Based on multivariate logistic regression, the DPP group was more likely to achieve a 5% weight loss within 6-12 months after enrollment (OR = 1.70; 95% CI = 1.29-2.25; p < .001) when compared with the no-DPP group., Conclusions: Diabetes Prevention Program-based lifestyle interventions demonstrated significant reduction in body weight and incident Type 2 diabetes mellitus when compared with nonenrollees.

Introduction: Evaluation of interventions can help to close the gap between research and practice but seldom takes place during implementation. Using the RE-AIM framework, we conducted a formative evaluation of the first year of the Intermountain Healthcare Diabetes Prevention Program (DPP)., Methods: Adult patients who met the criteria for prediabetes (HbA1c of 5.70%-6.49% or fasting plasma glucose of 100-125 mg/dL) were attributed to a primary care provider from August 1, 2013, through July 31, 2014. Physicians invited eligible patients to participate in the program during an office visit. We evaluated 1) reach, with data on patient eligibility, participation, and representativeness; 2) effectiveness, with data on attaining a 5% weight loss; 3) adoption, with data on providers and clinics that referred patients to the program; and 4) implementation, with data on patient encounters. We did not measure maintenance., Results: Of the 6,862 prediabetes patients who had an in-person office visit with their provider, 8.4% of eligible patients enrolled. Likelihood of participation was higher among patients who were female, aged 70 years or older, or overweight; had depression and higher weight at study enrollment; or were prescribed metformin. DPP participants were more likely than nonparticipants to achieve a 5% weight loss (odds ratio, 1.70; 95% confidence interval, 1.29-2.25; P < .001). Providers from 7 of 8 regions referred patients to the DPP; 174 providers at 53 clinics enrolled patients. The mean number of DPP counseling encounters per patient was 2.3 (range, 1-16)., Conclusion: The RE-AIM framework was useful for estimating the formative impact (ie, reach, effectiveness, adoption, and implementation fidelity) of a DPP-based lifestyle intervention deployed in a learning health care system.

Improving 30-day readmission continues to be problematic for most hospitals. This study reports the creation and validation of sex-specific inpatient (i) heart failure (HF) risk scores using electronic data from the beginning of inpatient care for effective and efficient prediction of 30-day readmission risk., Methods: HF patients hospitalized at Intermountain Healthcare from 2005 to 2012 (derivation: n=6079; validation: n=2663) and Baylor Scott & White Health (North Region) from 2005 to 2013 (validation: n=5162) were studied. Sex-specific iHF scores were derived to predict post-hospitalization 30-day readmission using common HF laboratory measures and age. Risk scores adding social, morbidity, and treatment factors were also evaluated., Results: The iHF model for females utilized potassium, bicarbonate, blood urea nitrogen, red blood cell count, white blood cell count, and mean corpuscular hemoglobin concentration; for males, components were B-type natriuretic peptide, sodium, creatinine, hematocrit, red cell distribution width, and mean platelet volume. Among females, odds ratios (OR) were OR=1.99 for iHF tertile 3 vs. 1 (95% confidence interval [CI]=1.28, 3.08) for Intermountain validation (P-trend across tertiles=0.002) and OR=1.29 (CI=1.01, 1.66) for Baylor patients (P-trend=0.049). Among males, iHF had OR=1.95 (CI=1.33, 2.85) for tertile 3 vs. 1 in Intermountain (P-trend <0.001) and OR=2.03 (CI=1.52, 2.71) in Baylor (P-trend < 0.001). Expanded models using 182-183 variables had predictive abilities similar to iHF., Conclusions: Sex-specific laboratory-based electronic health record-delivered iHF risk scores effectively predicted 30-day readmission among HF patients. Efficient to calculate and deliver to clinicians, recent clinical implementation of iHF scores suggest they are useful and useable for more precise clinical HF treatment., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Understanding costs and ensuring that we demonstrate value in healthcare is a foundational presumption as we transform the way we deliver and pay for healthcare in the U.S. With a focus on population health and payment reforms underway, there is increased pressure to examine cost-effectiveness in healthcare delivery. Cost-effectiveness analysis (CEA) is a type of economic analysis comparing the costs and effects (i.e. health outcomes) of two or more treatment options. The result is expressed as a ratio where the denominator is the gain in health from a measure (e.g. years of life or quality-adjusted years of life) and the numerator is the incremental cost associated with that health gain. For higher cost interventions, the lower the ratio of costs to effects, the higher the value. While CEA is not new, the approach continues to be refined with enhanced statistical techniques and standardized methods. This article describes the CEA approach and also contrasts it to optional approaches, in order for readers to fully appreciate caveats and concerns. CEA as an economic evaluation tool can be easily misused owing to inappropriate assumptions, over reliance, and misapplication. Twelve issues to be considered in using CEA results to drive healthcare delivery decision-making are summarized. Appropriately recognizing both the strengths and the limitations of CEA is necessary for informed resource allocation in achieving the maximum value for healthcare services provided., Competing Interests: The authors declare that they have no competing interests. No competing interests were disclosed. No competing interests were disclosed.

