The Intended and Unintended Consequences of Quality Improvement Interventions for Small Practices in a Community-based Electronic Health Record Implementation Project

Ryan, Andrew M.; McCullough, Colleen; Shih, Sarah C.; Wang, Jason; Ryan, Mandy Smith; Casalino, Lawrence P.

doi:10.1097/mlr.0000000000000186

Cited by 24 publications

(18 citation statements)

References 22 publications

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“…SOL language queries are sent to participating practices and aggregate counts are returned automatically to a secure database without transmitting patient-identifiable data. Providers who share data on the Hub regularly engage with DOHMH on improving documentation quality and on using EHRs to increase the delivery of needed preventive care, track chronic disease, and improve disease management 7–10…”

Section: Methodsmentioning

confidence: 99%

Can Electronic Health Records Be Used for Population Health Surveillance? Validating Population Health Metrics Against Established Survey Data

McVeigh¹,

Newton-Dame²,

Chan³

et al. 2016

eGEMs

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Introduction:Electronic health records (EHRs) offer potential for population health surveillance but EHR-based surveillance measures require validation prior to use. We assessed the validity of obesity, smoking, depression, and influenza vaccination indicators from a new EHR surveillance system, the New York City (NYC) Macroscope. This report is the second in a 3-part series describing the development and validation of the NYC Macroscope. The first report describes in detail the infrastructure underlying the NYC Macroscope; design decisions that were made to maximize data quality; characteristics of the population sampled; completeness of data collected; and lessons learned from doing this work. This second report, which addresses concerns related to sampling bias and data quality, describes the methods used to evaluate the validity and robustness of NYC Macroscope prevalence estimates; presents validation results for estimates of obesity, smoking, depression and influenza vaccination; and discusses the implications of our findings for NYC and for other jurisdictions embarking on similar work. The third report applies the same validation methods described in this report to metabolic outcomes, including the prevalence, treatment and control of diabetes, hypertension and hyperlipidemia.Methods:NYC Macroscope prevalence estimates, overall and stratified by sex and age group, were compared to reference survey estimates for adult New Yorkers who reported visiting a doctor in the past year. Agreement was evaluated against 5 a priori criteria. Sensitivity and specificity were assessed by examining individual EHR records in a subsample of 48 survey participants.Results:Among adult New Yorkers in care, the NYC Macroscope prevalence estimate for smoking (15.2%) fell between estimates from NYC HANES (17.7 %) and CHS (14.9%) and met all 5 a priori criteria. The NYC Macroscope obesity prevalence estimate (27.8%) also fell between the NYC HANES (31.3%) and CHS (24.7%) estimates, but met only 3 a priori criteria. Sensitivity and specificity exceeded 0.90 for both the smoking and obesity indicators. The NYC Macroscope estimates of depression and influenza vaccination prevalence were more than 10 percentage points lower than the estimates from either reference survey. While specificity was > 0.90 for both of these indicators, sensitivity was < 0.70.Discussion:Through this work we have demonstrated that EHR data from a convenience sample of providers can produce acceptable estimates of smoking and obesity prevalence among adult New Yorkers in care; gained a better understanding of the challenges involved in estimating depression prevalence from EHRs; and identified areas for additional research regarding estimation of influenza vaccination prevalence. We have also shared lessons learned about how EHR indicators should be constructed and offer methodologic suggestions for validating them.Conclusions:This work adds to a rapidly emerging body of literature about how to define, collect and interpret EHR-based surveillance measures and...

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Section: Methodsmentioning

confidence: 99%

Can Electronic Health Records Be Used for Population Health Surveillance? Validating Population Health Metrics Against Established Survey Data

McVeigh¹,

Newton-Dame²,

Chan³

et al. 2016

eGEMs

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“…2 Efforts to change provider behavior through payment reform have not always had the intended effect on health outcomes. 3-10 …”

