Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading

A Generic Transformation Approach for Complex Laboratory Data Using the Fast Healthcare Interoperability Resources Mapping Language: Method Development and Implementation

Background

Reaching meaningful interoperability between proprietary health care systems is a ubiquitous task in medical informatics, where communication servers are traditionally used for referring and transforming data from the source to target systems. The Mirth Connect Server, an open-source communication server, offers, in addition to the exchange functionality, functions for simultaneous manipulation of data. The standard Fast Healthcare Interoperability Resources (FHIR) has recently become increasingly prevalent in national health care systems. FHIR specifies its own standardized mechanisms for transforming data structures using StructureMaps and the FHIR mapping language (FML).

Objective

In this study, a generic approach is developed, which allows for the application of declarative mapping rules defined using FML in an exchangeable manner. A transformation engine is required to execute the mapping rules.

Methods

FHIR natively defines resources to support the conversion of instance data, such as an FHIR StructureMap. This resource encodes all information required to transform data from a source system to a target system. In our approach, this information is defined in an implementation-independent manner using FML. Once the mapping has been defined, executable Mirth channels are automatically generated from the resources containing the mapping in JavaScript format. These channels can then be deployed to the Mirth Connect Server.

Results

The resulting tool is called FML2Mirth, a Java-based transformer that derives Mirth channels from detailed declarative mapping rules based on the underlying StructureMaps. Implementation of the translate functionality is provided by the integration of a terminology server, and to achieve conformity with existing profiles, validation via the FHIR validator is built in. The system was evaluated for its practical use by transforming Labordatenträger version 2 (LDTv.2) laboratory results into Medical Information Object (Medizinisches Informationsobjekt) laboratory reports in accordance with the National Association of Statutory Health Insurance Physicians' specifications and into the HL7 (Health Level Seven) Europe Laboratory Report. The system could generate complex structures, but LDTv.2 lacks some information to fully comply with the specification.

Conclusions

The tool for the auto-generation of Mirth channels was successfully presented. Our tests reveal the feasibility of using the complex structures of the mapping language in combination with a terminology server to transform instance data. Although the Mirth Server and the FHIR are well established in medical informatics, the combination offers space for more research, especially with regard to FML. Simultaneously, it can be stated that the mapping language still has implementation-related shortcomings that can be compensated by Mirth Connect as a base technology.

LLM-AIx: An open source pipeline for Information Extraction from unstructured medical text based on privacy preserving Large Language Models

In clinical science and practice, text data, such as clinical letters or procedure reports, is stored in an unstructured way. This type of data is not a quantifiable resource for any kind of quantitative investigations and any manual review or structured information retrieval is time-consuming and costly. The capabilities of Large Language Models (LLMs) mark a paradigm shift in natural language processing and offer new possibilities for structured Information Extraction (IE) from medical free text. This protocol describes a workflow for LLM based information extraction (LLM-AIx), enabling extraction of predefined entities from unstructured text using privacy preserving LLMs. By converting unstructured clinical text into structured data, LLM-AIx addresses a critical barrier in clinical research and practice, where the efficient extraction of information is essential for improving clinical decision-making, enhancing patient outcomes, and facilitating large-scale data analysis. The protocol consists of four main processing steps: 1) Problem definition and data preparation, 2) data preprocessing, 3) LLM-based IE and 4) output evaluation. LLM-AIx allows integration on local hospital hardware without the need of transferring any patient data to external servers. As example tasks, we applied LLM-AIx for the anonymization of fictitious clinical letters from patients with pulmonary embolism. Additionally, we extracted symptoms and laterality of the pulmonary embolism of these fictitious letters. We demonstrate troubleshooting for potential problems within the pipeline with an IE on a real-world dataset, 100 pathology reports from the Cancer Genome Atlas Program (TCGA), for TNM stage extraction. LLM-AIx can be executed without any programming knowledge via an easy-to-use interface and in no more than a few minutes or hours, depending on the LLM model selected.

