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Calculations of Financial Incentives for Providers in a Pay-for-Performance Program: Manual Review Versus Data From Structured Fields in Electronic Health Records.

ABSTRACT: Hospital report cards and financial incentives linked to performance require clinical data that are reliable, appropriate, timely, and cost-effective to process. Pay-for-performance plans are transitioning to automated electronic health record (EHR) data as an efficient method to generate data needed for these programs.To determine how well data from automated processing of structured fields in the electronic health record (AP-EHR) reflect data from manual chart review and the impact of these data on performance rewards.Cross-sectional analysis of performance measures used in a cluster randomized trial assessing the impact of financial incentives on guideline-recommended care for hypertension.A total of 2840 patients with hypertension assigned to participating physicians at 12 Veterans Affairs hospital-based outpatient clinics. Fifty-two physicians and 33 primary care personnel received incentive payments.Overall, positive and negative agreement indices and Cohen's kappa were calculated for assessments of guideline-recommended antihypertensive medication use, blood pressure (BP) control, and appropriate response to uncontrolled BP. Pearson's correlation coefficient was used to assess how similar participants' calculated earnings were between the data sources.By manual chart review data, 72.3% of patients were considered to have received guideline-recommended antihypertensive medications compared with 65.0% by AP-EHR review (?=0.51). Manual review indicated 69.5% of patients had controlled BP compared with 66.8% by AP-EHR review (?=0.87). Compared with 52.2% of patients per the manual review, 39.8% received an appropriate response by AP-EHR review (?=0.28). Participants' incentive payments calculated using the 2 methods were highly correlated (r?0.98). Using the AP-EHR data to calculate earnings, participants' payment changes ranged from a decrease of $91.00 (-30.3%) to an increase of $18.20 (+7.4%) for medication use (interquartile range, -14.4% to 0%) and a decrease of $100.10 (-31.4%) to an increase of $36.40 (+15.4%) for BP control or appropriate response to uncontrolled BP (interquartile range, -11.9% to -6.1%).Pay-for-performance plans that use only EHR data should carefully consider the measures and the structure of the EHR before data collection and financial incentive disbursement. For this study, we feel that a 10% difference in the total amount of incentive earnings disbursed based on AP-EHR data compared with manual review is acceptable given the time and resources required to abstract data from medical records.

SUBMITTER: Urech TH

PROVIDER: S-EPMC4570867 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Calculations of Financial Incentives for Providers in a Pay-for-Performance Program: Manual Review Versus Data From Structured Fields in Electronic Health Records.

Urech Tracy H TH Woodard LeChauncy D LD Virani Salim S SS Dudley R Adams RA Lutschg Meghan Z MZ Petersen Laura A LA

Medical care 20151001 10

<h4>Background</h4>Hospital report cards and financial incentives linked to performance require clinical data that are reliable, appropriate, timely, and cost-effective to process. Pay-for-performance plans are transitioning to automated electronic health record (EHR) data as an efficient method to generate data needed for these programs.<h4>Objective</h4>To determine how well data from automated processing of structured fields in the electronic health record (AP-EHR) reflect data from manual ch ...[more]