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confidence: 99%

Hemodialysis Hospitalizations and Readmissions: The Effects of Payment Reform

Erickson

Winkelmayer

Chertow

et al. 2017

American Journal of Kidney Diseases

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Background In 2004 the Centers for Medicare & Medicaid Services (CMS) changed reimbursement for physicians and advanced practitioners caring for patients receiving hemodialysis from a capitated to a tiered fee-for-service system, encouraging increased face-to-face visits. This early version of a pay-for-performance initiative targeted a care process: more frequent provider visits in hemodialysis. While more frequent provider visits in hemodialysis are associated with fewer hospitalizations and re-hospitalizations, it is unknown whether encouraging more frequent visits through reimbursement policy also yielded these benefits. Study Design We used a retrospective cohort, interrupted time-series study design to examine whether the 2004 nephrologist reimbursement reform led to reduced hospitalizations and re-hospitalizations. We also used published data to estimate a range of annual economic costs associated with more frequent visits. Setting & Participants Medicare beneficiaries in the United States receiving hemodialysis in the two years prior to and following reimbursement reform. Predictor The two years following nephrologist reimbursement reform. Outcomes Odds of hospitalization and 30-day hospital readmission for all causes and fluid overload; US dollars. Results We found no significant change in all-cause hospitalization or re-hospitalization, and slight reductions in hospitalization and re-hospitalization for fluid overload following reimbursement reform; the estimated economic cost associated with additional visits ranged from $13 to $87 million per year, depending on who (physicians or advanced practitioners) spent additional time visiting patients and how much additional effort was involved. Limitations Due to limited information about how much additional time providers spent seeing patients after reimbursement reform, we could only examine a range of potential economic costs associated with the reform. Conclusions A Medicare reimbursement policy designed to encourage more frequent visits during outpatient hemodialysis may have been costly. The policy was associated with fewer hospitalizations and re-hospitalizations for fluid overload, but had no effect on all-cause hospitalizations or re-hospitalizations.

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“…PCIP assists medical practices use EHRs and other forms of health information technology to increase the delivery of needed preventive care, track chronic disease, and improve disease management 12–15. As the Regional Extension Center for Health IT in NYC, PCIP collaborates with more than 20,050 providers and 1,600 practices using multiple EHR platforms.…”

Section: Methodsmentioning

confidence: 99%

Design of the New York City Macroscope: Innovations in Population Health Surveillance Using Electronic Health Records

Newton-Dame¹,

McVeigh²,

Schreibstein³

et al. 2016

eGEMs

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Introduction:Electronic health records (EHRs) have the potential to offer real-time, inexpensive standardized health data about chronic health conditions. Despite rapid expansion, EHR data evaluations for chronic disease surveillance have been limited. We present design and methods for the New York City (NYC) Macroscope, an EHR-based chronic disease surveillance system.This methods report is the first in a three part series describing the development and validation of the NYC Macroscope. This report describes in detail the infrastructure underlying the NYC Macroscope; indicator definitions; design decisions that were made to maximize data quality; characteristics of the population sampled; completeness of data collected; and lessons learned from doing this work. The second report describes the methods used to evaluate the validity and robustness of NYC Macroscope prevalence estimates; presents validation results for estimates of obesity, smoking, depression and influenza vaccination; and discusses the implications of our findings for NYC and for other jurisdictions embarking on similar work. The third report applies the same validation methods to metabolic outcomes, including the prevalence, treatment and control of diabetes, hypertension and hyperlipidemia.Methods:We designed the NYC Macroscope for comparison to a local “gold standard,” the 2013–14 NYC Health and Nutrition Examination Survey, and the telephonic 2013 Community Health Survey. NYC Macroscope indicators covered prevalence, treatment, and control of diabetes, hypertension, and hyperlipidemia; and prevalence of influenza vaccination, obesity, depression and smoking. Indicators were stratified by age, sex, and neighborhood poverty, and weighted to the in-care NYC population and limited to primary care patients. Indicator queries were distributed to a virtual network of primary care practices; 392 practices and 716,076 adult patients were retained in the final sample.Findings:The NYC Macroscope covered 10% of primary care providers and 15% of all adult patients in NYC in 2013 (8–47% of patients by neighborhood). Data completeness varied by domain from 98% for blood pressure among patients with hypertension to 33% for depression screening.Discussion:Design and validation efforts undertaken by NYC are described here to provide one potential blueprint for leveraging EHRs for population health monitoring. To replicate a model like NYC Macroscope, jurisdictions should establish buy-in; build informatics capacity; use standard, simple case defnitions; establish documentation quality thresholds; restrict to primary care providers; and weight the sample to a target population.

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The Intended and Unintended Consequences of Quality Improvement Interventions for Small Practices in a Community-based Electronic Health Record Implementation Project

Abstract: Technical assistance and financial incentives-alongside EHR implementation-can improve quality of care. Financial incentives for quality may not result in similar improvements for incentivized and unincentivized measures.

Cited by 24 publications

References 22 publications

Can Electronic Health Records Be Used for Population Health Surveillance? Validating Population Health Metrics Against Established Survey Data

Can Electronic Health Records Be Used for Population Health Surveillance? Validating Population Health Metrics Against Established Survey Data

Hemodialysis Hospitalizations and Readmissions: The Effects of Payment Reform

Design of the New York City Macroscope: Innovations in Population Health Surveillance Using Electronic Health Records

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