The test repository is a tree-like organizational structure at the project level. It allows you to hierarchically organize tests within folders and sub-folders. This folder concept is common in some tools and resembles file organization in a computer's operating system.
The Meaningful Use program (see Chapter 1) has propelled the development of both EHR-linked and EHR-integrated registries. For example, EHR-integrated registries have expanded to meet EHR certification requirements and to help health systems meet requirements for workflow efficiency and quality improvement to achieve value-based criteria (e.g., improving population health). EHR-linked registries have grown as the Meaningful Use program specifically requires the reporting of EHR data to external registries (e.g., public health registries, quality reporting registries).4 Meaningful Use Stage-1 provided an optional objective (which became a mandatory objective in Meaningful Use Stage-2) for eligible hospitals and professionals to submit EHR-extracted electronic data to immunization registries.5 Meaningful Use Stage-2 further expanded EHR reporting to cancer registries and other specialized registries (e.g., birth defects, chronic diseases, and traumatic injury registries).6
In addition, different healthcare facilities may use different laboratory tests to measure the same analyte, each of which has a different laboratory code. Discussion is needed across the provider network on how to link lab items, preferably using automated tools and not a manual process, so that a single query across the network will return all the desired data from multiple EHRs for a single registry. In addition, certain lab results are protected by federal and state laws (e.g., lab tests revealing HIV status) and thus might be missing from EHR-extracts reporting to external registries. Further, some laboratory data are accessible to clinicians without incorporation into the EHR; in fact, some lab data require active steps by the clinician to import into the EHR. Inaccurate interpretations may be made without understanding why some lab data are missing from an EHR.
The increasing semantic and syntactic interoperability among healthcare providers is a major driver for EHR-linked registries. EHR-linked research registries often use application programing interfaces hosted by healthcare providers to extract and share standardized EHR data and then use semi-automated approaches to merge the EHR data with existing registry records. Moreover, bi-directionally interoperable EHR-linked registries may also serve an important role by delivering relevant information from a registry back to a clinician (e.g., natural history of disease, safety, effectiveness, and quality).
As a basic good practice, registries should use some form of data curation to review and assess data quality. In the context of EHR-based registries, data quality issues stem from the fact that data extracted from EHRs often requires extensive cleaning and preparation before being imported into registries. EHRs are designed to manage the transaction of healthcare and support clinical workflow and documentation for billing. The purpose of an EHR is not to conduct research, and EHRs are not designed to systemically collect research-grade longitudinal data. As a result, data captured by EHRs are of variable quality.14,45 For example, EHRs often house reliable laboratory and medication data for clinical purposes, but EHRs typically lack consistent and sufficiently detailed data on risk factors, levels of education, or socioeconomic status.16 The quality of source data can affect both the underlying data as represented in a registry and the results generated using such data. Thus, EHR data may not be appropriate for some research purposes.
A formative study done as part of the project Scaling up Care for Perinatal Depression for Improving Maternal and Infant Health in Nigeria, assessed the factors that might promote or hinder the delivery of quality services to women with perinatal depression (appendix 1). All 23 facilities sampled had the lowest level of institutional support for continuous care for depression. Of the 218 patients who screened positive for perinatal depression by use of a validated tool, only three were identified by primary health-care workers. The treatment offered to these three patients was non-existent or grossly inadequate. None were provided with structured psychosocial interventions or offered specific follow-up to address their depression. However, 96% of the women in all sampled facilities reported that the quality of care provided in the clinics was good and of sufficient quality, and 98% reported that they were satisfied with the care they had received.
The development of a national policy and strategy for health system quality is a prerequisite to country-led measurement and is discussed further in the next section.212 Assessing measurement approaches against the standards defined in the national strategy will provide insight on gaps and inefficiencies in measuring quality. Another responsibility of a national institution for health system measurement is the development of the quality measurement toolkit. The toolkit can differ by context and resource availability, but should include three tiers: foundational systems, routine data, and targeted studies. The first tier consists of vital registries to track population births and deaths, supply chain management, and human resources information systems, including provider payment tracking. These elements are fundamental for a sound understanding of the population and the capacity of the health system. The second tier is routine data collection through electronic health records or health information systems; many measures for effective coverage and system competence can be derived from routine health information systems. Accuracy and parsimony are essential to these measurements, because of not only their importance, but also their high potential burden. The third tier consists of targeted health system studies, which include health facility and population surveys and patient registries to probe more deeply into health needs and system performance. Facility assessments must be more agile and responsive to national priorities, with increased emphasis on measures that might be hard to capture in a routine system, including timeliness and accuracy of care delivery and patient experience. Patient registries can be developed as a subset of facility assessments to provide information on health outcomes and patient perspectives over time for priority groups or conditions.213 Population surveys, ideally linked to health facility assessments or routine health system data, can be broadened to address the range of conditions reflected in the SDG agenda and to provide the voice of users and non-users on their needs and outcomes. These surveys will continue to be instrumental in providing data for equity assessment, particularly in lower-income countries. When optimised, the combination of these data sources has powerful potential to advance the quality of health systems. The matrix of tools will differ by context, because one of the aims of the SDG era is for all countries to own their data systems and to define their data needs within a common framework. National ownership of tools at all tiers is important for the results to be integrated and used.208 Regional and global partners can facilitate and catalyse this work by providing public goods of centralised evidence and tools. These can include repositories for available indicators, evidence and guidance on the role of measurement platforms and methods for triangulating across them (eg, in effective coverage estimation), and tools for synthesising insight for dissemination. Regional collaborations might prove beneficial for sharing learning and avoiding duplication of efforts, particularly for small countries. Initiatives such as the Quality of Care Network and the Health Data Collaborative are important steps in this direction.
Gerry Altmiller, EdD, APRN, ACNS-BC, ANEF, FAAN, earned a diploma in nursing from Our Lady of Lourdes School of Nursing in Camden, New Jersey, a baccalaureate degree from La Salle University in Philadelphia, and an advanced practice degree as a clinical nurse specialist from Widener University in Chester, Pennsylvania. She later returned to Widener and earned her doctoral degree in higher education leadership. Gerry is a professor of nursing at The College of New Jersey in Ewing, New Jersey. As a board-certified clinical nurse specialist, she has served in a consultant role for Einstein Healthcare Network in Philadelphia, Pennsylvania for over a decade, supporting direct care nurses in their research, evidence-based practice, and quality improvement projects.
There are numerous benefits of writing unit tests; they help with regression, provide documentation, and facilitate good design. However, hard to read and brittle unit tests can wreak havoc on your code base. This article describes some best practices regarding unit test design for your .NET Core and .NET Standard projects.
A high code coverage percentage is often associated with a higher quality of code. However, the measurement itself can't determine the quality of code. Setting an overly ambitious code coverage percentage goal can be counterproductive. Imagine a complex project with thousands of conditional branches, and imagine that you set a goal of 95% code coverage. Currently the project maintains 90% code coverage. The amount of time it takes to account for all of the edge cases in the remaining 5% could be a massive undertaking, and the value proposition quickly diminishes.
A high code coverage percentage isn't an indicator of success, nor does it imply high code quality. It just represents the amount of code that is covered by unit tests. For more information, see unit testing code coverage.
Try not to introduce dependencies on infrastructure when writing unit tests. The dependencies make the tests slow and brittle and should be reserved for integration tests. You can avoid these dependencies in your application by following the Explicit Dependencies Principle and using Dependency Injection. You can also keep your unit tests in a separate project from your integration tests. This approach ensures your unit test project doesn't have references to or dependencies on infrastructure packages. 2b1af7f3a8