Measuring Quality Of Care Research Articles

Impact of Socioeconomic Deprivation on Care Quality and Surgical Outcomes for Early-Stage Non-Small Cell Lung Cancer in United States Veterans.

Background: Socioeconomic deprivation has been associated with higher lung cancer risk and mortality in non-Veteran populations. However, the impact of socioeconomic deprivation on outcomes for non-small cell lung cancer (NSCLC) in an integrated and equal-access healthcare system, such as the Veterans Health Administration (VHA), remains unclear. Hence, we investigated the impact of area-level socioeconomic deprivation on access to care and postoperative outcomes for early-stage NSCLC in United States Veterans. Methods: We conducted a retrospective cohort study of patients with clinical stage I NSCLC receiving surgical treatment in the VHA between 1 October 2006 and 30 September 2016. A total of 9704 Veterans were included in the study and assigned an area deprivation index (ADI) score, a measure of socioeconomic deprivation incorporating multiple poverty, education, housing, and employment indicators. We used multivariable analyses to evaluate the relationship between ADI and postoperative outcomes as well as adherence to guideline-concordant care quality measures (QMs) for stage I NSCLC in the preoperative (positron emission tomography [PET] imaging, appropriate smoking management, pulmonary function testing [PFT], and timely surgery [≤12 weeks after diagnosis]) and postoperative periods (appropriate surveillance imaging, smoking management, and oncology referral). Results: Compared to Veterans with low socioeconomic deprivation (ADI ≤ 50), those residing in areas with high socioeconomic deprivation (ADI > 75) were less likely to have timely surgery (multivariable-adjusted odds ratio [aOR] 0.832, 95% confidence interval [CI] 0.732-0.945) and receive PET imaging (aOR 0.592, 95% CI 0.502-0.698) and PFT (aOR 0.816, 95% CI 0.694-0.959) prior to surgery. In the postoperative period, Veterans with high socioeconomic deprivation had an increased risk of 30-day readmission (aOR 1.380, 95% CI 1.103-1.726) and decreased odds of meeting all postoperative care QMs (aOR 0.856, 95% CI 0.750-0.978) compared to those with low socioeconomic deprivation. There was no association between ADI and overall survival (adjusted hazard ratio [aHR] 0.984, 95% CI 0.911-1.062) or cumulative incidence of cancer recurrence (aHR 1.047, 95% CI 0.930-1.179). Conclusions: Our results suggest that Veterans with high socioeconomic deprivation have suboptimal adherence to care QMs for stage I NSCLC yet do not have inferior long-term outcomes after curative-intent resection. Collectively, these findings demonstrate the efficacy of an integrated, equal-access healthcare system in mitigating disparities in lung cancer survival that are frequently present in other populations. Future VHA policies should continue to target increasing adherence to QMs and reducing postoperative readmission for socioeconomically disadvantaged Veterans with early-stage NSCLC.

Leverage machine learning to identify key measures in hospital operations management: a retrospective study to explore feasibility and performance of four common algorithms

BackgroundMeasures in operations management are pivotal for monitoring and assessing various aspects of hospital performance. Existing literature highlights the importance of regularly updating key management measures to reflect changing trends and organizational goals. Advancements in machine learning (ML) have presented promising opportunities for enhancing the process of updating operations management measures. However, their specific application and performance remain relatively unexplored. We aimed to investigate the feasibility and effectiveness of using common ML techniques to identify and update key measures in hospital operations management.MethodsHistorical data on 43 measures on financial balance and quality of care under 4 categories were retrieved from the BI system of a regional health system in Central China. The dataset included 17 surgical and 15 non-surgical departments over 48 months. Four common ML techniques, linear models (LM), random forest (RF), partial least squares (PLS), and neural networks (NN), were used to identify the most important measures. Ordinary least square was employed to investigate the impact of the top 10 measures. A ground truth validation compared the ML-identified key measures against the humanly decided strategic measures from annual meeting minutes.ResultsFor financial balancing, inpatient treatment revenue was an important measure in 3/4 years, followed by equipment depreciation costs. The measures identified using the same technique differed between years, though RF and PLS yielded relatively consistent results. For quality of care, none of the ML-identified measures repeated over the years. Those consistently important over four years differed almost entirely among four techniques. On ground truth validation, the 2016–2019 ML-identified measures were among the humanly identified measures, with the exception of equipment depreciation from the 2019 dataset. All the ML-identified measures for quality of care failed to coincide with the humanly decided measures.ConclusionsUsing ML to identify key hospital operational measures is viable but performance of ML techniques vary considerably. RF performs best among the four techniques in identifying key measures in financial balance. None of the ML techniques seem effective for identifying quality of care measures. ML is suggested as a decision support tool to remind and inspire decision-makers in certain aspects of hospital operations management.

