Are pediatric quality care measures too stringent?

Integration and Impact of Telemedicine in Underserved Pediatric Primary Care

Telemedicine, more novel in provision of pediatric care, rapidly expanded due to the recent coronavirus disease 2019 pandemic. This study aimed to determine the feasibility of telemedicine for acute and chronic care provision in an underserved pediatric primary care center. Items assessed included patient demographic data, chief complaint, and alternative care locations if telemedicine was not available. In our setting, 62% of telemedicine visits were for acute concerns and 38% for chronic concerns. Of acute telemedicine visits, 16.5% of families would have sought care in the Emergency Department/Urgent Care, and 11.3% would have opted for no care had telemedicine not been offered. The most common chronic issues addressed were attention deficit hyperactivity disorder (80.3%) and asthma (16.9%). Racial disparities existed among our telemedicine visits with Black patients utilizing telemedicine services less frequently than non-Black patients. Telemedicine is feasible for pediatric acute and chronic care, but systems must be designed to mitigate widening racial disparities.

Addressing electronic clinical information in the construction of quality measures

Electronic health records (EHR) and registries play a central role in health care and provide access to detailed clinical information at the individual, institutional, and population level. Use of these data for clinical quality/performance improvement and cost management has been a focus of policy initiatives over the past decade. The Children's Health Insurance Program Reauthorization Act of 2009 (CHIPRA)-mandated Pediatric Quality Measurement Program supports development and testing of quality measures for children on the basis of electronic clinical information, including de novo measures and respecification of existing measures designed for other data sources. Drawing on the experience of Centers of Excellence, we review both structural and pragmatic considerations in e-measurement. The presence of primary observations in EHR-derived data make it possible to measure outcomes in ways that are difficult with administrative data alone. However, relevant information may be located in narrative text, making it difficult to interpret. EHR systems are collecting more discrete data, but the structure, semantics, and adoption of data elements vary across vendors and sites. EHR systems also differ in ability to incorporate pediatric concepts such as variable dosing and growth percentiles. This variability complicates quality measurement, as do limitations in established measure formats, such as the Quality Data Model, to e-measurement. Addressing these challenges will require investment by vendors, researchers, and clinicians alike in developing better pediatric content for standard terminologies and data models, encouraging wider adoption of technical standards that support reliable quality measurement, better harmonizing data collection with clinical work flow in EHRs, and better understanding the behavior and potential of e-measures.

Variation in outcomes of quality measurement by data source

OBJECTIVE

To evaluate selected Children's Health Insurance Program Reauthorization Act claims-based quality measures using claims data alone, electronic health record (EHR) data alone, and both data sources combined.

METHODS

Our population included pediatric patients from 46 clinics in the OCHIN network of community health centers, who were continuously enrolled in Oregon's public health insurance program during 2010. Within this population, we calculated selected pediatric care quality measures according to the Children's Health Insurance Program Reauthorization Act technical specifications within administrative claims. We then calculated these measures in the same cohort, by using EHR data, by using the technical specifications plus clinical data previously shown to enhance capture of a given measure. We used the k statistic to determine agreement in measurement when using claims versus EHR data. Finally, we measured quality of care delivered to the study population, when using a combined dataset of linked, patient-level administrative claims and EHR data.

RESULTS

When using administrative claims data, 1.0% of children (aged 3-17) had a BMI percentile recorded, compared with 71.9% based on the EHR data (k agreement [k] # 0.01), and 72.0% in the combined dataset. Among children turning 2 in 2010, 20.2% received all recommended immunizations according to the administrative claims data, 17.2% according to the EHR data (k = 0.82), and 21.4% according to the combined dataset.

CONCLUSIONS

Children's care quality measures may not be accurate when assessed using only administrative claims. Adding EHR data to administrative claims data may yield more complete measurement.

The ADVANCE network: accelerating data value across a national community health center network

The ADVANCE (Accelerating Data Value Across a National Community Health Center Network) clinical data research network (CDRN) is led by the OCHIN Community Health Information Network in partnership with Health Choice Network and Fenway Health. The ADVANCE CDRN will ‘horizontally’ integrate outpatient electronic health record data for over one million federally qualified health center patients, and ‘vertically’ integrate hospital, health plan, and community data for these patients, often under-represented in research studies. Patient investigators, community investigators, and academic investigators with diverse expertise will work together to meet project goals related to data integration, patient engagement and recruitment, and the development of streamlined regulatory policies. By enhancing the data and research infrastructure of participating organizations, the ADVANCE CDRN will serve as a ‘community laboratory’ for including disadvantaged and vulnerable patients in patient-centered outcomes research that is aligned with the priorities of patients, clinics, and communities in our network.

Variations in measurement of sexual activity based on EHR definitions

OBJECTIVE

The goal of this study was to compare the performance of 4 operational definitions of sexual activity by using data electronically abstracted from electronic health records (EHRs) and examine how documentation of Chlamydia screening and positivity vary according to definition of sexual activity.

METHODS

Extracts were created from EHRs of adolescent females 12 to 19 years old who had ≥1 visit to a primary care practice during 2011 at 4 US pediatric health care organizations. We created 4 definitions of sexual activity derived from electronically abstracted indicator variables. Percent sexually active, documentation of Chlamydia screening, and rate of positive Chlamydia test results per 1000 adolescent females according to the sexual activity definition were calculated.

RESULTS

The most commonly documented individual indicator of sexual activity was "patient report of being sexually active" (mean across 4 sites: 19.2%). The percentage of adolescent females classified as sexually active varied by site and increased as more indicator variables were included. As the definition of sexual activity expanded, the percentage of sexually active females who received at least 1 Chlamydia test decreased. Using a broader definition of sexual activity resulted in improved identification of adolescent females with Chlamydia infection. For each sexual activity definition and performance item, the difference was statistically significant (P < .0001).

CONCLUSIONS

Information about sexual activity may be gathered from a variety of data sources, and changing the configurations of these indicators results in differences in the percentage of adolescent females classified as sexually active, screened for Chlamydia infection, and Chlamydia infection rates.

Measuring prevention more broadly: an empirical assessment of CHIPRA core measures

OBJECTIVE

To assess limitations of using select Children's Health Insurance Program Reauthorization Act (CHIPRA) core claims-based measures in capturing the preventive services that may occur in the clinical setting.

METHODS

We use claims data from ALL Kids, the Alabama Children's Health Insurance Program (CHIP), to calculate each of four quality measures under two alternative definitions: (1) the formal claims-based guidelines outlined in the CMS Technical Specifications, and (2) a broader definition of appropriate claims for identifying preventive service use. Additionally, we examine the extent to which these two claims-based approaches to measuring quality differ in assessments of disparities in quality of care across subgroups of children.

RESULTS

Statistically significant differences in rates were identified when comparing the two definitions for calculating each quality measure. Measure differences ranged from a 1.9 percentage point change for measure #13 (receiving preventive dental services) to a 25.5 percentage point change for measure #12 (adolescent well-care visit). We were able to identify subgroups based upon family income, rural location, and chronic disease status with differences in quality within the core measures. However, some identified disparities were sensitive to the approach used to calculate the quality measure.

CONCLUSIONS

Differences in CHIP design and structure, across states and over time, may limit the usefulness of select claims-based core measures for detecting disparities accurately. Additional guidance and research may be necessary before reporting of the measures becomes mandatory.