Anesthesia decision analysis using a cloud-based big data platform

Big data technologies have proliferated since the dawn of the cloud-computing era. Traditional data storage, extraction, transformation, and analysis technologies have thus become unsuitable for the large volume, diversity, high processing speed, and low value density of big data in medical strategies, which require the development of novel big data application technologies. In this regard, we investigated the most recent big data platform breakthroughs in anesthesiology and designed an anesthesia decision model based on a cloud system for storing and analyzing massive amounts of data from anesthetic records. The presented Anesthesia Decision Analysis Platform performs distributed computing on medical records via several programming tools, and provides services such as keyword search, data filtering, and basic statistics to reduce inaccurate and subjective judgments by decision-makers. Importantly, it can potentially to improve anesthetic strategy and create individualized anesthesia decisions, lowering the likelihood of perioperative complications.

Use of Metadata-Driven Approaches for Data Harmonization in the Medical Domain: Scoping Review

BACKGROUND

Multisite clinical studies are increasingly using real-world data to gain real-world evidence. However, due to the heterogeneity of source data, it is difficult to analyze such data in a unified way across clinics. Therefore, the implementation of Extract-Transform-Load (ETL) or Extract-Load-Transform (ELT) processes for harmonizing local health data is necessary, in order to guarantee the data quality for research. However, the development of such processes is time-consuming and unsustainable. A promising way to ease this is the generalization of ETL/ELT processes.

OBJECTIVE

In this work, we investigate existing possibilities for the development of generic ETL/ELT processes. Particularly, we focus on approaches with low development complexity by using descriptive metadata and structural metadata.

METHODS

We conducted a literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We used 4 publication databases (ie, PubMed, IEEE Explore, Web of Science, and Biomed Center) to search for relevant publications from 2012 to 2022. The PRISMA flow was then visualized using an R-based tool (Evidence Synthesis Hackathon). All relevant contents of the publications were extracted into a spreadsheet for further analysis and visualization.

RESULTS

Regarding the PRISMA guidelines, we included 33 publications in this literature review. All included publications were categorized into 7 different focus groups (ie, medicine, data warehouse, big data, industry, geoinformatics, archaeology, and military). Based on the extracted data, ontology-based and rule-based approaches were the 2 most used approaches in different thematic categories. Different approaches and tools were chosen to achieve different purposes within the use cases.

CONCLUSIONS

Our literature review shows that using metadata-driven (MDD) approaches to develop an ETL/ELT process can serve different purposes in different thematic categories. The results show that it is promising to implement an ETL/ELT process by applying MDD approach to automate the data transformation from Fast Healthcare Interoperability Resources to Observational Medical Outcomes Partnership Common Data Model. However, the determining of an appropriate MDD approach and tool to implement such an ETL/ELT process remains a challenge. This is due to the lack of comprehensive insight into the characterizations of the MDD approaches presented in this study. Therefore, our next step is to evaluate the MDD approaches presented in this study and to determine the most appropriate MDD approaches and the way to integrate them into the ETL/ELT process. This could verify the ability of using MDD approaches to generalize the ETL process for harmonizing medical data.

Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record

The electronic health record (EHR) represents a rich source of patient information, increasingly being leveraged for cardiovascular research. Although its primary use remains the seamless delivery of health care, the various longitudinally aggregated structured and unstructured data elements for each patient within the EHR can define the computational phenotypes of disease and care signatures and their association with outcomes. Although structured data elements, such as demographic characteristics, laboratory measurements, problem lists, and medications, are easily extracted, unstructured data are underused. The latter include free text in clinical narratives, documentation of procedures, and reports of imaging and pathology. Rapid scaling up of data storage and rapid innovation in natural language processing and computer vision can power insights from unstructured data streams. However, despite an array of opportunities for research using the EHR, specific expertise is necessary to adequately address confidentiality, accuracy, completeness, and heterogeneity challenges in EHR-based research. These often require methodological innovation and best practices to design and conduct successful research studies. Our review discusses these challenges and their proposed solutions. In addition, we highlight the ongoing innovations in federated learning in the EHR through a greater focus on common data models and discuss ongoing work that defines such an approach to large-scale, multicenter, federated studies. Such parallel improvements in technology and research methods enable innovative care and optimization of patient outcomes.

Engaging Patients and Other Stakeholders in "Designing for Dissemination" of Record Linkage Methods and Tools

BACKGROUND

Novel record linkage (RL) methods have the potential to enhance clinical informatics by integrating patient data from multiple sources-including electronic health records, insurance claims, and digital health devices-to inform patient-centered care. Engaging patients and other stakeholders in the use of RL methods in patient-centered outcomes research (PCOR) is a key step in ensuring RL methods are viewed as acceptable, appropriate, and useful. The University of Colorado Record Linkage (CURL) platform empowers the use of RL in PCOR.