A Quality Improvement Initiative for Inpatient Advance Care Planning

The Centers for Medicare & Medicaid Services (CMS) implemented advance care planning (ACP) billing codes in 2016 to encourage practitioners to conduct and document ACP conversations, and included ACP as a quality metric in the CMS Bundled Payments for Care Improvement Initiative in 2018. Use of this billing code in the inpatient setting has not been studied. To determine whether a quality improvement intervention to increase inpatient ACP is effective in increasing ACP billing rates or changing hospital treatment plans or patient outcomes. This nationwide cohort study and difference-in-differences analyses compared changes in ACP billing, treatment, and outcomes in Medicare fee-for-service beneficiaries aged 65 years and older who were hospitalized and cared for by 3 different groups: practitioners employed by a national acute care staffing organization who underwent an ACP quality improvement intervention, nonintervention practitioners at the same hospital, and control group practitioners from other hospitals. Using data from the Master Beneficiary Summary File, acute care hospital stays for nonsurgical conditions were linked to Medicare enrollment, claims, and vital status data from 1-year preadmission to 1-year postadmission from 2015 to 2019. The ACP billing rates for each group were assessed for associations with 6 inpatient treatments and 4 outcomes. Data analyses were performed from January 2022 to December 2024. Billed ACP conversations, receipt of intensive care and life support (intensive care unit admission, gastrostomy tube placement, mechanical ventilation, tracheostomy), treatment limitations (newly initiated do-not-resuscitate orders) and outcomes (discharge to hospice, inpatient death, 30-day postadmission death, and 1-year postadmission death). The total study sample included 109 intervention hospitals, 1691 control hospitals, nearly 12 million Medicare fee-for-service beneficiaries aged 65 years and older, and 738 309 practitioners associated with admissions from 2016 to 2018. ACP billing rates increased more for the intervention (1.3% in preintervention to 14.0% in postintervention; P < .001) than for the nonintervention (same hospitals) and control groups (odds ratio [OR], 2.6; 95% CI, 1.7-4.1 intervention vs control). Increased ACP billing rates were significantly associated with decreased rates of inpatient death in the intervention group (OR, 0.95; 95% CI, 0.90-1.00) compared to the nonintervention (OR, 1.10; 95% CI, 1.04-1.17) and control groups (reference). All other associations were nonsignificant. This nationwide cohort study suggests that while the ACP quality initiative increased ACP billing, changes in clinical outcomes were inconsistent with the hypotheses. Further study is needed to address questions regarding confounding by unobserved measures of care quality.

Association between Unmet Social Need and Ambulatory Quality of Care for US Children

ObjectivesChildren who experience socioeconomic adversity often have worse health; however, less is known about their quality of care. We sought to evaluate the association between parent/caregiver-reported socioeconomic adversity and quality of pediatric primary, acute, and chronic ambulatory care on a national level. MethodsThis was a retrospective cohort study of 5368 representative US children (1–17 years) in the 2021 Medical Expenditure Panel Survey. Socioeconomic adversity was defined as parent/caregiver-reported food, housing, transportation, or utility insecurity in the past 12 months. Outcomes included 10 quality measures of primary, acute, and chronic care, and experience of care measured through parent/caregiver survey. We described variation in socioeconomic adversity and used multivariable regression to examine associations with quality outcomes. ResultsOne-third of parent/caregivers reported socioeconomic adversity. Food insecurity (23.6%) was most common followed by utility (19.5%), housing (15.0%), and transportation (4.7%) insecurity. Black (53.2%) and Hispanic (46.9%) parent/caregivers experienced the highest rates of socioeconomic adversity. Children with socioeconomic adversity received lower quality of care for four quality measures, including more frequent Emergency Department visits (Odds Ratio (OR)= 1.69 [95% Confidence Interval (CI): 1.28–2.23]), less favorable asthma medication ratio (OR=0.04 [95% CI: 0.01–0.31]), and less frequent well child (OR=0.73 [95% CI: 0.59–0.90]) and dental care (OR=0.76 [95% CI: 0.63–0.94], P < .05 for all). There were no statistically significant differences in experience of care. ConclusionsSocioeconomic adversity is common among US children with disproportionate impact on Black and Hispanic families. There are significant disparities in pediatric primary, acute, and chronic care quality, based on parent/caregiver-reported socioeconomic adversity, highlighting the need for systems-level interventions.