Objective: To determine the risk of type 2 diabetes (T2DM) diagnosis among patients with confirmed and unconfirmed prediabetes (preDM) relative to an at-risk group receiving care from primary care physicians over a 5-year period., Study Design: Utilizing data from the Intermountain Healthcare (IH) Enterprise Data Warehouse (EDW) from 2006-2013, we performed a prospective analysis using discrete survival analysis to estimate the time to diagnosis of T2DM among groups., Population Studied: Adult patients who had at least one outpatient visit with a primary care physician during 2006-2008 at an IH clinic and subsequent visits through 2013. Patients were included for the study if they were (a) at-risk for diabetes (BMI ≥ 25 kg/m2 and one additional risk factor: high risk ethnicity, first degree relative with diabetes, elevated triglycerides or blood pressure, low HDL, diagnosis of gestational diabetes or polycystic ovarian syndrome, or birth of a baby weighing >9 lbs); or (b) confirmed preDM (HbA1c ≥ 5.7-6.49% or fasting blood glucose 100-125 mg/dL); or (c) unconfirmed preDM (documented fasting lipid panel and glucose 100-125 mg/dL on the same day)., Principal Findings: Of the 33,838 patients who were eligible for study, 57.0% were considered at-risk, 38.4% had unconfirmed preDM, and 4.6% had confirmed preDM. Those with unconfirmed and confirmed preDM tended to be Caucasian and a greater proportion were obese compared to those at-risk for disease. Patients with unconfirmed and confirmed preDM tended to have more prevalent high blood pressure and depression as compared to the at-risk group. Based on the discrete survival analyses, patients with unconfirmed preDM and confirmed preDM were more likely to develop T2DM when compared to at-risk patients., Conclusions: Unconfirmed and confirmed preDM are strongly associated with the development of T2DM as compared to patients with only risk factors for disease.