OBJECTIVES

This study aimed to describe the process of engaging patients and other stakeholders in the design of an RL dissemination package to support the use of RL methods in PCOR.

METHODS

Customer discovery, value proposition design, and user experience methods were used to iteratively develop an RL dissemination package that includes animated explainer videos for patients and an RL research planning workbook for researchers. Patients and other stakeholders (researchers, data managers, and regulatory officials) were engaged in the RL dissemination package design.

RESULTS

Patient partners emphasized the importance of conveying how RL methods may benefit patients and the rules researchers must follow to protect the privacy and security of patient data. Other stakeholders described accuracy, flexibility, efficiency, and data security compared with other available RL solutions. Dissemination package communication products reflect the value propositions identified by key stakeholders. As prioritized by patients, the animated explainer videos emphasize the data privacy and security processes and procedures employed when performing research using RL. The RL workbook addresses researchers' and data managers' needs to iteratively design RL projects and provides accompanying resources to alleviate leadership and regulatory officials' concerns about data regulation compliance.

CONCLUSION

Dissemination products to promote adoption and use of CURL include materials to facilitate patient engagement in RL research and investigator step-by-step decision-making materials about the integration of RL methods in PCOR.

Improved guideline adherence in oncology through clinical decision-support systems: still hindered by current health IT infrastructures?

PURPOSE OF REVIEW

Despite several efforts to enhance guideline adherence in cancer management, the rate of adherence remains often dissatisfactory in clinical routine. Clinical decision-support systems (CDSS) have been developed to support the management of cancer patients by providing evidence-based recommendations. In this review, we focus on both current evidence supporting the beneficial effects of CDSS on guideline adherence as well as technical and structural requirements for CDSS implementation in clinical routine.

RECENT FINDINGS

Some studies have demonstrated a significant improvement of guideline adherence by CDSSs in oncologic diseases such as breast cancer, colon cancer, cervical cancer, prostate cancer, and hepatocellular carcinoma as well as in the management of cancer pain. However, most of these studies were rather small and designs rather simple. One reason for this limited evidence might be that CDSSs are only occasionally implemented in clinical routine. The main limitations for a broader implementation might lie in the currently existing clinical data infrastructures that do not sufficiently allow CDSS interoperability as well as in some CDSS tools themselves, if handling is hampered by poor usability.

SUMMARY

In principle, CDSSs improve guideline adherence in clinical cancer management. However, there are some technical und structural obstacles to overcome to fully implement CDSSs in clinical routine.

TransformEHRs: a flexible methodology for building transparent ETL processes for EHR reuse

BACKGROUND

 During the COVID-19 pandemic, several methodologies were designed for obtaining electronic health record (EHR)-derived datasets for research. These processes are often based on black boxes, on which clinical researchers are unaware of how the data were recorded, extracted, and transformed. In order to solve this, it is essential that extract, transform, and load (ETL) processes are based on transparent, homogeneous, and formal methodologies, making them understandable, reproducible, and auditable.

OBJECTIVES

 This study aims to design and implement a methodology, according with FAIR Principles, for building ETL processes (focused on data extraction, selection, and transformation) for EHR reuse in a transparent and flexible manner, applicable to any clinical condition and health care organization.

METHODS

 The proposed methodology comprises four stages: (1) analysis of secondary use models and identification of data operations, based on internationally used clinical repositories, case report forms, and aggregated datasets; (2) modeling and formalization of data operations, through the paradigm of the Detailed Clinical Models; (3) agnostic development of data operations, selecting SQL and R as programming languages; and (4) automation of the ETL instantiation, building a formal configuration file with XML.

RESULTS

 First, four international projects were analyzed to identify 17 operations, necessary to obtain datasets according to the specifications of these projects from the EHR. With this, each of the data operations was formalized, using the ISO 13606 reference model, specifying the valid data types as arguments, inputs and outputs, and their cardinality. Then, an agnostic catalog of data was developed through data-oriented programming languages previously selected. Finally, an automated ETL instantiation process was built from an ETL configuration file formally defined.

CONCLUSIONS

 This study has provided a transparent and flexible solution to the difficulty of making the processes for obtaining EHR-derived data for secondary use understandable, auditable, and reproducible. Moreover, the abstraction carried out in this study means that any previous EHR reuse methodology can incorporate these results into them.