Wildfire exposures and in-hospital length of stay following lung cancer surgery.

311 Background: Wildfires pose substantial health and safety threats to patients recovering from lung cancer surgery. Without specific disaster preparedness guidelines, oncologists might resort to improvisational strategies, such as increasing the length of stay (LOS), a federal care quality measure with financial implication for hospitals, to better protect the health and safety of this medically vulnerable population. Methods: Individuals aged ≥18 years who received curative-intent lobectomy or pneumonectomy for stage I-III NSCLC between 2004 and 2021 were selected from the National Cancer Database. Exposure was defined as a Federal Emergency Management Agency (FEMA) Presidential Disaster Declaration in the county of the treatment facility between date of surgery and date of discharge from the hospital. Differences in the cumulative distribution function of LOS, defined as days between date of surgery and date of discharge from the hospital, were evaluated between exposed and propensity score matched unexposed patients, who were treated at the same facility but at a time when no disasters occurred, using the product-limit method and log-rank test. In sensitivity analysis, patients with discharge date equal to date of death (in-hospital mortality) were excluded, and the association was evaluated stratified by stage. Results: Patients exposed to a wildfire disaster declaration in the county of the treating facility had longer LOS (p&lt;0.001) than unexposed patients (9.4days compared to 7.5 days, respectively) overall and for each of the stages (I-III) for which surgery is the recommended treatment modality. In sensitivity analysis, there were no in-hospital mortality difference between exposed and unexposed patients (10.8% and 10.5%, respectively, p=0.76). Excluding patients who died in the hospital did not change the main results. Conclusions: Patients whose facility is impacted by a wildfire disaster during recovery from lung cancer surgery have longer length of stay than similar patients treated at the same facility but at times when no disaster occurred. Future studies should evaluate whether extended hospital stay improves safety and quality of surgical care. Moreover, these findings should be considered for disaster preparedness guidelines tailored to vulnerable patient population.

Automated Identification of HeartFailureWith Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing.

The lack of automated tools for measuring care quality limits the implementation of a national program to assess guideline-directed care in heart failure with reduced ejection fraction (HFrEF). The authors aimed to automate the identification of patients with HFrEF at hospital discharge, an opportunity to evaluate and improve the quality of care. The authors developed a novel deep-learning language model for identifying patients with HFrEF from discharge summaries of hospitalizations with heart failure at Yale New Haven Hospital during 2015 to 2019. HFrEF was defined by left ventricular ejection fraction<40% on antecedent echocardiography. The authors externally validated the model at Northwestern Medicine, community hospitals of Yale, and the MIMIC-III (Medical Information Mart for Intensive Care III) database. A total of 13,251 notes from 5,392 unique individuals (age 73 ± 14 years, 48% women), including 2,487 patients with HFrEF (46.1%), were used for model development (train/held-out: 70%/30%). The model achieved an area under receiver-operating characteristic curve (AUROC) of 0.97 and area under precision recall curve (AUPRC) of 0.97 in detecting HFrEF on the held-out set. The model had high performance in identifying HFrEF with AUROC=0.94 and AUPRC=0.91 on 19,242 notes from Northwestern Medicine, AUROC=0.95 and AUPRC=0.96 on 139 manually abstracted notes from Yale community hospitals, and AUROC=0.91 and AUPRC=0.92 on 146 manually reviewed notes from MIMIC-III. Model-based predictions of HFrEF corresponded to a net reclassification improvement of 60.2 ± 1.9% compared with diagnosis codes (P< 0.001). The authors developed a language model that identifies HFrEF from clinical notes with high precision and accuracy, representing a key element in automating quality assessment for individuals with HFrEF.

Electronic Health Record Nudges and Health Care Quality and Outcomes in Primary Care

