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Examples of standard ICD-9 codes and associated non- standard ICD codes

Examples of standard ICD-9 codes and associated non- standard ICD codes

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Randomized controlled trials face cost, logistic, and generalizability limitations, including difficulty engaging racial/ethnic minorities. Real-world data (RWD) from pragmatic trials, including electronic health record (EHR) data, may produce intervention evaluation findings generalizable to diverse populations. This case study of Project IMPACT d...

Contexts in source publication

Context 1
... pragmatic trials, data accuracy is paramount for valid findings that are applicable to the settings of interest. 22 During validation procedures for IMPACT, we discovered that EHR registry functions could not pull accurate patient lists that matched our outcome definition, thereby compromising the validity of findings (Tables 1 and 2, Figure 2) and limiting the utility of reports for clinic staff. Lack of registry functionality and commonalities across EHR platforms has been documented as a challenge in quality initiatives. ...
Context 2
... One of the 2 EHR platforms allowed providers to create their own ICD codes, resulting in multiple versions of same ICD code (Table 2). ...
Context 3
... example, at 1 clinic, when using 1 standard ICD-9 code for hypertensive diagnosis (401.9), our report yielded many fewer patients than when we included all nonstandard codes (Table 2) for the same diagnosis. ...

Citations

... Thirty-two articles were identified, the most among the trial process tasks. Twenty-eight of the articles were clinical trials while the remaining 4 articles were methodology articles focused on feasibility of representing or capturing outcomes within RWD. [68][69][70][71] Of the 28 clinical trials, most focused on medication-related outcomes, including concordance with guidelines, 39,53,55,58,64,65 patient adherence or discontinuation of medica- Note: We did not find any articles regarding generalizability dissemination, but include it as a potential pathway to connect generalizability assessments and trial process tasks. To represent no articles, the box and arrow are represented as faded dashed lines. ...
... Another prominent observation was that almost all included articles expressed challenges with using RWD, with some being ex-plicit experiences and lessons. 24,43,68 Common challenges typically were related to data availability, data quality, and electronic phenotyping. For data availability, this meant that some relevant components to define a clinical entity were absent. ...
Article
Objective Real-world data (RWD), defined as routinely collected healthcare data, can be a potential catalyst for addressing challenges faced in clinical trials. We performed a scoping review of database-specific RWD applications within clinical trial contexts, synthesizing prominent uses and themes. Materials and Methods Querying 3 biomedical literature databases, research articles using electronic health records, administrative claims databases, or clinical registries either within a clinical trial or in tandem with methodology related to clinical trials were included. Articles were required to use at least 1 US RWD source. All abstract screening, full-text screening, and data extraction was performed by 1 reviewer. Two reviewers independently verified all decisions. Results Of 2020 screened articles, 89 qualified: 59 articles used electronic health records, 29 used administrative claims, and 26 used registries. Our synthesis was driven by the general life cycle of a clinical trial, culminating into 3 major themes: trial process tasks (51 articles); dissemination strategies (6); and generalizability assessments (34). Despite a diverse set of diseases studied, <10% of trials using RWD for trial process tasks evaluated medications or procedures (5/51). All articles highlighted data-related challenges, such as missing values. Discussion Database-specific RWD have been occasionally leveraged for various clinical trial tasks. We observed underuse of RWD within conducted medication or procedure trials, though it is subject to the confounder of implicit report of RWD use. Conclusion Enhanced incorporation of RWD should be further explored for medication or procedure trials, including better understanding of how to handle related data quality issues to facilitate RWD use.
... In particular, EHR data are now able to be rapidly ingested, analyzed, and repurposed to support clinical practice via applications such as clinical decision support systems, supporting progress toward realizing a learning health system that uses data in near-real time. 1 While extracting, transforming, and using data within a single institution or health care system with 1 EHR system can be challenging, incorporating data from multiple information systems that can include third-party EHRs, presents even more challenges. 2 V C The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. ...
Article