Background: Adverse drug event (ADE) detection is an important priority for patient safety research. Trigger tools have been developed to help identify ADEs. In previous work we developed seven concurrent, action-oriented, electronic trigger algorithms designed to prompt clinicians to address ADEs in outpatient care., Objectives: We assessed the potential adoption and usefulness of the seven triggers by testing the positive predictive validity and obtaining stakeholder input., Methods: We adapted ADE triggers, "bone marrow toxin-white blood cell count (BMT-WBC)," "bone marrow toxin - platelet (BMT-platelet)," "potassium raisers," "potassium reducers," "creatinine," "warfarin," and "sedative hypnotics," with logic to suppress flagging events with evidence of clinical intervention and applied the triggers to 50,145 patients from three large health care systems. Four pharmacists assessed trigger positive predictive value (PPV) with respect to ADE detection (conservatively excluding ADEs occurring during clinically appropriate care) and clinical usefulness (i.e., whether the trigger alert could change care to prevent harm). We measured agreement between raters using the free kappa and assessed positive PPV for the trigger's detection of harm, clinical usefulness, and both. Stakeholders from the participating health care systems rated the likelihood of trigger adoption and the perceived ease of implementation., Findings: Agreement between pharmacist raters was moderately high for each ADE trigger (kappa free > 0.60). Trigger PPVs for harm ranged from 0 (Creatinine, BMT-WBC) to 17 percent (potassium raisers), while PPV for care change ranged from 0 (WBC) to 60 percent (Creatinine). Fifteen stakeholders rated the triggers. Our assessment identified five of the seven triggers as good candidates for implementation: Creatinine, BMT-Platelet, Potassium Raisers, Potassium Reducers, and Warfarin., Conclusions: At least five outpatient ADE triggers performed well and merit further evaluation in outpatient clinical care. When used in real time, these triggers may promote care changes to ameliorate patient harm.

Purpose: The purpose of this study was to determine the impact of diabetes self-management education (DSME) in improving processes and outcomes of diabetes care as measured by a five component diabetes bundle and HbA1c, in individuals with type 2 diabetes mellitus (T2DM)., Methods: A retrospective analysis was performed for adult T2DM patients who received DSME training in 2011-2012 from an accredited American Diabetes Association center at Intermountain Healthcare (IH) and had an HbA1c measurement within the prior 3 months and 2-6 months after completing their first DSME visit. Control patients were selected from the same clinics as case-patients using random number generator to achieve a 1 to 4 ratio. Case and control patients were included if 1) pre-education HbA1c was between 6.0%-14.0%; 2) their main provider was a primary care physician; 3) they met the national Healthcare Effectiveness Data and Information Set criteria for inclusion in the IH diabetes registry. The IH diabetes bundle includes retinal eye exam, nephropathy screening or prescription of angiotensin converting enzyme or angiotensin receptor blocker; blood pressure <140/90 mmHg, LDL <100 mg/dL, HbA1c <8.0%., Results: DSME patients had a significant difference in achievement of the five element IH diabetes bundle and in HbA1c % compared to those without DSME. After adjusting for possible confounders in a multivariate logistic regression model, DSME patients had a 1.5 fold difference in improvement in their diabetes bundle and almost a 3 fold decline in HbA1c compared to the control group., Conclusion: Standardized DSME taught within an IH American Diabetes Association center is strongly associated with a substantial improvement in patients meeting all five elements of a diabetes bundle and a decline in HbA1c beyond usual care. Given the low operating cost of the DSME program, these results strongly support the value adding benefit of this program in treating T2DM patients.

Background: Algorithms leveraging electronic data may reduce manual review burden for surgical site infection (SSI) surveillance with little to no reduction in sensitivity. We developed an algorithm to identify colon and open reduction of fracture (FX) SSIs to reduce manual chart review., Methods: A retrospective cohort of colon and FX procedures and associated SSIs was constructed. Potential SSIs were identified by positive microbiologic cultures or administrative data for diagnosis or treatment of wound infection. Sensitivity and specificity of the algorithm were assessed. The number of charts needing review to identify 1 SSI, and the potential time-savings from the algorithm, were calculated., Results: Four hundred seventy-three colon (SSI rate = 7%) and 1081 FX (SSI rate = 3%) procedures were identified. The algorithm was 91% and 97% sensitive and 76% and 93% specific for colon and FX procedures, respectively. Overall, chart review would have been reduced by 24.3 hours per 100 procedures, decreasing the number of charts to review to identify 1 SSI from 23.9 for manual review to 3.9 with the algorithm., Conclusions: The algorithm identified SSIs with excellent sensitivity and specificity, resulting in substantial reductions in manual chart review. This algorithm could be tailored and applied to other hospitals., (Copyright © 2014 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)