Nudges have been increasingly studied as a tool for facilitating behavior change and may represent a novel way to modify the electronic health record (EHR) to encourage evidence-based care. To evaluate the association between EHR nudges and health care outcomes in primary care settings and describe implementation facilitators and barriers. On June 9, 2023, an electronic search was performed in PubMed, Embase, PsycINFO, CINAHL, and Web of Science for all articles about clinician-facing EHR nudges. After reviewing titles, abstracts, and full texts, the present review was restricted to articles that used a randomized clinical trial (RCT) design, focused on primary care settings, and evaluated the association between EHR nudges and health care quality and patient outcome measures. Two reviewers abstracted the following elements: country, targeted clinician types, medical conditions studied, length of evaluation period, study design, sample size, intervention conditions, nudge mechanisms, implementation facilitators and barriers encountered, and major findings. The findings were qualitatively reported by type of health care quality and patient outcome and type of primary care condition targeted. The Risk of Bias 2.0 tool was adapted to evaluate the studies based on RCT design (cluster, parallel, crossover). Studies were scored from 0 to 5 points, with higher scores indicating lower risk of bias. Fifty-four studies met the inclusion criteria. Overall, most studies (79.6%) were assessed to have a moderate risk of bias. Most or all descriptive (eg, documentation patterns) (30 of 38) or patient-centeredness measures (4 of 4) had positive associations with EHR nudges. As for other measures of health care quality and patient outcomes, few had positive associations between EHR nudges and patient safety (4 of 12), effectiveness (19 of 48), efficiency (0 of 4), patient-reported outcomes (0 of 3), patient adherence (1 of 2), or clinical outcome measures (1 of 7). This systematic review found low- and moderate-quality evidence that suggested that EHR nudges were associated with improved descriptive measures (eg, documentation patterns). Meanwhile, it was unclear whether EHR nudges were associated with improvements in other areas of health care quality, such as effectiveness and patient safety outcomes. Future research is needed using longer evaluation periods, a broader range of primary care conditions, and in deimplementation contexts.

Inequalities in Ambulatory Care Sensitive Conditions: An International Comparison of High-Income Countries

ObjectiveTo examine the differences in inequalities in Ambulatory Care Sensitive Conditions (ACSC) hospitalizations relative to socioeconomic status across nine countries, namely Australia, Canada, England, Finland, France, New Zealand, Spain, Switzerland, and the United States (US). ApproachThe International Collaborative on Costs, Outcomes, and Needs in Care (ICCONIC) research collaborative developed national data sets with hospitalization and sociodemographic data aggregated to small-area levels and pooled across countries using a common data model. Inequalities were assessed using both the slope index of inequality and the relative index of inequality. ResultsThis study developed a common definition and set of codes for ACSC hospitalizations that proved to be comparable across countries and regions. Consistent socioeconomic gradients were observed in all countries with higher ACSC hospitalization rates for individuals in most disadvantaged areas. The greatest difference in hospitalizations between the highest and lowest income quintiles were observed in New Zealand (1603 per 100,000), Finland (1802 per 100,000) and England (1955 per 100,000). ConclusionsThe high-income countries included in this study displayed many commonalities in inequalities in ACSC hospitalizations. As a proxy measure of primary care access and quality, the observed inequalities may implicate disparities in health care quality and access for the lowest socioeconomic status groups. ImplicationsBarriers to health care access in low socioeconomic groups remain a complex and multifaceted issue. Future research should investigate the underlying contributing factors to the observed ACSC hospitalization equity gradients, including primary care delivery model, remuneration approach, and population structure.

The maternal and newborn health eCohort to track longitudinal care quality: study protocol and survey development.

The MNH eCohort was developed to fill gaps in maternal and newborn health (MNH) care quality measurement. In this paper, we describe the survey development process, recruitment strategy, data collection procedures, survey content and plans for analysis of the data generated by the study. We also compare the survey content to that of existing multi-country tools on MNH care quality. The eCohort is a longitudinal mixed-mode (in-person and phone) survey that will recruit women in health facilities at their first antenatal care (ANC) visit. Women will be followed via phone survey until 10-12 weeks postpartum. User-reported information will be complemented with data from physical health assessments at baseline and endline, extraction from MNH cards, and a brief facility survey. The final MNH eCohort instrument is centered around six key domains of high-quality health systems including competent care (content of ANC, delivery, and postnatal care for the mother and newborn), competent systems (prevention and detection, timely care, continuity, integration), user experience, health outcomes, confidence in the health system, and economic outcomes. The eCohort combines the maternal and newborn experience and, due to its longitudinal nature, will allow for quality assessment according to specific risks that evolve throughout the pregnancy and postpartum period. Detailed information on medical and obstetric history and current health status of respondents and newborns will allow us to determine whether women and newborns at risk are receiving needed care. The MNH eCohort will answer novel questions to guide health system improvements and to fill data gaps in implementing countries.

Comparing methods for assessing the reliability of health care quality measures.

