Interoperable genetic lab test reports: mapping key data elements to HL7 FHIR specifications and professional reporting guidelines.

OBJECTIVE: In many cases, genetic testing labs provide their test reports as portable document format files or scanned images, which limits the availability of the contained information to advanced informatics solutions, such as automated clinical decision support systems. One of the promising standards that aims to address this limitation is Health Level Seven International (HL7) Fast Healthcare Interoperability Resources Clinical Genomics Implementation Guide-Release 1 (FHIR CG IG STU1). This study aims to identify various data content of some genetic lab test reports and map them to FHIR CG IG specification to assess its coverage and to provide some suggestions for standard development and implementation. MATERIALS AND METHODS: We analyzed sample reports of 4 genetic tests and relevant professional reporting guidelines to identify their key data elements (KDEs) that were then mapped to FHIR CG IG. RESULTS: We identified 36 common KDEs among the analyzed genetic test reports, in addition to other unique KDEs for each genetic test. Relevant suggestions were made to guide the standard implementation and development. DISCUSSION AND CONCLUSION: The FHIR CG IG covers the majority of the identified KDEs. However, we suggested some FHIR extensions that might better represent some KDEs. These extensions may be relevant to FHIR implementations or future FHIR updates.The FHIR CG IG is an excellent step toward the interoperability of genetic lab test reports. However, it is a work-in-progress that needs informative and continuous input from the clinical genetics' community, specifically professional organizations, systems implementers, and genetic knowledgebase providers.

A qualitative investigation of biomedical informatics interoperability standards for genetic test reporting: benefits, challenges, and motivations from the testing laboratory's perspective.

PURPOSE: Genetic laboratory test reports can often be of limited computational utility to the receiving clinical information systems, such as clinical decision support systems. Many health-care interoperability (HC) standards aim to tackle this problem, but the perceived benefits, challenges, and motivations for implementing HC interoperability standards from the labs' perspective has not been systematically assessed. METHODS: We surveyed genetic testing labs across the United States and conducted a semistructured interview with responding lab representatives. We conducted a thematic analysis of the interview transcripts to identify relevant themes. A panel of experts discussed and validated the identified themes. RESULTS: Nine labs participated in the interview, and 24 relevant themes were identified within five domains. These themes included the challenge of complex and changing genetic knowledge, the motivation of competitive advantage, provided financial incentives, and the benefit of supporting the learning health system. CONCLUSION: Our study identified the labs' perspective on various aspects of implementing HC interoperability standards in producing and communicating genetic test reports. Interviewees frequently reported that increased adoption of HC standards may be motivated by competition and programs incentivizing and regulating the incorporation of interoperability standards for genetic test data, which could benefit quality control, research, and other areas.

A qualitative study of prevalent laboratory information systems and data communication patterns for genetic test reporting.

PURPOSE: The availability of genetic test data within the electronic health record (EHR) is a pillar of the US vision for an interoperable health IT infrastructure and a learning health system. Although EHRs have been highly investigated, evaluation of the information systems used by the genetic labs has received less attention-but is necessary for achieving optimal interoperability. This study aimed to characterize how US genetic testing labs handle their information processing tasks. METHODS: We followed a qualitative research method that included interviewing lab representatives and a panel discussion to characterize the information flow models. RESULTS: Ten labs participated in the study. We identified three generic lab system models and their relevant characteristics: a backbone system with additional specialized systems for interpreting genetic results, a brokering system that handles housekeeping and communication, and a single primary system for results interpretation and report generation. CONCLUSION: Labs have heterogeneous workflows and generally have a low adoption of standards when sending genetic test reports back to EHRs. Core interpretations are often delivered as free text, limiting their computational availability for clinical decision support tools. Increased provision of genetic test data in discrete and standard-based formats by labs will benefit individual and public health.

Descriptive Usability Study of CirrODS: Clinical Decision and Workflow Support Tool for Management of Patients With Cirrhosis.