Objective: To compare the healthcare use by children with and without a traumatic brain injury (TBI) in the year following injury to understand whether children access primary care., Participants: Children 0 to 15 years with a TBI (N = 545) and (N = 2310) uninjured age and sex-matched comparisons., Setting: A full benefits healthcare plan from 2000 to 2007., Main Measures: Mean annual healthcare utilization., Results: Children with TBI had higher mean annual outpatient visits (4.2 vs. 3.5, P = .001), but similar mean annual general pediatric visits (2.7 vs. 2.8, P = .3) than comparison children. More cases than comparisons attended a general pediatric visit (80.0% vs. 73.3%, risk ratio = 1.1, 95% CI: 1.0-1.1). However, approximately 50% of children older than 7 years who had an intracranial injury did not attend a general pediatric visit and those were slightly more likely to receive specialty care (adjusted risk ratio = 1.1, 95% CI: 1.0-1.2). These children did not appear to be substituting specialty for primary care., Conclusions: Children with a full benefits insurance plan do not access primary care routinely after TBI. These findings present a challenge for designing a system to screen children after TBI.

Background: Emergency departments (EDs) are an important source of care for a large segment of the population of the United States. In 2009 there were more than 136 million visits to the ED each year, and more than half of hospital admissions begin in the ED. Measurement and monitoring of emergency department performance has been prompted by The Joint Commission's patient flow standards. A study was conducted to attempt to correlate ED volume and other operating characteristics with performance on metrics., Methods: A retrospective analysis of the Emergency Department Benchmarking Alliance annual ED survey data for the most recent year for which data were available (2009) was performed to explore observed patterns in ED performance relative to size and operating characteristics. The survey was based on 14.6 million ED visits in 358 hospitals across the United States, with an ED size representation (sampling) approximating that of the Emergency Medicine Network (EM Net)., Results: Larger EDs (with higher annual volumes) had longer lengths of stay (p < .0001), higher left without being seen rates (p < .0001), and longer door-to-physician times (p < .0001), all suggesting poorer operational performance. Operating characteristics indicative of higher acuity were associated with worsened performance on metrics and lower acuity characteristics with improved performance., Conclusion: ED volume, which also correlates with many operating characteristics, is the strongest predictor of operational performance on metrics and can be used to categorize EDs for comparative analysis. Operating characteristics indicative of acuity also influence performance. The findings suggest that ED performance measures should take ED volume, acuity, and other characteristics into account and that these features have important implications for ED design, operations, and policy decisions.

Members of a consortium of leading US health care systems, known as the High Value Healthcare Collaborative, used administrative data to examine differences in their delivery of primary total knee replacement. The goal was to identify opportunities to improve health care value by increasing the quality and reducing the cost of that procedure. The study showed substantial variations across the participating health care organizations in surgery times, hospital lengths-of-stay, discharge dispositions, and in-hospital complication rates. The study also revealed that higher surgeon caseloads were associated with shorter lengths-of-stay and operating time, as well as fewer in-hospital complications. These findings led the consortium to test more coordinated management for medically complex patients, more use of dedicated teams, and a process to improve the management of patients' expectations. These innovations are now being tried by the consortium's members to evaluate whether they increase health care value.

It has been estimated that full implementation of the Affordable Care Act will extend coverage to thirty-two million previously uninsured Americans. However, rapidly rising health care costs could thwart that effort. Since 1988 Intermountain Healthcare has applied to health care delivery the insights of W. Edwards Deming's process management theory, which says that the best way to reduce costs is to improve quality. Intermountain achieved such quality-based savings through measuring, understanding, and managing variation among clinicians in providing care. Intermountain created data systems and management structures that increased accountability, drove improvement, and produced savings. For example, a new delivery protocol helped reduce rates of elective induced labor, unplanned cesarean sections, and admissions to newborn intensive care units. That one protocol saves an estimated $50 million in Utah each year. If applied nationally, it would save about $3.5 billion. "Organized care" along these lines may be central to the long-term success of health reform.