Quality measurement plays an increasing role in U.S. health care. Measures inform quality improvement efforts, public reporting of variations in quality of care across providers and hospitals, and high-stakes financial decisions. To be meaningful in these contexts, measures should be reliable and not heavily impacted by chance variations in sampling or measurement. Several different methods are used in practice by measure developers and endorsers to evaluate reliability; however, there is uncertainty and debate over differences between these methods and their interpretations. We review methods currently used in practice, pointing out differences that can lead to disparate reliability estimates. We compare estimates from 14 different methods in the case of two sets of mental health quality measures within a large health system. We find that estimates can differ substantially and that these discrepancies widen when sample size is reduced.

Conceptual and methodological recommendations for assessing the empirical validity of process measures of health care quality.

Conceptual and methodological recommendations for assessing the empirical validity of process measures of health care quality.

Suboptimal monitoring and management in patients with unrecorded stage 3 chronic kidney disease in real-world settings: Insights from REVEAL-CKD.

Clinical practice guidelines for patients with chronic kidney disease (CKD) recommend regular monitoring and management of kidney function and CKD risk factors. However, the majority of patients with stage 3 CKD lack a diagnosis code, and data on the implementation of these recommendations in the real world are limited. To assess the implementation of guideline-directed monitoring and management practices in the real world in patients with stage 3 CKD without a recorded diagnosis code. REVEAL-CKD (NCT04847531) is a multinational, observational study of patients with stage 3 CKD. Eligible patients had ≥2 consecutive estimated glomerular filtration rate (eGFR) measurements indicative of stage 3 CKD recorded >90 and ≤730 days apart, lacked an International Classification of Diseases 9/10 diagnosis code corresponding to CKD any time before and up to 6 months after the second eGFR measurement. Testing of key measures of care quality were assessed. The study included 435,971 patients from 9 countries. In all countries, the prevalence of urinary albumin-creatinine ratio and albuminuria testing was low. Angiotensin-converting enzyme inhibitor, angiotensin receptor blocker and statin prescriptions were highly variable, and sodium-glucose cotransporter-2 inhibitor prescriptions remained below 21%. Blood pressure measurements were recorded in 20.2%-89.9% of patients. Overall, a large proportion of patients with evidence of stage 3 CKD did not receive recommended, guideline-directed monitoring and management. The variability in standard of care among countries demonstrates a clear opportunity to improve monitoring and management of these patients, most likely improving long-term outcomes.

Alternative Payment Models and Performance in Federally Qualified Health Centers

ImportanceSeveral state Medicaid agencies have transitioned from traditional fee-for-service to a value-centric alternative payment model (APM) to reimburse federally qualified health centers (FQHCs). Little is known about the effects of this shift on FQHC performance.ObjectiveTo assess the association between APMs and the clinical performance, payer mix, risk profile, and financial sustainability of FQHCs.Design, Setting, and ParticipantsThis retrospective cohort study was performed in 684 FQHCs (representing 37 states plus the District of Columbia) that continuously operated between January 2009 and December 2021. Data on payer mix (eg, type of insurance) and risk profile (eg, proportion of patients with chronic conditions) of FQHC patients were obtained from the Uniform Data System, and clinic-level financial data (eg, revenue) were obtained from Internal Revenue Service form 990 tax documents. Data were analyzed between November 2022 and October 2023.ExposureInitial rollout of a value-based payment model (ie, an APM) for FQHCs, as offered by state Medicaid program, between January 2013 and December 2021.Main Outcomes and MeasuresThe main outcomes were 4 audited process measures of health care quality (cervical and colorectal cancer screening and body mass index [BMI] assessment for adults and children) and 2 intermediate health outcome measures (hypertension control and diabetes control). A difference-in-differences design was used with staggered implementation comparing FQHCs before and after the initial APM rollout vs contemporaneous changes in FQHCs in states without APMs.ResultsA total of 684 FQHCs (8892 FQHC-years) that served 17 823 959 patients in 2021 (57.3% female) were included in the study. Among FQHCs in states implementing APMs, significant differential increases in 3 of the 4 process quality measures were observed compared with FQHCs in states that did not implement an APM: colorectal cancer screening (3.24 percentage points [pp]; 95% CI, 1.40-5.08 pp), adult BMI (3.19 pp; 95% CI, 0.70-5.68 pp), and child BMI (4.50 pp; 95% CI, 1.83-7.17 pp). There were also modest differential improvements in blood pressure control for individuals with hypertension (1.02 pp; 95% CI, 0.04-2.00 pp) and blood glucose control for individuals with type 2 diabetes (1.02 pp; 95% CI, 0.02-2.02 pp) compared with FQHCs in states without an APM. There was no evidence that the APM rollout was associated with clinics selecting healthier patients (–0.01 pp; 95% CI, –0.21 to 0.19 pp) or stinting on care (–0.02 visits; 95% CI, –0.08 to 0.04 visits).Conclusions and RelevanceIn this cohort study, introduction of Medicaid APM options for FQHCs was associated with modest, statistically significant increases in quality concentrated among FQHCs with APM models that explicitly incentivized quality. This finding suggests that APMs can be both a financially viable and a health-promoting model for reimbursement in the health care safety net.

