Clinical Outcomes of Orthopedic Surgery Co-Management by Internal Medicine Advanced Practice Clinicians: A Cohort Study.

BACKGROUND: Comanagement of orthopedic surgery patients by internal medicine hospitalists is associated with improvements in clinical outcomes including complications, length of stay, and cost. Clinical outcomes of orthopedic comanagement performed solely by internal medicine advanced practice clinicians have not been reported. Our objecyive was to compare clinical outcomes between advanced practice clinician-based comanagement and usual orthopedic care. METHODS: This is a retrospective cohort study in patients 18 years or older, hospitalized for orthopedic joint or spine surgery between May 1, 2014 and January 1, 2022. Outcomes assessed were length of stay, intensive care unit (ICU) transfer, return to operating room, in-hospital and 30-day mortality, 30-day readmission, and total direct cost, excluding surgical implants. Generalized boosted regression and propensity score weighting was used to compare clinical outcomes and health care cost between usual care and advanced practice clinician comanagement. RESULTS: Advanced practice clinician comanagement was associated with a 5% reduction in mean length of stay (rate ratio = 0.95, P = .009), decreased odds of returning to the operating room (odds ratio [OR] 0.51, P = .002), and a significant reduction in 30-day mortality (OR 0.32, P = .037) compared with usual orthopedic care in a weighted analysis. Need for ICU transfer was higher with advanced practice clinician comanagement (OR 1.54, P = .009), without significant differences in 30-day readmission or in-hospital mortality. CONCLUSIONS: We observed reductions in length of stay, health care costs, return to the operating room, and 30-day mortality with advanced practice clinician comanagement compared with usual orthopedic care. Our findings suggest that advanced practice clinician-based comanagement may represent a safe and cost-effective model for orthopedic comanagement.

Informatics tools to implement late cardiovascular risk prediction modeling for population management of high-risk childhood cancer survivors.

BACKGROUND: Clinical informatics tools to integrate data from multiple sources have the potential to catalyze population health management of childhood cancer survivors at high risk for late heart failure through the implementation of previously validated risk calculators. METHODS: The Oklahoma cohort (n = 365) harnessed data elements from Passport for Care (PFC), and the Duke cohort (n = 274) employed informatics methods to automatically extract chemotherapy exposures from electronic health record (EHR) data for survivors 18 years old and younger at diagnosis. The Childhood Cancer Survivor Study (CCSS) late cardiovascular risk calculator was implemented, and risk groups for heart failure were compared to the Children's Oncology Group (COG) and the International Guidelines Harmonization Group (IGHG) recommendations. Analysis within the Oklahoma cohort assessed disparities in guideline-adherent care. RESULTS: The Oklahoma and Duke cohorts both observed good overall concordance between the CCSS and COG risk groups for late heart failure, with weighted kappa statistics of .70 and .75, respectively. Low-risk groups showed excellent concordance (kappa > .9). Moderate and high-risk groups showed moderate concordance (kappa .44-.60). In the Oklahoma cohort, adolescents at diagnosis were significantly less likely to receive guideline-adherent echocardiogram surveillance compared with survivors younger than 13 years old at diagnosis (odds ratio [OD] 0.22; 95% confidence interval [CI]: 0.10-0.49). CONCLUSIONS: Clinical informatics tools represent a feasible approach to leverage discrete treatment-related data elements from PFC or the EHR to successfully implement previously validated late cardiovascular risk prediction models on a population health level. Concordance of CCSS, COG, and IGHG risk groups using real-world data informs current guidelines and identifies inequities in guideline-adherent care.

Rural, Large Town, and Urban Differences in Optimal Subspecialty Follow-up and Survivorship Care Plan Documentation among Childhood Cancer Survivors.