Objective: The objective of this study is to evaluate the performance of 5 triggers to detect adverse events (AEs) associated with outpatient surgery. Triggers use surveillance algorithms derived from clinical logic to flag cases where AEs have most likely occurred. Current efforts to detect AEs have focused primarily on the inpatient setting, despite the increase in outpatient surgery in all health care settings., Methods: Using trigger logic, we retrospectively evaluated data from 3 large health care systems' electronic medical records. Patients were eligible for inclusion if they had an outpatient (same-day) surgery in 2007 and at least 1 clinical note in the 6 months after the surgery. Two nurse abstractors reviewed a sample of trigger-flagged cases from each health care system. After reaching interrater reliability targets (κ > 0.60), we calculated the positive predictive value (PPV) of each trigger and the confidence interval of the estimate., Results: The surgical triggers flagged between 1% and 22% of the outpatient surgery cases, with a wide range in PPVs (6.0%-62.0%). The pulmonary embolism and deep vein thrombosis and emergency department triggers had the lowest proportion of flagged cases along with the highest PPVs, showing the most promise for screening cases with a high probability of AE occurrence., Conclusions: Triggers may be useful in identifying a narrow set of surgeries for further review to determine if a surgical AE occurred, complementing existing tools and initiatives used to detect AEs. Improved detection of AEs in outpatient surgery should help target potential areas for quality improvement.

Intermountain Healthcare is a high-performing health system and a recognized leader in quality improvement. We use a clinical integration strategy focused on eight clinical programs to support the practice of evidence-based care. Accelerated improvements that enhance patient safety, clinical excellence, and operational efficiency are tested and then spread across the system via care process models and program-specific board goals. While we have nearly 60 evidence-based care process models in place (in addition to multiple operational effectiveness initiatives), we provide three exemplars to illustrate cost savings and the relative impact on hospital/medical group versus payer benefit. These clinical best practices include very early lung recruitment (VE LR) for neonates with respiratory distress syndrome, guidelines for elective inductions in labor and delivery, and prevention of congestive heart failure (CHF) readmissions. Due to perverse incentives in the third party payment system--where healthcare providers are often paid to do more tests and treatments as opposed to providing clinical value--doing what's right for our patients commonly yields savings to our payers while negatively impacting the delivery system budget. In this article, we present a suggested strategy for negotiated capture of these savings.

Guidelines recommend intrapartum antibiotic prophylaxis (IAP) for parturient women who have a screen positive for group B Streptococcus (GBS). Clindamycin should be used for IAP only if the maternal GBS isolate is susceptible. We report a case of clindamycin-resistant GBS disease in a newborn infant whose mother received clindamycin IAP, and we review clindamycin susceptibility testing., (Copyright 2010 Mosby, Inc. All rights reserved.)

Most patients with mental health (MH) conditions, such as depression, receive care for their conditions from a primary care physician (PCP) in their health/medical home. Providing MH care, however, presents many challenges for the PCP, including (1) the difficulty of getting needed consultation from an MH specialist; (2) the time constraints of a busy PCP practice; (3) the complicated nature of recognizing depression, which may be described with only somatic complaints; (4) the barriers to reimbursement and compensation; and (5) associated medical and social comorbidities. Practice managers, emergency departments, and health plans are stretched to provide care for complex patients with unmet MH needs. At the same time, payment reform linked to accountable care organizations and/or episodic bundle payments, MH parity rules, and increasing MH costs to large employers and payers all highlight the critical need to identify high-quality, efficient, integrated MH care delivery practices. Over the past ten years, Intermountain Healthcare has developed a team-based approach-known as mental health integration (MHI)-for caring for these patients and their families. The team includes the PCPs and their staff, and they, in turn, are integrated with MH professionals, community resources, care management, and the patient and his or her family. The integration model goes far beyond co-location in its team-based approach; it is operationalized at the clinic, thereby improving both physician and staff satisfaction. Patients treated in MHI clinics also show improved satisfaction, lower costs, and better quality outcomes. The MHI program is financially sustainable in routinized clinics without subsidies. MHI is a successful approach to improving care for patients with MH conditions in primary care health homes.