Objective: Patients that undergo medical transfer represent 1 patient population that remains infrequently studied due to challenges in aggregating data across multiple domains and sources that are necessary to capture the entire episode of patient care. To facilitate access to and secondary use of transport patient data, we developed the Transport Data Repository that combines data from 3 separate domains and many sources within our health system. Methods: The repository is a relational database anchored by the Unified Medical Language System unique concept identifiers to integrate, map, and standardize the data into a common data model. Primary data domains included sending and receiving hospital encounters, medical transport record, and custom hospital transport log data. A 4-step mapping process was developed: 1) automatic source code match, 2) exact text match, 3) fuzzy matching, and 4) manual matching. Results: 431 090 total mappings were generated in the Transport Data Repository, consisting of 69 010 unique concepts with 77% of the data being mapped automatically. Transport Source Data yielded significantly lower mapping results with only 8% of data entities automatically mapped and a significant amount (43%) remaining unmapped. Discussion: The multistep mapping process resulted in a majority of data been automatically mapped. Poor matching of transport medical record data is due to the third-party vendor data being generated and stored in a nonstandardized format. Conclusion: The multistep mapping process developed and implemented is necessary to normalize electronic health data from multiple domains and sources into a common data model to support secondary use of data.
... In New York City (NYC), small practices comprise 40% of primary care providers (PCPs) and serve NYC's poorest and most racial/ethnically diverse neighborhoods [16]. CDSS QI initiatives often require an infrastructure that is not readily available to small practices and require purchasing of additional software and applications as well as training on these systems to use them accurately [16,17]. Small practices' lack of access to or suboptimal participation in QI initiatives, therefore, can potentially widen the gap in provision of quality care to health-disparity populations [18][19][20][21]. ...
... Relevant elements of the model for this initiative include delivery system design and clinical information systems, which are addressed by enhancing practice capacity to implement registries of individuals with uncontrolled diabetes. Further guided by PCIP's best practices [32,33,36] and literature on common challenges faced by under-resourced practices in implementing QI efforts (with emphasis on employing user-centered strategies) [17,36], implementation was conducted in 3 phases. Each phase of the QI initiative, associated activities, and challenges and strategies used to address them are summarized in (Table 1) and briefly described in the following sections. ...
... Building upon past successful strategies [11,17,32,38], the practice facilitator and an academic research coordinator conducted an initial 1-to 2-hour training with clinic staff and clinicians. We presented the clinics with a generic suggested workflow ( Figure 1) that could be customized to each clinic. ...
... But PCMH recognition is higher among larger and better-resourced practices (Scholle et al. 2013;Berry et al. 2013). Small practices face distinct challenges in transforming their teams, workflow, technology, and finances to achieve patient-centered care, yet little is known about successful strategies to implement PCMH principles and other quality-improvement measures in small practices (Berry et al. 2013;PHIP 2018;Scholle et al. 2013;Divney et al. 2019). ...
... Physicians and staff thus struggled with optimally using their EHR systems. Their struggles reflect challenges common to underresourced practices, who are unable to carefully select and tailor their EHR systems and often use systems with known deficiencies in functionality related to PCMH incentives (Cohen et al. 2018;Divney et al. 2019). ...
... Our findings also show that the practices' organizational capacity for adopting EHR technology constrained their use of the alerts and order sets. Underresourced practices that do not have the capacity to purposefully choose and customize their EHRs may not fully reap the benefits of EHR adoption (Divney et al. 2019). Yet, despite espousing some ambivalence about aspects of alerts and order sets, physicians were keen to sustain their EHR capability for all tools mentioned, a point we return to in the discussion. ...
Article
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Small primary care practices are critical to advancing Affordable Care Act (ACA) aims, yet their efforts and experiences remain little studied. We examine two strategies derived from ACA population-health provisions—enhanced use of electronic health records (EHRs) and community health worker (CHW)–led peer coaching—for hypertension control in sixteen small practices serving South Asian immigrant communities in New York City. Based on interviews with physicians, staff, and CHWs, we analyze “street-level” dilemmas encountered in implementing the strategies. Findings indicate that the strategies reinforce clinic-community social ties but present distinct challenges for small practices: internal management constraints that impede formal CHW-physician contact, and external incentives linked to EHR tools that, physicians and staff perceive, do not meet immigrant communities’ needs and expectations in medical encounters.
... 42 One article, a perspective piece, also argued for expanded collection of social determinants of health data in mental health care 32 and a case report outlined the challenges of participation in a pragmatic trial within small, immigrant-serving healthcare practices. 43 Interventions in development included personal health records for new settings and audiences. 34,36 Another article highlighted rural public health practitioners' information and training needs in preparation for an intervention. ...