PMID: 26340661

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Project description:Most evaluations of pay-for-performance (P4P) incentives have focused on large-group practices. Thus, the effect of P4P in small practices, where many US residents receive care, is largely unknown. Furthermore, whether electronic health records (EHRs) with chronic disease management capabilities support small-practice response to P4P has not been studied.To assess the effect of P4P incentives on quality in EHR-enabled small practices in the context of an established quality improvement initiative.A cluster-randomized trial of small (<10 clinicians) primary care clinics in New York City from April 2009 through March 2010. A city program provided all participating clinics with the same EHR software with decision support and patient registry functionalities and quality improvement specialists offering technical assistance.Incentivized clinics were paid for each patient whose care met the performance criteria, but they received higher payments for patients with comorbidities, who had Medicaid insurance, or who were uninsured (maximum payments: $200/patient; $100,000/clinic). Quality reports were given quarterly to both the intervention and control groups.Comparison of differences in performance improvement, from the beginning to the end of the study, between control and intervention clinics for aspirin or antithrombotic prescription, blood pressure control, cholesterol control, and smoking cessation interventions. Mixed-effects logistic regression was used to account for clustering of patients within clinics, with a treatment by time interaction term assessing the statistical significance of the effect of the intervention.Participating clinics (n?=?42 for each group) had similar baseline characteristics, with a mean of 4592 (median, 2500) patients at the intervention group clinics and 3042 (median, 2000) at the control group clinics. Intervention clinics had greater adjusted absolute improvement in rates of appropriate antithrombotic prescription (12.0% vs 6.1%, difference: 6.0% [95% CI, 2.2% to 9.7%], P?=?.001 for interaction term), blood pressure control (no comorbidities: 9.7% vs 4.3%, difference: 5.5% [95% CI, 1.6% to 9.3%], P?=?.01 for interaction term; with diabetes mellitus: 9.0% vs 1.2%, difference: 7.8% [95% CI, 3.2% to 12.4%], P?=?.007 for interaction term; with diabetes mellitus or ischemic vascular disease: 9.5% vs 1.7%, difference: 7.8% [95% CI, 3.0% to 12.6%], P?=?.01 for interaction term), and in smoking cessation interventions (12.4% vs 7.7%, difference: 4.7% [95% CI, -0.3% to 9.6%], P?=?.02 for interaction term). Intervention clinics performed better on all measures for Medicaid and uninsured patients except cholesterol control, but no differences were statistically significant.Among small EHR-enabled clinics, a P4P incentive program compared with usual care resulted in modest improvements in cardiovascular care processes and outcomes. Because most proposed P4P programs are intended to remain in place more than a year, further research is needed to determine whether this effect increases or decreases over time.clinicaltrials.gov Identifier: NCT00884013.

Project description:The objective was to compare case ascertainment, agreement, validity, and missing values for clinical research data obtained, processed, and linked electronically from electronic health records (EHR) compared to "manual" data processing and record abstraction in a cohort of out-of-hospital trauma patients.? This was a secondary analysis of two sets of data collected for a prospective, population-based, out-of-hospital trauma cohort evaluated by 10 emergency medical services (EMS) agencies transporting to 16 hospitals, from January 1, 2006, through October 2, 2007. Eighteen clinical, operational, procedural, and outcome variables were collected and processed separately and independently using two parallel data processing strategies by personnel blinded to patients in the other group. The electronic approach included EHR data exports from EMS agencies, reformatting, and probabilistic linkage to outcomes from local trauma registries and state discharge databases. The manual data processing approach included chart matching, data abstraction, and data entry by a trained abstractor. Descriptive statistics, measures of agreement, and validity were used to compare the two approaches to data processing.? During the 21-month period, 418 patients underwent both data processing methods and formed the primary cohort. Agreement was good to excellent (kappa = 0.76 to 0.97; intraclass correlation coefficient [ICC] = 0.49 to 0.97), with exact agreement in 67% to 99% of cases and a median difference of zero for all continuous and ordinal variables. The proportions of missing out-of-hospital values were similar between the two approaches, although electronic processing generated more missing outcomes (87 of 418, 21%, 95% confidence interval [CI] = 17% to 25%) than the manual approach (11 of 418, 3%, 95% CI = 1% to 5%). Case ascertainment of eligible injured patients was greater using electronic methods (n = 3,008) compared to manual methods (n = 629).? In this sample of out-of-hospital trauma patients, an all-electronic data processing strategy identified more patients and generated values with good agreement and validity compared to traditional data collection and processing methods.

