Harnessing electronic clinical data to report adult and prenatal immunization quality measures.
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ABSTRACT: Objective
To explore the use of health plan quality measures specified for electronic clinical data to monitor immunizations.Materials and methods
We analyzed 2018 data submitted by health plans reporting 2 new Healthcare Effectiveness Data and Information Set measures assessing receipt of clinically recommended vaccines among pregnant women and adults. We analyzed the number of plans reporting a valid performance rate and electronic data source used. We consulted expert panels and reviewed coverage rates from other sources to understand the results.Results
We received 136 data submissions across commercial, Medicaid and Medicare plans and 87 submissions across commercial and Medicaid plans for the adult and prenatal immunization measures, respectively. These submissions represent approximately 15% of possible submissions. Plans used claims, registries and electronic health records. Mean performance rates for adult immunizations were 21.2 (commercial), 14.0 (Medicaid) and 19.5% (Medicare). Mean rates for prenatal immunizations were 33.1 (commercial) and 16.7% (Medicaid).Discussion
Results from the first year of reporting 2 electronic clinical data measures suggest health plans can feasibly report these measures and are seeking electronic data to supplement claims. Comparison of rates to other national results showed lower than expected rates for the adult immunization measure. However, prenatal immunization rates were on par with those from a national survey, suggesting this measure is closer to use for quality improvement.Conclusion
Quality measure reporting that encourages connection to electronic data sources is a step forward in performance monitoring and improvement. The use of electronic sources may advance health information exchange for patient care.
SUBMITTER: Byron SC
PROVIDER: S-EPMC8449614 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ImportanceThe capability and capacity of primary care practices to report electronic clinical quality measures (eCQMs) are questionable.ObjectiveTo determine how quickly primary care practices can report eCQMs and the practice characteristics associated with faster reporting.Design, setting, and participantsThis quality improvement study examined an initiative (EvidenceNOW Southwest) to enhance primary care practices' ability to adopt evidence-based cardiovascular care approaches: aspirin prescribing, blood pressure control, cholesterol management, and smoking cessation (ABCS). A total of 211 primary care practices in Colorado and New Mexico participating in EvidenceNOW Southwest between February 2015 and December 2017 were included.InterventionsPractices were instructed on eCQM specifications that could be produced by an electronic health record, a registry, or a third-party platform. Practices received 9 months of support from a practice facilitator, a clinical health information technology advisor, and the research team. Practices were instructed to report their baseline ABCS eCQMs as soon as possible.Main outcomes and measuresThe main outcome was time to report the ABCS eCQMs. Cox proportional hazards models were used to examine practice characteristics associated with time to reporting.ResultsPractices were predominantly clinician owned (48%) and in urban or suburban areas (71%). Practices required a median (interquartile range) of 8.2 (4.6-11.9) months to report any ABCS eCQM. Time to report differed by eCQM: practices reported blood pressure management the fastest (median [interquartile range], 7.8 [3.5-10.4] months) and cholesterol management the slowest (median [interquartile range], 10.5 [6.6 to >12] months) (log-rank P < .001). In multivariable models, the blood pressure eCQM was reported more quickly by practices that participated in accountable care organizations (hazard ratio [HR], 1.88; 95% CI, 1.40-2.53; P < .001) or participated in a quality demonstration program (HR, 1.58; 95% CI, 1.14-2.18; P = .006). The cholesterol eCQM was reported more quickly by practices that used clinical guidelines for cardiovascular disease management (HR, 1.35; 95% CI, 1.18-1.53; P < .001). Compared with Federally Qualified Health Centers, hospital-owned practices had greater ability to report blood pressure eCQMs (HR, 2.66; 95% CI, 95% CI, 1.73-4.09; P < .001), and clinician-owned practices had less ability to report cholesterol eCQMs (HR, 0.52; 95% CI, 0.35-0.76; P < .001).Conclusions and relevanceIn this study, time to report eCQMs varied by measure and practice type, with very few practices reporting quickly. Practices took longer to report a new cholesterol measure than other measures. Programs that require eCQM reporting should consider the time and effort practices must exert to produce reports. Practices may benefit from additional support to succeed in new programs that require eCQM reporting.