Implementation of kidney palliative care-lessons learned from the US Department of Veterans Affairs.

Advanced kidney disease is a progressive life-limiting illness associated with high symptom burden, disability, and highly intensive care near the end of life. There is growing interest in integrating palliative care principles into the care of patients with advanced kidney disease to improve care and outcomes for these patients. The United States (US) Department of Veterans Affairs (VA) has been a leader in advancing palliative care initiatives across its health system and whose experience and approach may be instructive to other health systems seeking to develop kidney palliative care (KPC) services. Herein, we review current KPC programs in the VA and highlight the different models of care that programs have been adopted and how key components of goals of care conversations and advance care planning, symptom management, multidisciplinary care, patient selection, and quality improvement have been implemented across programs. VA KPC programs have adopted "parallel", "merged", and "embedded" models of KPC that reflect the different configurations of partnerships between nephrology and palliative care providers to deliver KPC. A primary service of VA KPC programs is providing goals of care conversations and advance care planning to referred patients and systematically documenting the outcomes of these discussions in standardized note templates in the electronic medical record. Symptom management is delivered by KPC providers through regular shared or sequential visits with patients' nephrology providers and is guided by patient responses to validated symptom surveys. Programs are staffed by allied health professionals, such as chaplains, pharmacists, social workers, and dieticians, to provide whole-person care and regularly huddle with nephrology staff to reach a shared understanding of each patient's care needs and plan. KPC programs implement champions who select patients in greatest need of KPC using a combination of clinical events that trigger referral for KPC and validated mortality risk prediction scores that are automatically generated in each patient's medical record. KPC programs also routinely collect clinical, patient-reported, process, and care quality measures to assess its services. The experiences of the VA highlight novel approaches that strive to close the care gaps in meeting the KPC needs of patients with advanced kidney disease.

Dementia Ideal Care: Ecosystem Map of Best Practices and Care Pathways Enhanced by Technology and Community.

Globally, much work has been done by nonprofit, private, and academic groups to develop best practices for the care of people living with dementia (PLWD), including Alzheimer's disease. However, these best practices reside in disparate repositories and tend to focus on one phase of the patient journey or one relevant group. To fill this gap, we developed a Dementia Ideal Care Map that everyone in the dementia ecosystem can use as an actionable tool for awareness, policy development, funding, research, training, service delivery, and technology design. The intended audience includes (and not limited to) policymakers, academia, industry, technology developers, health system leaders, clinicians, social service providers, patient advocates, PLWD, their families, and communities at large. A search was conducted for published dementia care best practices and quality measures, which were then summarized in a visual diagram. The draft diagram was analyzed to identify barriers to ideal care. Then, additional processes, services, technologies, and quality measures to overcome those challenges were brainstormed. Feedback was then obtained from experts. The Dementia Ideal Care Map summarizes the ecosystem of over 200 best practices, nearly 100 technology enablers, other infrastructure, and enhanced care pathways in one comprehensive diagram. It includes psychosocial interventions, care partner support, community-based organizations; awareness, risk reduction; initial detection, diagnosis, ongoing medical care; governments, payers, health systems, businesses, data, research, and training. Dementia Ideal Care Map is a practical tool for planning and coordinating dementia care. This visualized ecosystem approach can be applied to other conditions.

KANSURVIVE: Testing a telementoring model for improving cancer survivorship care in rural practice.