Background: The purpose of the partnership science subcommittee's collective discussions and reactions to tools and strategies was to promote and support effective management of organizational-based, participatory research (OBPR) partnerships., Research Partnerships: Research partnerships are broadly defined to include interorganizational relationships where resources are shared to advance a collective purpose-in this case, applied, participatory research. Several major funding agencies have recognized their role in supporting research partnerships and have used a "push" strategy via earmarked funds for such activities. OPBR partnerships made possible through support can engage multiple entities to enhance the generalizability and potential for modeling and spread of findings., The Partnership Strength Survey: Effective management of the research partnership should stimulate collaborative problem solving based on organizational priorities for shared learning and spread of research results. Current surveys were drawn on to create a participant survey for continuously improving and monitoring partnership strength and synergy., Implications for Practice: The in-depth exploration of participatory research is intended to move health system-based staff from a passive to an active role in the research process and to encourage executives to support and encourage research participation.

Background: Because of uniform availability, hospital administrative data are appealing for surveillance of adverse drug events (ADEs). Expert-generated surveillance rules that rely on the presence of International Classification of Diseases, 9th Revision Clinical Modification (ICD-9-CM) codes have limited accuracy. Rules based on nonlinear associations among all types of available administrative data may be more accurate., Objectives: By applying hierarchically optimal classification tree analysis (HOCTA) to administrative data, derive and validate surveillance rules for bleeding/anticoagulation problems and delirium/psychosis., Research Design: Retrospective cohort design., Subjects: A random sample of 3987 admissions drawn from all 41 Utah acute-care hospitals in 2001 and 2003., Measures: Professional nurse reviewers identified ADEs using implicit chart review. Pharmacists assigned Medical Dictionary for Regulatory Activities codes to ADE descriptions for identification of clinical groups of events. Hospitals provided patient demographic, admission, and ICD9-CM data., Results: Incidence proportions were 0.8% for drug-induced bleeding/anticoagulation problems and 1.0% for drug-induced delirium/psychosis. The model for bleeding had very good discrimination and sensitivity at 0.87 and 86% and fair positive predictive value (PPV) at 12%. The model for delirium had excellent sensitivity at 94%, good discrimination at 0.83, but low PPV at 3%. Poisoning and adverse event codes designed for the targeted ADEs had low sensitivities and, when forced in, degraded model accuracy., Conclusions: Hierarchically optimal classification tree analysis is a promising method for rapidly developing clinically meaningful surveillance rules for administrative data. The resultant model for drug-induced bleeding and anticoagulation problems may be useful for retrospective ADE screening and rate estimation.

A "transitional care pharmacist" (TCP) was deployed within an acute care setting to identify opportunities for improved continuity of care. The provision of medication reconciliation services, drug consultation, patient counseling and planning for after-hospital care was time consuming but also fruitful, resulting in roughly nine interventions per patient. Areas with the greatest potential for morbidity reduction were the resumption of home medications during the acute stay and at discharge. Allergy identification was a key contribution at admission, as was the provision of a detailed follow-up plan at discharge. Targeting high-risk patients and spreading portions of the work to other disciplines could achieve added efficiency in this service. Results have value to hospitals implementing medication reconciliation programs.