BACKGROUND: Children with cancer from rural and nonurban areas face unique challenges. Health equity for this population requires attention to geographic disparities in optimal survivorship-focused care. METHODS: The Oklahoma Childhood Cancer Survivor Cohort was based on all patients reported to the institutional cancer registry and ≤ 18 years old at diagnosis between January 1, 2005, and September 24, 2014. Suboptimal follow-up was defined as no completed oncology-related clinic visit five to 7 years after their initial diagnosis (survivors were 7-25 years old at end of the follow-up period). The primary predictor of interest was rurality. RESULTS: Ninety-four (21%) of the 449 eligible survivors received suboptimal follow-up. There were significant differences (P = 0.01) as 36% of survivors from large towns (n = 28/78) compared with 21% (n = 20/95) and 17% (n = 46/276) of survivors from small town/isolated rural and urban areas received suboptimal follow-up, respectively. Forty-five percent of adolescents at diagnosis were not seen in the clinic compared with 17% of non-adolescents (P < 0.01). An adjusted risk ratio of 2.2 (95% confidence interval, 1.5, 3.2) was observed for suboptimal follow-up among survivors from large towns, compared with survivors from urban areas. Seventy-three percent of survivors (n = 271/369) had a documented survivorship care plan with similar trends by rurality. CONCLUSIONS: Survivors from large towns and those who were adolescents at the time of diagnosis were more likely to receive suboptimal follow-up care compared with survivors from urban areas and those diagnosed younger than thirteen. IMPACT: Observed geographic disparities in survivorship care will inform interventions to promote equitable care for survivors from nonurban areas.

Comparison of Resident, Advanced Practice Clinician, and Hospitalist Teams in an Academic Medical Center: Association With Clinical Outcomes and Resource Utilization.

BACKGROUND: Academic medical centers have expanded their inpatient medicine services with advanced practice clinicians (APCs) or nonteaching hospitalists in response to patient volumes, residency work hour restrictions, and recently, COVID-19. Reports of clinical outcomes, cost, and resource utilization differ among inpatient team structures. OBJECTIVE: Directly compare outcomes among resident, APC, and solo hospitalist inpatient general medicine teams. DESIGN: Retrospective cohort study using multivariable analysis adjusted for time of admission, interhospital transfer, and comorbidities that compares clinical outcomes, cost, and resource utilization. SUBJECTS: Patients 18 years or older discharged from an inpatient medicine service between July 2015 and July 2018 (N = 12,716). MAIN MEASURES: Length of stay (LOS), 30-day readmission, inpatient mortality, normalized total direct cost, discharge time, and consultation utilization. KEY RESULTS: Resident teams admitted fewer patients at night (32.0%; P < .001) than did APC (49.5%) and hospitalist (48.6%) teams. APCs received nearly 4% more outside transfer patients (P = .015). Hospitalists discharged patients 26 minutes earlier than did residents (mean hours after midnight [95% CI], 14.58 [14.44-14.72] vs 15.02 [14.97-15.08]). Adjusted consult utilization was 15% higher for APCs (adjusted mean consults per admission [95% CI], 1.00 [0.96-1.03]) and 8% higher for residents (0.93 [0.90-0.95]) than it was for hospitalists (0.85 [0.80-0.90]). No differences in LOS, readmission, mortality, or cost were observed between the teams. CONCLUSION: We observed similar costs, LOS, 30-day readmission, and mortality among hospitalist, APC, and resident teams. Our results suggest clinical outcomes are not significantly affected by team structure. The addition of APC or hospitalist teams represent safe and effective alternatives to traditional inpatient resident teams.

Validity of Medical Record Abstraction and Electronic Health Record-Generated Reports to Assess Performance on Cardiovascular Quality Measures in Primary Care.

Importance: Cardiovascular disease is the leading cause of death in the United States. To improve cardiovascular outcomes, primary care must have valid methods of assessing performance on cardiovascular clinical quality measures, including aspirin use (aspirin measure), blood pressure control (BP measure), and smoking cessation counseling and intervention (smoking measure). Objective: To compare observed performance scores measured using 2 imperfect reference standard data sources (medical record abstraction [MRA] and electronic health record [EHR]-generated reports) with misclassification-adjusted performance scores obtained using bayesian latent class analysis. Design, Setting, and Participants: This cross-sectional study used a subset of the 2016 aspirin, BP, and smoking performance data from the Healthy Hearts for Oklahoma Project. Each clinical quality measure was calculated for a subset of a practice's patient population who can benefit from recommended care (ie, the eligible population). A random sample of 380 eligible patients were included for the aspirin measure; 126, for the BP measure; and 115, for the smoking measure. Data were collected from 21 primary care practices belonging to a single large health care system from January 1 to December 31, 2018, and analyzed from February 21 to April 17, 2019. Main Outcomes and Measures: The main outcomes include performance scores for the aspirin, BP, and smoking measures using imperfect MRA and EHRs and estimated through bayesian latent class models. Results: A total of 621 eligible patients were included in the analysis. Based on MRA and EHR data, observed aspirin performance scores were 76.0% (95% bayesian credible interval [BCI], 71.5%-80.1%) and 74.9% (95% BCI, 70.4%-79.1%), respectively; observed BP performance scores, 80.6% (95% BCI, 73.2%-86.9%) and 75.1% (95% BCI, 67.2%-82.1%), respectively; and observed smoking performance scores, 85.7% (95% BCI, 78.6%-91.2%) and 75.4% (95% BCI, 67.0%-82.6%), respectively. Misclassification-adjusted estimates were 74.9% (95% BCI, 70.5%-79.1%) for the aspirin performance score, 75.0% (95% BCI, 66.6%-82.5%) for the BP performance score, and 83.0% (95% BCI, 74.4%-89.8%) for the smoking performance score. Conclusions and Relevance: Ensuring valid performance measurement is critical for value-based payment models and quality improvement activities in primary care. This study found that extracting information for the same individuals using different data sources generated different performance score estimates. Further research is required to identify the sources of these differences.