e13791 Background: Cancer survivors require long-term, coordinated care to address the physical, emotional, and psychosocial effects of cancer and its treatment. KanSurvive, a single arm feasibility trial, evaluated the effectiveness of the KanSurvive ECHO intervention combined with practice facilitation (PF) to promote better adoption of evidence-based practice (EBP) survivorship care guidelines for breast, colorectal, lung, and prostate cancer among rural survivors. Methods: Fifteen primary care clinics in Kansas (80% in RUCA codes 4-10), including 62 healthcare professionals, were invited to participate in four, 1-hour Project ECHO telementoring program held in 2021. Real-time videoconferencing sessions covered EBP survivorship guidelines for breast, colorectal, lung, and prostate cancer. Clinics used brief plan-do-study-act cycles with PF supporting implementation of the quality improvement process. Utilizing the National Quality Forum framework, a performance indicator booklet with established measures in preventive health and cancer survivorship was developed as a data collection and quality improvement resource. Performance indicators included EBP screening actions related to cancer surveillance, lifestyle factors, distress, evidence of a review of family history of cancer, and the patient’s cancer diagnosis documented in the EHR. Results: Three clinics (7 = healthcare professionals) withdrew from the project citing competing demands and a provider leaving the practice. The COVID-19 pandemic delayed project initiation and prevented clinic visits. Recruitment and study procedures were implemented remotely. Clinic staff identified 138 cancer survivors (M = 13, IQR [6-16] per practice) seen in the year pre-intervention, and 129 survivors in the year prior to post-intervention. Pre-intervention, overall tumor surveillance was 91%, but lung cancer surveillance was lower than other tumor types at 70%. Other than surveillance, measures of baseline performance were similar across tumor types: screening for distress 84%, assessment of BMI 97%, screening for tobacco use 83% and documentation of family history 70%. Post-intervention performance indicators were similar except for documentation of family history which increased to 89% (p &lt; 0.01). Conclusions: Standardized survivorship metrics can facilitate quality assessment as part of an ECHO-based cancer survivorship program. Although there are modest opportunities for improvement, PCPs in this study caring for rural patients perform reasonably well across a variety of cancer survivorship performance metrics comparable to national primary care quality measures. Documented assessment of family history of cancer lags other metrics and improved after participation in the KanSurvive telementoring program.

Electronic clinical quality measure (eCQM) concordance and quality care disparities among patients with breast cancer (BC).

e23237 Background: BC mortality is decreasing steadily, yet disparities in treatment and outcomes persist. eCQMs are metrics specified in a standard electronic format using data from electronic health records (EHR) and other systems to measure care quality. CancerLinQ is a health technology platform aggregating EHR data from ~7M patients (pts) seen in US oncology practices and cancer centers. This analysis assesses odds of non-concordance with 4 BC-specific eCQMs in CancerLinQ. Methods: Pts actively treated for BC in 2021 in CancerLinQ data were included in this study. The following four practice-level eCQMs were assessed as proportions of BC pts who: eCQM1 – Undergo HER2 testing eCQM2 – Are &lt; age 70 with early-stage, hormone receptor (HR) negative BC who receive combination chemotherapy &lt; 4 months of dx eCQM3 – Have early-stage, HER2+ BC and receive trastuzumab eCQM4 – Have early-stage, HR+ BC and receive tamoxifen or an aromatase inhibitor (AI) &lt; 1-year of dx Ethnicity, race, age at dx, pt/practice rural status, and practice type were assessed as covariates. Adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) were calculated for associations among study variables and receipt of guideline non-concordant care. Results: aORs (95% CI) of pt characteristics were compared to reference groups - non-Hispanic, white, age at dx 60-69, married, urban: eCQM1 (N = 21296): For unknown ethnicity, aOR (95% CI) = 1.5 (1.3 1.6); unknown race = 1.3 (1.2, 1.5); age 70-79 = 1.1 (1.0, 1.2), age 80-89 = 1.4 (1.2, 1.5), age &gt; 90 = 1.5 (1.1, 2.2); formerly married/partnered = 1.2 (1.1, 1.3), thus having greater odds of non-concordance. Age 40-49, aOR (95% CI) = 0.9 (0.8, 0.986), had lower odds of non-concordance. eCQM2 (N = 1259): For unknown ethnicity, aOR (95% CI) = 1.9 (1.2, 3.1); Asian = 2.9 (1.7, 4.9) or other race = 2.3 (1.2, 4.1); and formerly married/partnered = 1.7 (1.2, 2.6), thus having greater odds of non-concordance. Pts age &lt; 40 = 0.6 (0.3, 0.9) had lower odds of non-concordance. eCQM3 (N = 1341): Age &gt; 80, aOR (95% CI) = 3.8 (1.5, 9.1), had greater odds of non-concordance. eCQM4 (N = 5063): For age &lt; 40, aOR (95% CI) = 2.9 (2.1, 4.1), age 40-49 = 1.6 (1.2, 2.0), and age 50-59 = 1.5 (1.2, 1.9); and formerly married/partnered = 1.3 (1.1, 1.7), thus having greater odds of non-concordance. Conclusions: As expected, pts &lt; 40 had greater odds of receiving chemotherapy and pts &gt; 80 had lower odds of receiving trastuzumab, possibly due to cardiotoxicity concerns. Although all women with early-stage HR+ BC are potential tamoxifen/AI candidates, pts &lt; 60 with early-stage HR+ BC had greater odds of not receiving tamoxifen/AI compared to those age 60-69. Additionally, divorced, separated, or widowed pts had greater odds of non-concordant care in 3 of 4 of eCQMs assessed. Our findings demonstrate the ability of eCQMs to identify clinical subgroups more likely to receive non-concordant care who may be eligible for targeted interventions.

