Evaluation of the consumer assessment of healthcare providers and systems in-center hemodialysis survey.

Background and objectivesThe US Centers for Medicare and Medicaid Services (CMS) End Stage Renal Disease Prospective Payment System and Quality Incentive Program requires that dialysis centers meet predefined criteria for quality of patient care to ensure future funding. The CMS selected the Consumer Assessment of Healthcare Providers and Systems In-Center Hemodialysis (CAHPS-ICH) survey for the assessment of patient experience of care. This analysis evaluated the psychometric properties of the CAHPS-ICH survey in a sample of hemodialysis patients.Design, setting, participants, & measurementsData were drawn from the Adelphi CKD Disease Specific Program (a retrospective, cross-sectional survey of nephrologists and patients). Selected United States-based nephrologists treating patients receiving hemodialysis completed patient record forms and provided information on their dialysis center. Patients (n=404) completed the CAHPS-ICH survey (comprising 58 questions) providing six scores for the assessment of patient experience of care. CAHPS-ICH item-scale convergence, discrimination, and reliability were evaluated for multi-item scales. Floor and ceiling effects were estimated for all six scores. Patient (demographics, dialysis history, vascular access method) and facility characteristics (size, ratio of patients-to-physicians, nurses, and technicians) associated with the CAHPS-ICH scores were also evaluated.ResultsItem-scale correlations and internal consistency reliability estimates provided support for the nephrologists' communication (range, 0.16-0.71; α=0.81) and quality of care (range, 0.16-0.76; α=0.90) composites. However, the patient information composite had low internal consistency reliability (α=0.55). Provider-to-patient ratios (range, 2.37 for facilities with >36 patients per physician to 2.8 for those with <8 patients per physician) and time spent in the waiting room (3.44 for >15 minutes of waiting time to 3.75 for 5 to <10 minutes) were characteristics most consistently related to patients' perceptions of dialysis care.ConclusionsCAHPS-ICH is a potentially valuable and informative tool for the evaluation of patients' experiences with dialysis care. Additional studies are needed to estimate clinically meaningful differences between care providers

Evaluation of the consumer assessment of healthcare providers and systems in-center hemodialysis survey.

Background and objectivesThe US Centers for Medicare and Medicaid Services (CMS) End Stage Renal Disease Prospective Payment System and Quality Incentive Program requires that dialysis centers meet predefined criteria for quality of patient care to ensure future funding. The CMS selected the Consumer Assessment of Healthcare Providers and Systems In-Center Hemodialysis (CAHPS-ICH) survey for the assessment of patient experience of care. This analysis evaluated the psychometric properties of the CAHPS-ICH survey in a sample of hemodialysis patients.Design, setting, participants, & measurementsData were drawn from the Adelphi CKD Disease Specific Program (a retrospective, cross-sectional survey of nephrologists and patients). Selected United States-based nephrologists treating patients receiving hemodialysis completed patient record forms and provided information on their dialysis center. Patients (n=404) completed the CAHPS-ICH survey (comprising 58 questions) providing six scores for the assessment of patient experience of care. CAHPS-ICH item-scale convergence, discrimination, and reliability were evaluated for multi-item scales. Floor and ceiling effects were estimated for all six scores. Patient (demographics, dialysis history, vascular access method) and facility characteristics (size, ratio of patients-to-physicians, nurses, and technicians) associated with the CAHPS-ICH scores were also evaluated.ResultsItem-scale correlations and internal consistency reliability estimates provided support for the nephrologists' communication (range, 0.16-0.71; α=0.81) and quality of care (range, 0.16-0.76; α=0.90) composites. However, the patient information composite had low internal consistency reliability (α=0.55). Provider-to-patient ratios (range, 2.37 for facilities with >36 patients per physician to 2.8 for those with <8 patients per physician) and time spent in the waiting room (3.44 for >15 minutes of waiting time to 3.75 for 5 to <10 minutes) were characteristics most consistently related to patients' perceptions of dialysis care.ConclusionsCAHPS-ICH is a potentially valuable and informative tool for the evaluation of patients' experiences with dialysis care. Additional studies are needed to estimate clinically meaningful differences between care providers

A claims‐based, machine‐learning algorithm to identify patients with pulmonary arterial hypertension

Abstract Many patients with pulmonary arterial hypertension (PAH) experience substantial delays in diagnosis, which is associated with worse outcomes and higher costs. Tools for diagnosing PAH sooner may lead to earlier treatment, which may delay disease progression and adverse outcomes including hospitalization and death. We developed a machine‐learning (ML) algorithm to identify patients at risk for PAH earlier in their symptom journey and distinguish them from patients with similar early symptoms not at risk for developing PAH. Our supervised ML model analyzed retrospective, de‐identified data from the US‐based Optum® Clinformatics® Data Mart claims database (January 2015 to December 2019). Propensity score matched PAH and non‐PAH (control) cohorts were established based on observed differences. Random forest models were used to classify patients as PAH or non‐PAH at diagnosis and at 6 months prediagnosis. The PAH and non‐PAH cohorts included 1339 and 4222 patients, respectively. At 6 months prediagnosis, the model performed well in distinguishing PAH and non‐PAH patients, with area under the curve of the receiver operating characteristic of 0.84, recall (sensitivity) of 0.73, and precision of 0.50. Key features distinguishing PAH from non‐PAH cohorts were a longer time between first symptom and the prediagnosis model date (i.e., 6 months before diagnosis); more diagnostic and prescription claims, circulatory claims, and imaging procedures, leading to higher overall healthcare resource utilization; and more hospitalizations. Our model distinguishes between patients with and without PAH at 6 months before diagnosis and illustrates the feasibility of using routine claims data to identify patients at a population level who might benefit from PAH‐specific screening and/or earlier specialist referral