... 37 Published articles in clinical research informatics highlight data quality concerns related to information about marginalized groups 24,25 and small healthcare practices that often serve them. 43 Three articles also outlined use of technology to recruit marginalized groups to participate in research. 30,40,41 For the work in consumer health informatics, we note that these articles primarily dealt with patient portals or personal health records, 26,33,34,36,42 with an additional 2 articles using digital trace data from online communities or social media as research data. ...
Article
Full-text available
Health informatics studies the use of information technology to improve human health. As informaticists, we seek to reduce the gaps between current healthcare practices and our societal goals for better health and healthcare quality, safety, or cost. It is time to recognize health equity as one of these societal goals-a point underscored by this Journal of the American Medical Informatics Association Special Focus Issue, "Health Informatics and Health Equity: Improving our Reach and Impact." This Special Issue highlights health informatics research that focuses on marginalized and underserved groups, health disparities, and health equity. In particular, this Special Issue intentionally showcases high-quality research and professional experiences that encompass a broad range of subdisciplines, methods, marginalized populations, and approaches to disparities. Building on this variety of submissions and other recent developments, we highlight contents of the Special Issue and offer an assessment of the state of research at the intersection of health informatics and health equity.
Article
Background Validated computable eligibility criteria use real-world data and facilitate the conduct of clinical trials. The Genomic Medicine at VA (GenoVA) Study is a pragmatic trial of polygenic risk score testing enrolling patients without known diagnoses of 6 common diseases: atrial fibrillation, coronary artery disease, type 2 diabetes, breast cancer, colorectal cancer, and prostate cancer. We describe the validation of computable disease classifiers as eligibility criteria and their performance in the first 16 months of trial enrollment. Methods We identified well-performing published computable classifiers for the 6 target diseases and validated these in the target population using blinded physician review. If needed, classifiers were refined and then underwent a subsequent round of blinded review until true positive and true negative rates ≥80% were achieved. The optimized classifiers were then implemented as pre-screening exclusion criteria; telephone screens enabled an assessment of their real-world negative predictive value (NPV-RW). Results Published classifiers for type 2 diabetes and breast and prostate cancer achieved desired performance in blinded chart review without modification; the classifier for atrial fibrillation required two rounds of refinement before achieving desired performance. Among the 1077 potential participants screened in the first 16 months of enrollment, NPV-RW of the classifiers ranged from 98.4% for coronary artery disease to 99.9% for colorectal cancer. Performance did not differ by gender or race/ethnicity. Conclusions Computable disease classifiers can serve as efficient and accurate pre-screening classifiers for clinical trials, although performance will depend on the trial objectives and diseases under study.
Article
Background: Community health workers (CHWs) have been identified as effective members of health care teams in improving health outcomes and reducing health disparities, especially among racial and ethnic minorities. There is a growing interest in integrating CHWs into clinical settings using health informatics-based strategies to help provide coordinated patient care and foster health-promoting behaviors. Objective: In this scoping review, we outline health informatics-based strategies for CHW-provider communication that aim to improve integration of CHWs into clinical settings. Design: A scoping review was conducted. Eligibility criteria: US-based sources between 2013 and 2018 were eligible. Study selection: Literature was identified through PubMed and Google queries and hand searching key reference lists. Articles were screened by title, abstract, and then full-text. Main outcome measures: Health informatics-based strategies for CHW-provider communication and their impacts on patient care were documented and analyzed. Results: Thirty-one articles discussed health informatics-based strategies for CHW-provider communication and/or integration of CHWs into clinical settings. These strategies include direct CHW documentation of patient encounters in electronic health records (EHRs) and other Web-based applications. The technologies were used to document patient encounters and patient barriers to health care providers but were additionally used for secure messaging and referral systems. These strategies were found to meet the needs of providers and CHWs while facilitating CHW-provider communication, CHW integration, and coordinated care. Conclusions: Health informatics-based strategies for CHW-provider communication are important for facilitating CHW integration and potentially improving patient outcomes and improving disparities among minority populations. This integration can support the development of future disease prevention programs and health care policies in which CHWs are an established part of the public health workforce. However, further investigation must be done on overcoming implementation challenges (eg, lack of time or funding), especially in smaller resource-challenged community-based clinics that serve minority patients.