Measuring the impact of anonymization on real-world consolidated health datasets engineered for secondary research use: Experiments in the context of MODELHealth project

Introduction

Electronic Health Records (EHRs) are essential data structures, enabling the sharing of valuable medical care information for a diverse patient population and being reused as input to predictive models for clinical research. However, issues such as the heterogeneity of EHR data and the potential compromisation of patient privacy inhibit the secondary use of EHR data in clinical research.

Objectives

This study aims to present the main elements of the MODELHealth project implementation and the evaluation method that was followed to assess the efficiency of its mechanism.

Methods

The MODELHealth project was implemented as an Extract-Transform-Load system that collects data from the hospital databases, performs harmonization to the HL7 FHIR standard and anonymization using the k-anonymity method, before loading the transformed data to a central repository. The integrity of the anonymization process was validated by developing a database query tool. The information loss occurring due to the anonymization was estimated with the metrics of generalized information loss, discernibility and average equivalence class size for various values of k.

Results

The average values of generalized information loss, discernibility and average equivalence class size obtained across all tested datasets and k values were 0.008473 ± 0.006216252886, 115,145,464.3 ± 79,724,196.11 and 12.1346 ± 6.76096647, correspondingly. The values of those metrics appear correlated with factors such as the k value and the dataset characteristics, as expected.

Conclusion

The experimental results of the study demonstrate that it is feasible to perform effective harmonization and anonymization on EHR data while preserving essential patient information.

The way forward for neuroethics in Japan: A review of five topics surrounding present challenges

Neuroethics is the study of how neuroscience impacts humans and society. About 15 years have passed since neuroethics was introduced to Japan, yet the field of neuroethics still seeks developed methodologies and an established academic identity. In light of progress in neuroscience and neurotechnology, the challenges for Japanese neuroethics in the 2020s can be categorized into five topics. (1) The need for further research into the importance of informed consent in psychiatric research and the promotion of public-patient engagement. (2) The need for a framework that constructs a global environment for neuroscience research that utilizes reliable samples and data. (3) The need for ethical support within a Japanese context regarding the construction of brain banks and the research surrounding their use. It is also important to reconsider the moral value of the human neural system and make comparisons with non-human primates. (4) An urgent need to study neuromodulation technologies that intervene in emotions. (5) The need to reconsider neuroscience and neurotechnology from social points of view. Rules for neuroenhancements and do-it-yourself neurotechnologies are urgently needed, while from a broader perspective, it is essential to study the points of contact between neuroscience and public health.

The Collaborative Metadata Repository (CoMetaR) Web App: Quantitative and Qualitative Usability Evaluation

Background

In the field of medicine and medical informatics, the importance of comprehensive metadata has long been recognized, and the composition of metadata has become its own field of profession and research. To ensure sustainable and meaningful metadata are maintained, standards and guidelines such as the FAIR (Findability, Accessibility, Interoperability, Reusability) principles have been published. The compilation and maintenance of metadata is performed by field experts supported by metadata management apps. The usability of these apps, for example, in terms of ease of use, efficiency, and error tolerance, crucially determines their benefit to those interested in the data.

Objective

This study aims to provide a metadata management app with high usability that assists scientists in compiling and using rich metadata. We aim to evaluate our recently developed interactive web app for our collaborative metadata repository (CoMetaR). This study reflects how real users perceive the app by assessing usability scores and explicit usability issues.

Methods

We evaluated the CoMetaR web app by measuring the usability of 3 modules: core module, provenance module, and data integration module. We defined 10 tasks in which users must acquire information specific to their user role. The participants were asked to complete the tasks in a live web meeting. We used the System Usability Scale questionnaire to measure the usability of the app. For qualitative analysis, we applied a modified think aloud method with the following thematic analysis and categorization into the ISO 9241-110 usability categories.

Results

A total of 12 individuals participated in the study. We found that over 97% (85/88) of all the tasks were completed successfully. We measured usability scores of 81, 81, and 72 for the 3 evaluated modules. The qualitative analysis resulted in 24 issues with the app.

Conclusions

A usability score of 81 implies very good usability for the 2 modules, whereas a usability score of 72 still indicates acceptable usability for the third module. We identified 24 issues that serve as starting points for further development. Our method proved to be effective and efficient in terms of effort and outcome. It can be adapted to evaluate apps within the medical informatics field and potentially beyond.