Role of Telehealth Use in Chronic Care Management and Disparity Reduction Among the Aging Population.

Introduction: To examine telehealth use in chronic care management and disparity reduction among the aging population. Methods: This longitudinal cohort study compared the changes in chronic care quality measures among patients with and without telehealth visits during the COVID-19 pandemic relative to patients in the previous years and by patient sociodemographic subgroup. Participants were Medicare fee-for-service beneficiaries 65 years or older from an Accountable Care Organization in the Midwest United States. Three utilization-based measures included having 2+ A1C tests, breast cancer screening, and depression screening. Three outcome-based measures included A1C control, blood pressure control, and depression diagnosis. Results: During the study period, the pandemic cohort experienced 5-17 percentage points' decrease in utilization-based measures (e.g., 2+ A1C tests 63.9% vs. 51.1%; OR [95% confidence intervals] = 0.35 [0.34-0.36]) from baseline relative to the control cohort. The outcome-based measures also significantly decreased but at smaller magnitudes (3-5 percentage points). About 51.5% patients had at least one telehealth visit. The utilization-based measures for these patients were significantly higher than those without any telehealth visit (e.g., 2+ A1C 57.1% vs. 51.1%, p < 0.01). However, the outcome-based measures were comparable. Patients from historically underserved groups had a larger decline in health care outcomes than their counterparts. Among patient with at least one telehealth visit, these disparities were no longer significant. Discussions: Telehealth was associated with less negative impact of the pandemic and better performance in chronic care management, but more for utilization-based measures and less for outcome-based measures. Telehealth was also associated with less disparities in care outcomes.

Split-sample reliability estimation in health care quality measurement: Once is not enough.

To examine the sensitivity of split-sample reliability estimates to the random split of the data and propose alternative methods for improving the stability of the split-sample method. Data were simulated to reflect a variety of real-world quality measure distributions and scenarios. There is no date range to report as the data are simulated. Simulation studies of split-sample reliability estimation were conducted under varying practical scenarios. All data were simulated using functions in R. Single split-sample reliability estimates can be very dependent on the random split of the data, especially in low sample size and low variability settings. Averaging split-sample estimates over many splits of the data can yield a more stable reliability estimate. Measure developers and evaluators using the split-sample reliability method should average a series of reliability estimates calculated from many resamples of the data without replacement to obtain a more stable reliability estimate.

New evidence on the impacts of cross-market hospital mergers on commercial prices and measures of quality.

To examine the impact of "cross-market" hospital mergers on prices and quality and the extent to which serial acquisitions contribute to any measured effects. 2009-2017 commercial claims from the Health Care Cost Institute (HCCI) and quality measures from Hospital Compare. Event study models in which the treated group consisted of hospitals that acquired hospitals further than 50 miles, and the control group was hospitals that were not part of any merger activity (as a target or acquirer) during the study period. We extracted data for 214 treated hospitals and 955 control hospitals. Six years after acquisition, cross-market hospital mergers had increased acquirer prices by 12.9% (CI: 0.6%-26.6%) relative to control hospitals, but had no discernible impact on mortality and readmission rates for heart failure, heart attacks and pneumonia. For serial acquirers, the price effect increased to 16.3% (CI: 4.8%-29.1%). For all acquisitions, the price effect was 21.8% (CI: 4.6%-41.7%) when the target's market share was greater than the acquirer's market share versus 9.7% (CI: -0.5% to 20.9%) when the opposite was true. The magnitude of the price effect was similar for out-of-state and in-state cross-market mergers. Additional evidence on the price and quality effects of cross-market mergers is needed at a time when over half of recent hospital mergers have been cross-market. To date, no hospital mergers have been challenged by the Federal Trade Commission on cross-market grounds. Our study is the third to find a positive price effect associated with cross-market mergers and the first to show no quality effect and how serial acquisitions contribute to the price effect. More research is needed to identify the mechanism behind the price effects we observe and analyze price effect heterogeneity.