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Project description:Researchers in clinical and pharmacoepidemiology fields have adopted information technology (IT) and electronic data capture, but these remain underused despite the benefits. This review discusses electronic case report forms and electronic data capture, specifically within pharmacoepidemiology and clinical research.The review used PubMed and the Institute of Electrical and Electronic Engineers library. Search terms used were agreed by the authors and documented. PubMed is medical and health based, whereas Institute of Electrical and Electronic Engineers is technology based. The review focuses on electronic case report forms and electronic data capture, but briefly considers other relevant topics; consent, ethics and security.There were 1126 papers found using the search terms. Manual filtering and reviewing of abstracts further condensed this number to 136 relevant manuscripts. The papers were further categorized: 17 contained study data; 40 observational data; 27 anecdotal data; 47 covering methodology or design of systems; one case study; one literature review; two feasibility studies; and one cost analysis.Electronic case report forms, electronic data capture and IT in general are viewed with enthusiasm and are seen as a cost-effective means of improving research efficiency, educating participants and improving trial recruitment, provided concerns about how data will be protected from misuse can be addressed. Clear operational guidelines and best practises are key for healthcare providers, and researchers adopting IT, and further work is needed on improving integration of new technologies with current systems. A robust method of evaluation for technical innovation is required.
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Project description:Hospital performance measures based on patient mortality and readmission have indicated modest rates of agreement. We examined if combining clinical data on laboratory tests and vital signs with administrative data leads to improved agreement with each other, and with other measures of hospital performance in the nation's largest integrated health care system.We used patient-level administrative and clinical data, and hospital-level data on quality indicators, for 2007-2010 from the Veterans Health Administration (VA). For patients admitted for acute myocardial infarction (AMI), heart failure (HF) and pneumonia we examined changes in hospital performance on 30-d mortality and 30-d readmission rates as a result of adding clinical data to administrative data. We evaluated whether this enhancement yielded improved measures of hospital quality, based on concordance with other hospital quality indicators.For 30-d mortality, data enhancement improved model performance, and significantly changed hospital performance profiles; for 30-d readmission, the impact was modest. Concordance between enhanced measures of both outcomes, and with other hospital quality measures - including Joint Commission process measures, VA Surgical Quality Improvement Program (VASQIP) mortality and morbidity, and case volume - remained poor.Adding laboratory tests and vital signs to measure hospital performance on mortality and readmission did not improve the poor rates of agreement across hospital quality indicators in the VA.Efforts to improve risk adjustment models should continue; however, evidence of validation should precede their use as reliable measures of quality.
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Project description:BackgroundThe increasing number of clinical trials and their complexity make it challenging to detect and identify clinical quality issues timely. Despite extensive sponsor audit programs and monitoring activities, issues related to data integrity, safety, sponsor oversight and patient consent have recurring audit and inspection findings. Recent developments in data management and IT systems allow statistical modeling to provide insights to clinical Quality Assurance (QA) professionals to help mitigate some of the key clinical quality issues more holistically and efficiently.MethodsWe used findings from a curated data set from Roche/Genentech operational and quality assurance study data, covering a span of 8 years (2011-2018) and grouped them into 5 clinical impact factor categories, for which we modeled the risk with a logistic regression using hand crafted features.ResultsWe were able to train 5 interpretable, cross-validated models with several distinguished risk factors, many of which confirmed field observations of our quality professionals. Our models were able to reliably predict a decrease in risk by 12-44%, with 2-8 coefficients each, despite a low signal-to-noise ratio in our data set.ConclusionWe proposed a modeling strategy that could provide insights to clinical QA professionals to help them mitigate key clinical quality issues (e.g., safety, consent, data integrity) in a more sustained data-driven way, thus turning the traditional reactive approach to a more proactive monitoring and alerting approach. Also, we are calling for cross-sponsors collaborations and data sharing to improve and further validate the use of statistical models in clinical QA.