BACKGROUND: There are gaps in delivering evidence-based care for patients with chronic liver disease and cirrhosis. OBJECTIVE: Our objective was to use interactive user-centered design methods to develop the Cirrhosis Order Set and Clinical Decision Support (CirrODS) tool in order to improve clinical decision-making and workflow. METHODS: Two work groups were convened with clinicians, user experience designers, human factors and health services researchers, and information technologists to create user interface designs. CirrODS prototypes underwent several rounds of formative design. Physicians (n=20) at three hospitals were provided with clinical scenarios of patients with cirrhosis, and the admission orders made with and without the CirrODS tool were compared. The physicians rated their experience using CirrODS and provided comments, which we coded into categories and themes. We assessed the safety, usability, and quality of CirrODS using qualitative and quantitative methods. RESULTS: We created an interactive CirrODS prototype that displays an alert when existing electronic data indicate a patient is at risk for cirrhosis. The tool consists of two primary frames, presenting relevant patient data and allowing recommended evidence-based tests and treatments to be ordered and categorized. Physicians viewed the tool positively and suggested that it would be most useful at the time of admission. When using the tool, the clinicians placed fewer orders than they placed when not using the tool, but more of the orders placed were considered to be high priority when the tool was used than when it was not used. The physicians' ratings of CirrODS indicated above average usability. CONCLUSIONS: We developed a novel Web-based combined clinical decision-making and workflow support tool to alert and assist clinicians caring for patients with cirrhosis. Further studies are underway to assess the impact on quality of care for patients with cirrhosis in actual practice.

Linkage between Utah All Payers Claims Database and Central Cancer Registry.

OBJECTIVE: To evaluate the linkage of claims from the Utah All Payers Claims Database (APCD) and Utah Cancer Registry (UCR). DATA SOURCES: Secondary data from 2013 and 2014 Utah APCD and 2013 UCR cases. STUDY DESIGN: This is a descriptive analysis of the quality of linkage between APCD claims data and cancer registry cases. DATA COLLECTION/EXTRACTION METHODS: We used the LinkPlus software to link Utah APCD and UCR data. PRINCIPAL FINDINGS: We were able to link 82.4 percent (9441/11 453) of the UCR reportable cancer cases with APCD claims. Of those linked, 66 percent were perfect matches. CONCLUSIONS: The quality of identifiers is high, evidence that claims data can potentially supplement cancer registry data for use in research.

Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs.

BACKGROUND: We developed an accurate, stakeholder-informed, automated, natural language processing (NLP) system to measure the quality of heart failure (HF) inpatient care, and explored the potential for adoption of this system within an integrated health care system. OBJECTIVE: To accurately automate a United States Department of Veterans Affairs (VA) quality measure for inpatients with HF. METHODS: We automated the HF quality measure Congestive Heart Failure Inpatient Measure 19 (CHI19) that identifies whether a given patient has left ventricular ejection fraction (LVEF) <40%, and if so, whether an angiotensin-converting enzyme inhibitor or angiotensin-receptor blocker was prescribed at discharge if there were no contraindications. We used documents from 1083 unique inpatients from eight VA medical centers to develop a reference standard (RS) to train (n=314) and test (n=769) the Congestive Heart Failure Information Extraction Framework (CHIEF). We also conducted semi-structured interviews (n=15) for stakeholder feedback on implementation of the CHIEF. RESULTS: The CHIEF classified each hospitalization in the test set with a sensitivity (SN) of 98.9% and positive predictive value of 98.7%, compared with an RS and SN of 98.5% for available External Peer Review Program assessments. Of the 1083 patients available for the NLP system, the CHIEF evaluated and classified 100% of cases. Stakeholders identified potential implementation facilitators and clinical uses of the CHIEF. CONCLUSIONS: The CHIEF provided complete data for all patients in the cohort and could potentially improve the efficiency, timeliness, and utility of HF quality measurements.

Evaluation of PHI Hunter in Natural Language Processing Research.