Automated production of research data marts from a canonical fast healthcare interoperability resource data repository: applications to COVID-19 research

OBJECTIVE

The rapidly evolving COVID-19 pandemic has created a need for timely data from the healthcare systems for research. To meet this need, several large new data consortia have been developed that require frequent updating and sharing of electronic health record (EHR) data in different common data models (CDMs) to create multi-institutional databases for research. Traditionally, each CDM has had a custom pipeline for extract, transform, and load operations for production and incremental updates of data feeds to the networks from raw EHR data. However, the demands of COVID-19 research for timely data are far higher, and the requirements for updating faster than previous collaborative research using national data networks have increased. New approaches need to be developed to address these demands.

METHODS

In this article, we describe the use of the Fast Healthcare Interoperability Resource (FHIR) data model as a canonical data model and the automated transformation of clinical data to the Patient-Centered Outcomes Research Network (PCORnet) and Observational Medical Outcomes Partnership (OMOP) CDMs for data sharing and research collaboration on COVID-19.

RESULTS

FHIR data resources could be transformed to operational PCORnet and OMOP CDMs with minimal production delays through a combination of real-time and postprocessing steps, leveraging the FHIR data subscription feature.

CONCLUSIONS

The approach leverages evolving standards for the availability of EHR data developed to facilitate data exchange under the 21st Century Cures Act and could greatly enhance the availability of standardized datasets for research.

Automated Production of Research Data Marts from a Canonical Fast Healthcare Interoperability Resource (FHIR) Data Repository: Applications to COVID-19 Research

Objective

Objective: The COVID-19 pandemic has enhanced the need for timely real-world data (RWD) for research. To meet this need, several large clinical consortia have developed networks for access to RWD from electronic health records (EHR), each with its own common data model (CDM) and custom pipeline for extraction, transformation, and load operations for production and incremental updating. However, the demands of COVID-19 research for timely RWD (e.g., 2-week delay) make this less feasible.

Methods and Materials

We describe the use of the Fast Healthcare Interoperability Resource (FHIR) data model as a canonical model for representation of clinical data for automated transformation to the Patient-Centered Outcomes Research Network (PCORnet) and Observational Medical Outcomes Partnership (OMOP) CDMs and the near automated production of linked clinical data repositories (CDRs) for COVID-19 research using the FHIR subscription standard. The approach was applied to healthcare data from a large academic institution and was evaluated using published quality assessment tools.

Results

Six years of data (1.07M patients, 10.1M encounters, 137M laboratory results), were loaded into the FHIR CDR producing 3 linked real-time linked repositories: FHIR, PCORnet, and OMOP. PCORnet and OMOP databases were refined in subsequent post processing steps into production releases and met published quality standards. The approach greatly reduced CDM production efforts.

Conclusions

FHIR and FHIR CDRs can play an important role in enhancing the availability of RWD from EHR systems. The above approach leverages 21 ^st Century Cures Act mandated standards and could greatly enhance the availability of datasets for research.

CLIN-IK-LINKS: A platform for the design and execution of clinical data transformation and reasoning workflows

BACKGROUND AND OBJECTIVE

Effective sharing and reuse of Electronic Health Records (EHR) requires technological solutions which deal with different representations and different models of data. This includes information models, domain models and, ideally, inference models, which enable clinical decision support based on a knowledge base and facts. Our goal is to develop a framework to support EHR interoperability based on transformation and reasoning services intended for clinical data and knowledge.

METHODS

Our framework is based on workflows whose primary components are reusable mappings. Key features are an integrated representation, storage, and exploitation of different types of mappings for clinical data transformation purposes, as well as the support for the discovery of new workflows. The current framework supports mappings which take advantage of the best features of EHR standards and ontologies. Our proposal is based on our previous results and experience working with both technological infrastructures.

RESULTS

We have implemented CLIN-IK-LINKS, a web-based platform that enables users to create, modify and delete mappings as well as to define and execute workflows. The platform has been applied in two use cases: semantic publishing of clinical laboratory test results; and implementation of two colorectal cancer screening protocols. Real data have been used in both use cases.