| S-EPMC7458946 | biostudies-literature
Project description:BACKGROUND:Creation of a new electronic health record (EHR)-based registry often can be a "one-off" complex endeavor: first developing new EHR data collection and clinical decision support tools, followed by developing registry-specific data extractions from the EHR for analysis. Each development phase typically has its own long development and testing time, leading to a prolonged overall cycle time for delivering one functioning registry with companion reporting into production. The next registry request then starts from scratch. Such an approach will not scale to meet the emerging demand for specialty registries to support population health and value-based care. OBJECTIVE:To determine if the creation of EHR-based specialty registries could be markedly accelerated by employing (a) a finite core set of EHR data collection principles and methods, (b) concurrent engineering of data extraction and data warehouse design using a common dimensional data model for all registries, and (c) agile development methods commonly employed in new product development. METHODS:We adopted as guiding principles to (a) capture data as a byproduct of care of the patient, (b) reinforce optimal EHR use by clinicians, (c) employ a finite but robust set of EHR data capture tool types, and (d) leverage our existing technology toolkit. Registries were defined by a shared condition (recorded on the Problem List) or a shared exposure to a procedure (recorded on the Surgical History) or to a medication (recorded on the Medication List). Any EHR fields needed - either to determine registry membership or to calculate a registry-associated clinical quality measure (CQM) - were included in the enterprise data warehouse (EDW) shared dimensional data model. Extract-transform-load (ETL) code was written to pull data at defined "grains" from the EHR into the EDW model. All calculated CQM values were stored in a single Fact table in the EDW crossing all registries. Registry-specific dashboards were created in the EHR to display both (a) real-time patient lists of registry patients and (b) EDW-generated CQM data. Agile project management methods were employed, including co-development, lightweight requirements documentation with User Stories and acceptance criteria, and time-boxed iterative development of EHR features in 2-week "sprints" for rapid-cycle feedback and refinement. RESULTS:Using this approach, in calendar year 2015 we developed a total of 43 specialty chronic disease registries, with 111 new EHR data collection and clinical decision support tools, 163 new clinical quality measures, and 30 clinic-specific dashboards reporting on both real-time patient care gaps and summarized and vetted CQM measure performance trends. CONCLUSIONS:This study suggests concurrent design of EHR data collection tools and reporting can quickly yield useful EHR structured data for chronic disease registries, and bodes well for efforts to migrate away from manual abstraction. This work also supports the view that in new EHR-based registry development, as in new product development, adopting agile principles and practices can help deliver valued, high-quality features early and often.
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Project description:PURPOSE:To assess whether primary care practices with and without support from a larger organization differ in their ability to produce timely reports on cardiovascular disease quality measures. BACKGROUND:Although many primary care practices are now part of larger organizations, it is not clear whether such a shift will improve the ability of those who work in these primary care settings to easily access and use their own data for improvement. METHODS:Smaller primary care practices were enrolled in a trial of external practice support to build quality improvement (QI) capacity. A request for clinical quality measure (eCQM) data were sent to each practice and study outcomes were defined based on the date on which practices first submitted valid data for at least 1 of the 3 measures. A practice survey completed by a clinic manager captured practice characteristics, including the presence of QI support from a larger organization. RESULTS:Of the 209 enrolled practices, 205 had complete data for analysis. Practices without central QI support had higher rates of eCQM submission at 30 days (38% vs 20%) and 60 days, (63% vs 48%) than practices with central QI support. Practices with central QI support took longer to submit data (median, 57 days) compared with practices without centralized support (median, 40 days) although this difference was not significant. CONCLUSION:The ability of smaller practices without centralized QI support to report their eCQMs more quickly may have implications for their subsequent ability to improve these measures.