OBJECTIVES: We introduce and evaluate a new, easily accessible tool using a common statistical analysis and business analytics software suite, SAS, which can be programmed to remove specific protected health information (PHI) from a text document. Removal of PHI is important because the quantity of text documents used for research with natural language processing (NLP) is increasing. When using existing data for research, an investigator must remove all PHI not needed for the research to comply with human subjects' right to privacy. This process is similar, but not identical, to de-identification of a given set of documents. MATERIALS AND METHODS: PHI Hunter removes PHI from free-form text. It is a set of rules to identify and remove patterns in text. PHI Hunter was applied to 473 Department of Veterans Affairs (VA) text documents randomly drawn from a research corpus stored as unstructured text in VA files. RESULTS: PHI Hunter performed well with PHI in the form of identification numbers such as Social Security numbers, phone numbers, and medical record numbers. The most commonly missed PHI items were names and locations. Incorrect removal of information occurred with text that looked like identification numbers. DISCUSSION: PHI Hunter fills a niche role that is related to but not equal to the role of de-identification tools. It gives research staff a tool to reasonably increase patient privacy. It performs well for highly sensitive PHI categories that are rarely used in research, but still shows possible areas for improvement. More development for patterns of text and linked demographic tables from electronic health records (EHRs) would improve the program so that more precise identifiable information can be removed. CONCLUSIONS: PHI Hunter is an accessible tool that can flexibly remove PHI not needed for research. If it can be tailored to the specific data set via linked demographic tables, its performance will improve in each new document set.

Exploration of ICD-9-CM coding of chronic disease within the Elixhauser Comorbidity Measure in patients with chronic heart failure.

INTRODUCTION: International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes capture comorbidities that can be used to risk adjust nonrandom patient groups. We explored the accuracy of capturing comorbidities associated with one risk adjustment method, the Elixhauser Comorbidity Measure (ECM), in patients with chronic heart failure (CHF) at one Veterans Affairs (VA) medical center. We explored potential reasons for the differences found between the original codes assigned and conditions found through retrospective review. METHODS: This descriptive, retrospective study used a cohort of patients discharged with a principal diagnosis coded as CHF from one VA medical center in 2003. One admission per patient was used in the study; with multiple admissions, only the first admission was analyzed. We compared the assignment of original codes assigned to conditions found in a retrospective, manual review of the medical record conducted by an investigator with coding expertise as well as by physicians. Members of the team experienced with assigning ICD-9-CM codes and VA coding processes developed themes related to systemic reasons why chronic conditions were not coded in VA records using applied thematic techniques. RESULTS: In the 181-patient cohort, 388 comorbid conditions were identified; 305 of these were chronic conditions, originally coded at the time of discharge with an average of 1.7 comorbidities related to the ECM per patient. The review by an investigator with coding expertise revealed a total of 937 comorbidities resulting in 618 chronic comorbid conditions with an average of 3.4 per patient; physician review found 872 total comorbidities with 562 chronic conditions (average 3.1 per patient). The agreement between the original and the retrospective coding review was 88 percent. The kappa statistic for the original and the retrospective coding review was 0.375 with a 95 percent confidence interval (CI) of 0.352 to 0.398. The kappa statistic for the retrospective coding review and physician review was 0.849 (CI, 0.823-0.875). The kappa statistic for the original coding and the physician review was 0.340 (CI, 0.316-0.364). Several systemic factors were identified, including familiarity with inpatient VA and non-VA guidelines, the quality of documentation, and operational requirements to complete the coding process within short time frames and to identify the reasons for movement within a given facility. CONCLUSION: Comorbidities within the ECM representing chronic conditions were significantly underrepresented in the original code assignment. Contributing factors potentially include prioritization of codes related to acute conditions over chronic conditions; coders' professional training, educational level, and experience; and the limited number of codes allowed in initial coding software. This study highlights the need to evaluate systemic causes of underrepresentation of chronic conditions to improve the accuracy of risk adjustment used for health services research, resource allocation, and performance measurement.