Project description:IntroductionData extraction from electronic health record (EHR) systems occurs through manual abstraction, automated extraction, or a combination of both. While each method has its strengths and weaknesses, both are necessary for retrospective observational research as well as sudden clinical events, like the COVID-19 pandemic. Assessing the strengths, weaknesses, and potentials of these methods is important to continue to understand optimal approaches to extracting clinical data. We set out to assess automated and manual techniques for collecting medication use data in patients with COVID-19 to inform future observational studies that extract data from the electronic health record (EHR).Materials and methodsFor 4,123 COVID-positive patients hospitalized and/or seen in the emergency department at an academic medical center between 03/03/2020 and 05/15/2020, we compared medication use data of 25 medications or drug classes collected through manual abstraction and automated extraction from the EHR. Quantitatively, we assessed concordance using Cohen's kappa to measure interrater reliability, and qualitatively, we audited observed discrepancies to determine causes of inconsistencies.ResultsFor the 16 inpatient medications, 11 (69%) demonstrated moderate or better agreement; 7 of those demonstrated strong or almost perfect agreement. For 9 outpatient medications, 3 (33%) demonstrated moderate agreement, but none achieved strong or almost perfect agreement. We audited 12% of all discrepancies (716/5,790) and, in those audited, observed three principal categories of error: human error in manual abstraction (26%), errors in the extract-transform-load (ETL) or mapping of the automated extraction (41%), and abstraction-query mismatch (33%).ConclusionOur findings suggest many inpatient medications can be collected reliably through automated extraction, especially when abstraction instructions are designed with data architecture in mind. We discuss quality issues, concerns, and improvements for institutions to consider when crafting an approach. During crises, institutions must decide how to allocate limited resources. We show that automated extraction of medications is feasible and make recommendations on how to improve future iterations.

Project description:BACKGROUND: Previous studies regarding the use of information technologies (IT) specifically among pediatricians and other physicians who treat children are lacking. As such, the objective of this study is to examine the use of electronic health record (EHR) systems and other IT applications among pediatricians and other child health providers (CHPs) in Florida. METHODS: We focus on pediatricians and other CHPs who responded to a state-wide physician survey of IT use. CHPs included general pediatricians, pediatric sub-specialists, and family physicians who self-reported a practice composition of at least 20% children. We compared general pediatricians to other CHPs and all CHPs (including pediatricians) to other physicians with respect to computer and internet availability, and to the use of personal digital assistants and EHRs. Those with an EHR were also compared regarding the availability of key functions available in their system. Statistical analyses included chi-square analysis and logistic regression models which controlled for numerous factors. RESULTS: A total of 4,203 surveys (28.2% response) including 1,021 CHPs, were returned. General pediatricians (13.7%) were significantly less likely to be using an EHR than both CHP family physicians (26.1%) and pediatric sub-specialists (29.6%; p < .001). In multivariate analysis, only general pediatricians were significantly less likely than other physicians to indicate the use of an EHR system (OR = .43; 95% C.I. = .29 - .64). Overall, CHPs were less likely to have key functions available in their EHR system including electronic prescribing (53.3% vs. 61.9%; p = .028), and electronic order entry (47.7% vs. 57.2%; p = .017) among others. General pediatricians and pediatric sub-specialists frequently lagged behind CHP family physicians with respect to key EHR functions. In contrast, CHPs had growth charts (51.3% vs. 24.0%; p < .001) and weight-based dosing functions (35.5% vs.22.7%; p < .001) more frequently than others. CONCLUSION: Physicians caring for children, and especially pediatricians, in Florida, are significantly slower than other doctors to adopt EHRs, and important electronic patient safety functionalities, into their office practices.

Dataset Information

Calculations of Financial Incentives for Providers in a Pay-for-Performance Program: Manual Review Versus Data From Structured Fields in Electronic Health Records.

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Calculations of Financial Incentives for Providers in a Pay-for-Performance Program: Manual Review Versus Data From Structured Fields in Electronic Health Records.

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