CONCLUSIONS

The CLIN-IK-LINKS platform allows the composition and execution of clinical data transformation workflows to convert EHR data into EHR and/or semantic web standards. Having proved its usefulness to implement clinical data transformation applications of interest, CLIN-IK-LINKS can be regarded as a valuable contribution to improve the semantic interoperability of EHR systems.

SCALPEL3: A scalable open-source library for healthcare claims databases

OBJECTIVE

This article introduces SCALPEL3 (Scalable Pipeline for Health Data), a scalable open-source framework for studies involving Large Observational Databases (LODs). It focuses on scalable medical concept extraction, easy interactive analysis, and helpers for data flow analysis to accelerate studies performed on LODs.

MATERIALS AND METHODS

Inspired from web analytics, SCALPEL3 relies on distributed computing, data denormalization and columnar storage. It was compared to the existing SAS-Oracle SNDS infrastructure by performing several queries on a dataset containing a three years-long history of healthcare claims of 13.7 million patients.

RESULTS AND DISCUSSION

SCALPEL3 horizontal scalability allows handling large tasks quicker than the existing infrastructure while it has comparable performance when using only a few executors. SCALPEL3 provides a sharp interactive control of data processing through legible code, which helps to build studies with full reproducibility, leading to improved maintainability and audit of studies performed on LODs.

CONCLUSION

SCALPEL3 makes studies based on SNDS much easier and more scalable than the existing framework [1]. It is now used at the agency collecting SNDS data, at the French Ministry of Health and soon at the National Health Data Hub in France [2].

The Generalized Data Model for clinical research

BACKGROUND

Most healthcare data sources store information within their own unique schemas, making reliable and reproducible research challenging. Consequently, researchers have adopted various data models to improve the efficiency of research. Transforming and loading data into these models is a labor-intensive process that can alter the semantics of the original data. Therefore, we created a data model with a hierarchical structure that simplifies the transformation process and minimizes data alteration.

METHODS

There were two design goals in constructing the tables and table relationships for the Generalized Data Model (GDM). The first was to focus on clinical codes in their original vocabularies to retain the original semantic representation of the data. The second was to retain hierarchical information present in the original data while retaining provenance. The model was tested by transforming synthetic Medicare data; Surveillance, Epidemiology, and End Results data linked to Medicare claims; and electronic health records from the Clinical Practice Research Datalink. We also tested a subsequent transformation from the GDM into the Sentinel data model.

RESULTS

The resulting data model contains 19 tables, with the Clinical Codes, Contexts, and Collections tables serving as the core of the model, and containing most of the clinical, provenance, and hierarchical information. In addition, a Mapping table allows users to apply an arbitrarily complex set of relationships among vocabulary elements to facilitate automated analyses.

CONCLUSIONS

The GDM offers researchers a simpler process for transforming data, clear data provenance, and a path for users to transform their data into other data models. The GDM is designed to retain hierarchical relationships among data elements as well as the original semantic representation of the data, ensuring consistency in protocol implementation as part of a complete data pipeline for researchers.

Colorado Asthma Toolkit Implementation Improves Some Process Measures of Asthma Care

BACKGROUND

The Colorado Asthma Toolkit Program (CATP) has been shown to improve processes of care with less evidence demonstrating improved outcomes.

OBJECTIVE

To model the association between pre-and-post-CATP status and asthma-related process and outcome measures among patients ages 5 to 64 years receiving care in safety-net primary care practices.

METHODS

This is an implementation study involving secondary prepost analysis of existing structured clinical, administrative, and claims data. Nine primary care practices in a federally qualified health center network implemented the CATP. Processes of care and health and utilization outcomes were evaluated prepost implementation in a cohort of patients with asthma using generalized linear mixed models.

RESULTS

The study cohort included 2678 patients age 5 to 64 years with at least one visit to one of the 9 participating practices during the study period (March 12, 2010 to December 1, 2012). A comparison of 12 months pre- and post-CATP implementation showed improvement in some process measures of asthma care associated with the intervention, including the rate of asthma-severity measurement, although no change in 2 Health care Effectiveness Data and Information Set measures: asthma medication ratio and medication management for people with asthma. We also found no change in asthma outcomes measured across multiple domains: exacerbations, utilization, symptom scores, and pulmonary physiology measures.

CONCLUSIONS

Implementation of the CATP in a primary care setting led to some improved processes of asthma care, but no changes in measured outcomes. Recommendations for future work include supplemental follow-up training including case review.