Background: Innovations adopted by healthcare organizations are often externally mandated. However, few studies examine how mandated innovations progress from adoption to sustained effective use. This study uses Rogers's model of organizational innovation to explore community health centers' (CHCs') mandated adoption and implementation of disease registries in the federal Health Disparities Collaborative (HDC)., Methods: Case studies were conducted on six CHCs in North Carolina participating in the HDC on type 2 diabetes mellitus. Data were collected from semistructured interviews with key staff, and from site-level and individual-level surveys., Results: Although disease registry adoption and implementation were mandated, CHCs exercised prerogative in the timing of registry adoption and the functions emphasized. Executive and medical director involvement, often directly on the HDC teams, was the single most salient influence on adoption and implementation. Staff members' personal experience with diabetes also provided context and gave registries added significance. Participants lauded HDC's technique of small-scale, rapid-cycle change, but valued even more shared problem solving and peer learning among HDC teams. However, lack of cross-training, inadequate resources, and staff turnover posed serious threats to sustainability of the registries., Conclusions: The present study illustrates the usefulness of Rogers's model for studying mandated innovation and highlights several key factors, including direct, personal involvement of organizational leadership, and shared problem solving and peer learning facilitated by the HDC. However, these six CHCs elected to participate early in the HDC, and may not be typical of North Carolina's remaining CHCs. Furthermore, most face important long-term challenges that threaten routinization.

Background: Secondary and tertiary prevention of chronic illness is a major challenge for the United States healthcare system. Controlled studies show that interventions can enhance secondary prevention in primary care practices, but they shed little light on implementation of secondary prevention outside the experimental context. This study examines the adoption and implementation of an important set of secondary and tertiary prevention efforts--diabetes management strategies--for type 2 diabetes in the everyday clinical practice of primary care. It explores whether adoption and implementation processes differ by type of strategy or prevalence of diabetes among patients in the practice., Methods: Holistic case studies (those used to assess a single analytic unit, in this case, the physician group practice, as opposed to multiple embedded subunits) were conducted in 2001-2002 on six primary care practices in North Carolina identified from a statewide physician survey on strategies for diabetes management. Practices were selected by prevalence of diabetes and type of strategy for diabetes management--patient oriented (focused on self-management) versus biomedical (focused on secondary prevention practices). Results were derived from thematic analysis of interviews and secondary documents., Results: Adoption and implementation did not differ by diabetes prevalence or type of diabetes strategy. All practices had a routine forum for vetting new strategies, and most used traditional channels for identifying them. Implementation often required adaptation of the strategy and the organization. Sustained use of a diabetes strategy depended on favorable organizational policies and procedures (e.g., training, job redesign) and ongoing commitment of resources., Conclusions: Diabetes management strategies are often complex and require adoption and implementation processes different from those described by classic innovation diffusion models. Alternative conceptual models that consider organizational process, structure, and culture are needed.

Objective: We sought to describe irritable bowel syndrome (IBS) treatment among women with chronic pelvic pain., Study Design: We performed a cross-sectional study of new chronic pelvic pain patients between 1993 and 2000 (n = 987). IBS was defined by Rome I criteria. IBS treatment was defined as lower gastrointestinal drugs or referral. Analyses were descriptive and multivariable., Results: IBS occurred in 35% of patients. In the highest quartile of pain, women with IBS were not more likely to have IBS treatment initiated. In the lowest three quarters of pain, women with IBS were 5.08 times more likely to have IBS treatment initiated. IBS was not diagnosed 40% of the time. IBS treatments were not recommended to 67% of patients with IBS. More than 35% of patients were prescribed narcotics., Conclusion: IBS is not consistently diagnosed and treated even in a pelvic pain clinic. Yet, treatment of IBS may reduce the overall abdominal pain of these patients.

We examined how five integrated delivery systems make decisions about and implement clinical information systems. Using case study methods, we identified general themes and explored how organizational context factors and information technology characteristics affect adoption and implementation processes.

Traditionally, medical education, research, and practice have focused on the care of the individual but an increasing emphasis on the care of populations has raised awareness among academic medical centers, integrated delivery systems, and managed care organizations of the value of embracing population-based health principles. Five principles are relevant in this regard: a community perspective, a clinical epidemiology perspective, evidence-based practice, an emphasis on outcomes, and an emphasis on prevention. This article describes these interrelated concepts together with specific strategies to effect implementation. Widespread awareness and adoption of these principles will have a profound impact on medical and public health education, practice, and ultimately the public's health.