Variation and Change Over Time in PROMIS-29 Survey Results Among Primary Care Patients With Type 2 Diabetes.

PURPOSE: We sought to describe results of patient-reported outcome measures implemented among primary care patients with diabetes and explore factors associated with changes in scores over time. METHODS: Two organizations serving diverse patient populations collected the PROMIS-29 survey at baseline and 3-month follow-up for patients with type 2 diabetes. Bayesian regression analysis was used to examine the relationship between patient characteristics and changes in PROMIS-29 scores. Exploratory analyses assessed relationships between goal-setting and changes in scores. RESULTS: The study population reported substantially more problems with physical functioning (mean: 42.5 at Site 1 and 38.9 at Site 2) and pain interference (mean: 58.0 at Site 1 and 61.1 at Site 2) compared to the general population (mean: 50; standard deviation: 10). At least 33% of patients had a clinically meaningful change (ie, at least half the standard deviation, or 5 points) in each PROMIS domain. For pain interference, 55% had no change, 22% improved by 5 or more points, and 23% worsened by 5 or more points. Bayesian regression analyses suggest that chronic conditions, insurance status, and Hispanic ethnicity are likely associated with decreased functioning over time. Exploratory analyses found that setting a mental health goal did not appear to be associated with improvement for anxiety or depression. CONCLUSIONS: Use of patient-reported outcome measures in routine clinical care identified areas of functional limitations among people with diabetes. However, changes in participants' PROMIS-29 scores over time were minimal. Research is needed to understand patterns of change in global and domain-specific functioning, particularly among racial/ethnic minorities.

Implementation of the PROMIS-29 in Routine Care for People With Diabetes: Challenges and Opportunities.

Using patient-reported outcome measures (PROMs) in care planning has the potential to improve care, but information about routine implementation in settings serving disadvantaged groups is needed. Two primary care clinics serving populations predominantly eligible for Medicaid and diverse in race/ethnicity implemented the PROMIS-29 as part of clinical care planning. Of the target population with diabetes, 26% (n = 490) completed the PROMs; the proportion that set a goal based on the PROMs differed by site. This report describes factors influencing the PROMs process and the results of interviews with patients and members of the care team about PROMs' implementation and impact.

An Analysis of Medication Adherence of Sooner Health Access Network SoonerCare Choice Patients.

Medication adherence is a desirable but rarely available metric in patient care, providing key insights into patient behavior that has a direct effect on a patient's health. In this research, we determine the medication adherence characteristics of over 46,000 patients enrolled in the Sooner Health Access Network (HAN), based on Medicaid claims data from the Oklahoma Health Care Authority. We introduce a new measure called Specific Medication PDC (smPDC), based on the popular Proportion of Days Covered (PDC) method, using the last fill date for the end date of the measurement duration. The smPDC method is demonstrated by calculating medication adherence across the eligible patient population, for relevant subpopulations over a two-year period spanning 2012 - 2013. We leverage a clinical analytics platform to disseminate adherence measurements to providers. Aggregate results demonstrate that the smPDC method is relevant and indicates potential opportunities for health improvement for certain population segments.

The cost of information technology-enabled diabetes management.

As a result of the high cost of diabetes, an array of interventions for managing this disease has been developed. Estimating the cost of various approaches to diabetes disease management is critical to inform purchasing decisions. This review focuses on 5 provider- and payer-sponsored diabetes management approaches that use information technology (IT) and provides cost estimates for each approach based on a literature review and interviews with 38 provider practices, hospitals, payers, and vendors. Cost estimates are reported for "typical" small, medium, and large provider practices and payers. Provider-sponsored diabetes registries are estimated to be the least expensive approach for small and medium sized practices. For large practices with electronic health record systems, modifying such systems with diabetes-specific clinical decision support capabilities is projected to be the most economical approach. While limited data prevented the inclusion of all implementation costs, these projections serve as a starting point to inform the purchasing decisions of organizations planning to introduce IT-enabled diabetes management.