| S-EPMC7453756 | biostudies-literature
Project description:BackgroundElectronic Case Report Forms (eCRFs) are increasingly chosen by investigators and sponsors of clinical research instead of the traditional pen-and-paper data collection (pCRFs). Previous studies suggested that eCRFs avoided mistakes, shortened the duration of clinical studies and reduced data collection costs.MethodsOur objectives were to describe and contrast both objective and subjective efficiency of pCRF and eCRF use in clinical studies. A total of 27 studies (11 eCRF, 16 pCRF) sponsored by the Paris hospital consortium, conducted and completed between 2001 and 2011 were included. Questionnaires were emailed to investigators of those studies, as well as clinical research associates and data managers working in Paris hospitals, soliciting their level of satisfaction and preferences for eCRFs and pCRFs. Mean costs and timeframes were compared using bootstrap methods, linear and logistic regression.ResultsThe total cost per patient was 374€ ±351 with eCRFs vs. 1,135€ ±1,234 with pCRFs. Time between the opening of the first center and the database lock was 31.7 months Q1 = 24.6; Q3 = 42.8 using eCRFs, vs. 39.8 months Q1 = 31.7; Q3 = 52.2 with pCRFs (p = 0.11). Electronic CRFs were globally preferred by all (31/72 vs. 15/72 for paper) for easier monitoring and improved data quality.ConclusionsThis study found that eCRFs and pCRFs are used in studies with different patient numbers, center numbers and risk. The first ones are more advantageous in large, low-risk studies and gain support from a majority of stakeholders.
| S-EPMC3909932 | biostudies-literature
Project description:ObjectiveAmbiguous definitions of quality measures in natural language impede their automated computability and also the reproducibility, validity, timeliness, traceability, comparability, and interpretability of computed results. Therefore, quality measures should be formalized before their release. We have previously developed and successfully applied a method for clinical indicator formalization (CLIF). The objective of our present study is to test whether CLIF is generalizable--that is, applicable to a large set of heterogeneous measures of different types and from various domains.Materials and methodsWe formalized the entire set of 159 Dutch quality measures for general practice, which contains structure, process, and outcome measures and covers seven domains. We relied on a web-based tool to facilitate the application of our method. Subsequently, we computed the measures on the basis of a large database of real patient data.ResultsOur CLIF method enabled us to fully formalize 100% of the measures. Owing to missing functionality, the accompanying tool could support full formalization of only 86% of the quality measures into Structured Query Language (SQL) queries. The remaining 14% of the measures required manual application of our CLIF method by directly translating the respective criteria into SQL. The results obtained by computing the measures show a strong correlation with results computed independently by two other parties.ConclusionsThe CLIF method covers all quality measures after having been extended by an additional step. Our web tool requires further refinement for CLIF to be applied completely automatically. We therefore conclude that CLIF is sufficiently generalizable to be able to formalize the entire set of Dutch quality measures for general practice.
| S-EPMC3932459 | biostudies-literature
Project description:Following the piloting of VaxTrac, an electronic immunization registry (EIR), we conducted a rapid assessment in November-December 2017 to evaluate the use of the EIR in 10 health facilities in Western Area Urban district in Sierra Leone [1]. In this data-in-brief report, we provide additional descriptive data from the assessment of the VaxTrac EIR in Sierra Leone. The assessment comprised aggregate data on vaccine doses administered that were abstracted from VaxTrac and three paper-based sources (daily tally sheets, register of children under the age of 2 years, and a summary form of doses administered). Data were abstracted for the following six vaccine doses in the immunization schedule in Sierra Leone: 1) Bacillus Calmette-Guérin vaccine, 2) first dose of pentavalent vaccine, 3) second dose of pentavalent vaccine, 4) third dose of pentavalent vaccine, 5) first dose of measles-containing vaccine, and 6) second dose of measles-containing vaccine. We descriptively analysed the abstracted data to examine the congruity between VaxTrac records and the three paper-based sources. Bar graphs were generated to visually depict the variations in number of administered vaccine doses by data source for each health facility. We provide the aggregated data for each vaccine dose abstracted by data source from each health facility as supplemental material (Excel file). The supplementary data reveal patterns in the congruity of vaccine doses captured that have implications for policy and programmatic decisions regarding the use of VaxTrac and other similar EIRs in low resource urban settings.
| S-EPMC7452626 | biostudies-literature