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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. 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. 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). 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. Unconfirmed and confirmed preDM are strongly associated with the development of T2DM as compared to patients with only risk factors for disease.

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.

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. [PUBLICATION ABSTRACT]

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.

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.

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.

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). 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%. 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. 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.

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. 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. 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. 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.

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. 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. Retrospective cohort study. 10 High Value Healthcare Collaborative (HVHC) health systems. 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. 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. 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.