Crossing the evidence chasm: building evidence bridges from process changes to clinical outcomes.

OBJECTIVE: Although demand for information about the effectiveness and efficiency of health care information technology grows, large-scale resource-intensive randomized controlled trials of health care information technology remain impractical. New methods are needed to translate more commonly available clinical process measures into potential impact on clinical outcomes. DESIGN: The authors propose a method for building mathematical models based on published evidence that provides an evidence bridge between process changes and resulting clinical outcomes. This method combines tools from systematic review, influence diagramming, and health care simulations. MEASUREMENTS: The authors apply this method to create an evidence bridge between retinopathy screening rates and incidence of blindness in diabetic patients. RESULTS: The resulting model uses changes in eye examination rates and other evidence-based population parameters to generate clinical outcomes and costs in a Markov model. CONCLUSION: This method may serve as an alternative to more expensive study designs and provide useful estimates of the impact of health care information technology on clinical outcomes through changes in clinical process measures.

Benefits of information technology-enabled diabetes management.

OBJECTIVE: To determine the financial and clinical benefits of implementing information technology (IT)-enabled disease management systems. RESEARCH DESIGN AND METHODS: A computer model was created to project the impact of IT-enabled disease management on care processes, clinical outcomes, and medical costs for patients with type 2 diabetes aged >25 years in the U.S. Several ITs were modeled (e.g., diabetes registries, computerized decision support, remote monitoring, patient self-management systems, and payer-based systems). Estimates of care process improvements were derived from published literature. Simulations projected outcomes for both payer and provider organizations, scaled to the national level. The primary outcome was medical cost savings, in 2004 U.S. dollars discounted at 5%. Secondary measures include reduction of cardiovascular, cerebrovascular, neuropathy, nephropathy, and retinopathy clinical outcomes. RESULTS: All forms of IT-enabled disease management improved the health of patients with diabetes and reduced health care expenditures. Over 10 years, diabetes registries saved $14.5 billion, computerized decision support saved $10.7 billion, payer-centered technologies saved $7.10 billion, remote monitoring saved $326 million, self-management saved $285 million, and integrated provider-patient systems saved $16.9 billion. CONCLUSIONS: IT-enabled diabetes management has the potential to improve care processes, delay diabetes complications, and save health care dollars. Of existing systems, provider-centered technologies such as diabetes registries currently show the most potential for benefit. Fully integrated provider-patient systems would have even greater potential for benefit. These benefits must be weighed against the implementation costs.

Efficacy of antiretroviral therapy programs in resource-poor settings: a meta-analysis of the published literature.

BACKGROUND: Despite the advent of effective combination antiretroviral drug therapy (ART) for the treatment of human immunodeficiency virus (HIV) infection, many doubt the feasibility of ART treatment programs in resource-poor settings. We performed a meta-analysis of the efficacy of ART programs in the developing world. We searched the Medline database with the index terms "HIV," "antiretroviral therapy," "CD4 count," "viral load," "experience," and "outcomes." A total of 201 abstracts were reviewed, and 25 articles were selected for detailed review. Ten observational studies with details on patient outcomes were ultimately included in the analysis. METHODS: Three readers independently extracted data from the articles. The details recorded included patient demographic characteristics, baseline CD4 cell counts, baseline HIV RNA viral loads, ART histories, outcomes, and timing of the outcome measure. RESULTS: The proportion of subjects with an undetectable HIV viral load provided the measure of treatment efficacy. A random-effects model weighted the proportion of patients with undetectable viral load at various time points during ART. The proportion was 0.697 (95% CI, 0.582-0.812) at month 6 and 0.573 (95% CI, 0.432-0.715) at month 12 of ART. The provision of medications free of charge to the patient was associated with a 29%-31% higher probability of having an undetectable viral load at months 6 and 12 than was the requirement that patients pay part or all of the cost of therapy. CONCLUSIONS: ART treatment programs in resource-poor settings have efficacy rates similar to those reported for developed countries. The provision of medications free of charge to the patient is associated with a significantly increased probability of virologic suppression at months 6 and 12 of ART.