Reducing healthcare disparities. How HIM workforce diversity helps.

HIM professionals can play an important role in reducing disparities in care that result from race and ethnicity. One practical step is by increasing the diversity of the HIM workforce.

The effectiveness of ICD-10-CM in capturing public health diseases.

This research study examined the usefulness of the ICD-10-CM system in capturing public health diseases (reportable diseases or the nationally notifiable infectious diseases, leading causes of death, and morbidity/mortality related to terrorism), when compared to ICD-9-CM.1-3 It also examined agreement levels of coders when coding public health diseases in both ICD-10-CM and ICD-9-CM. Overall results demonstrate that ICD-10-CM is more specific and fully captures more of the public health diseases examined than ICD-9-CM. In the analysis of all the public health diseases, such as reportable diseases (p<0.001), top 10 causes of death (p<0.001), and those related to terrorism (p<0.001), it was found that the overall rankings for disease capture for ICD-10-CM were significantly higher than the rankings for ICD-9-CM. When examining whether diseases were captured more straightforwardly and clearly (regarding agreement levels) between coding systems, statistically significant differences were found for external causes of injury (p<0.001), diabetes (average rank only, p<0.05), lower respiratory disease (p<0.001), heart disease (p<0.001), and malignant neoplasms (p<0.05). Although this result may be due to the coder's higher level of experience with ICD-9-CM, it also points to the potential need for more specific coding education and practice with the ICD-10-CM system.

Development of a public health assessment tool to prevent Lyme disease: tool construction and validation.

This study involved the design and validation of a new Lyme disease risk assessment instrument. The study was funded in part by a research grant from the American Health Information Management Association (AHIMA) Foundation on Research and Education (FORE). The resulting instrument measured theoretical constructs such as attitudes, behaviors, beliefs, skills, and knowledge relative to Lyme disease. The survey assessment tool is described here, and the tool development process, the validation and reliability process, and results are presented. The assessment tool was created by using a standard instrument development process that first involved constructing possible items (questions) based on several health behavior theories and known health risk behaviors. These items were then further refined by using focus groups, a small pilot study, factor analysis, and a large-scale pilot study. Validity and reliability indices were established with a test-retest reliability coefficient of .66, and finally the tool was used among a population living in a Lyme-disease-endemic area. Cronbach's alpha coefficients of .737 for behavioral items, .573 for cognitive items, and .331 for environmental items were established.

Current coding competency compared to projected competency.

Coding competency is extremely important to the health information management (HIM) profession and healthcare in general. The research presented in this article evaluates coding skill and competency using practice-based research. The projected skill set for the clinical data specialist, the future coding role set forth in the publication Evolving HIM Careers, was used to determine how prepared current coders are in terms of projected competencies. To conduct this investigation, a random sample of coders and non-coders were surveyed to determine how well the current level of skills relate to the skills described for the clinical data specialist. In addition to evaluating the skills of current coders, non-coders were used to determine whether there was a statistically significant difference between coders and non-coders' skills relative to the future competencies. If the coders and non-coders had similar self-assessed skills, the validity of the skill set would be questionable. If however, the self-assessed skill was significantly different, the assertion that the skill set is specific to coders would be more credible. The findings from the research suggest that there are many skills projected for the clinical data specialist that are shared by both coders and non-coders. Also, neither coders nor non-coders reflected the level of competence in their self-assessed skills in many areas, such as understanding coding and classification systems other than ICD-9 and CPT, designing audit tools, performing quality audits, and selecting statistical software applications appropriate to the data to be captured. The research also suggests that coding professionals who wish to prepare for the future should acquire more communication, research, and management skills. Further, because there are only a few skills that were found to be significantly different between the two groups, the non-coding health information professionals can prepare to become coding professionals by gaining skills in coding systems and reimbursement software. Moreover, the implication is that the skill set projected for coders applies to all HIM professionals as well.