Correction: Identifying Patients Who Meet Criteria for Genetic Testing of Hereditary Cancers Based on Structured and Unstructured Family Health History Data in the Electronic Health Record: Natural Language Processing Approach.

[This corrects the article DOI: 10.2196/37842.].

Identifying Patients Who Meet Criteria for Genetic Testing of Hereditary Cancers Based on Structured and Unstructured Family Health History Data in the Electronic Health Record: Natural Language Processing Approach.

BACKGROUND: Family health history has been recognized as an essential factor for cancer risk assessment and is an integral part of many cancer screening guidelines, including genetic testing for personalized clinical management strategies. However, manually identifying eligible candidates for genetic testing is labor intensive. OBJECTIVE: The aim of this study was to develop a natural language processing (NLP) pipeline and assess its contribution to identifying patients who meet genetic testing criteria for hereditary cancers based on family health history data in the electronic health record (EHR). We compared an algorithm that uses structured data alone with structured data augmented using NLP. METHODS: Algorithms were developed based on the National Comprehensive Cancer Network (NCCN) guidelines for genetic testing for hereditary breast, ovarian, pancreatic, and colorectal cancers. The NLP-augmented algorithm uses both structured family health history data and the associated unstructured free-text comments. The algorithms were compared with a reference standard of 100 patients with a family health history in the EHR. RESULTS: Regarding identifying the reference standard patients meeting the NCCN criteria, the NLP-augmented algorithm compared with the structured data algorithm yielded a significantly higher recall of 0.95 (95% CI 0.9-0.99) versus 0.29 (95% CI 0.19-0.40) and a precision of 0.99 (95% CI 0.96-1.00) versus 0.81 (95% CI 0.65-0.95). On the whole data set, the NLP-augmented algorithm extracted 33.6% more entities, resulting in 53.8% more patients meeting the NCCN criteria. CONCLUSIONS: Compared with the structured data algorithm, the NLP-augmented algorithm based on both structured and unstructured family health history data in the EHR increased the number of patients identified as meeting the NCCN criteria for genetic testing for hereditary breast or ovarian and colorectal cancers.

Using CDS Hooks to increase SMART on FHIR app utilization: a cluster-randomized trial.

OBJECTIVE: HL7 SMART on FHIR apps have the potential to improve healthcare delivery and EHR usability, but providers must be aware of the apps and use them for these potential benefits to be realized. The HL7 CDS Hooks standard was developed in part for this purpose. The objective of this study was to determine if contextually relevant CDS Hooks prompts can increase utilization of a SMART on FHIR medical reference app (MDCalc for EHR). MATERIALS AND METHODS: We conducted a 7-month, provider-randomized trial with 70 providers in a single emergency department. The intervention was a collection of CDS Hooks prompts suggesting the use of 6 medical calculators in a SMART on FHIR medical reference app. The primary outcome was the percentage of provider-patient interactions in which the app was used to view a recommended calculator. Secondary outcomes were app usage stratified by individual calculators. RESULTS: Intervention group providers viewed a study calculator in the app in 6.0% of interactions compared to 2.6% in the control group (odds ratio = 2.45, 95% CI, 1.2-5.2, P value .02), an increase of 130%. App use was significantly greater for 2 of 6 calculators. DISCUSSION AND CONCLUSION: Contextually relevant CDS Hooks prompts led to a significant increase in SMART on FHIR app utilization. This demonstrates the potential of using CDS Hooks to guide appropriate use of SMART on FHIR apps and was a primary motivation for the development of the standard. Future research may evaluate potential impacts on clinical care decisions and outcomes.

Evaluation in Life Cycle of Information Technology (ELICIT) framework: Supporting the innovation life cycle from business case assessment to summative evaluation.

OBJECTIVE: Our objective was to develop an evaluation framework for electronic health record (EHR)-integrated innovations to support evaluation activities at each of four information technology (IT) life cycle phases: planning, development, implementation, and operation. METHODS: The evaluation framework was developed based on a review of existing evaluation frameworks from health informatics and other domains (human factors engineering, software engineering, and social sciences); expert consensus; and real-world testing in multiple EHR-integrated innovation studies. RESULTS: The resulting Evaluation in Life Cycle of IT (ELICIT) framework covers four IT life cycle phases and three measure levels (society, user, and IT). The ELICIT framework recommends 12 evaluation steps: (1) business case assessment; (2) stakeholder requirements gathering; (3) technical requirements gathering; (4) technical acceptability assessment; (5) user acceptability assessment; (6) social acceptability assessment; (7) social implementation assessment; (8) initial user satisfaction assessment; (9) technical implementation assessment; (10) technical portability assessment; (11) long-term user satisfaction assessment; and (12) social outcomes assessment. DISCUSSION: Effective evaluation requires a shared understanding and collaboration across disciplines throughout the entire IT life cycle. In contrast with previous evaluation frameworks, the ELICIT framework focuses on all phases of the IT life cycle across the society, user, and IT levels. Institutions seeking to establish evaluation programs for EHR-integrated innovations could use our framework to create such shared understanding and justify the need to invest in evaluation. CONCLUSION: As health care undergoes a digital transformation, it will be critical for EHR-integrated innovations to be systematically evaluated. The ELICIT framework can facilitate these evaluations.

Establishing a multidisciplinary initiative for interoperable electronic health record innovations at an academic medical center.

OBJECTIVE: To establish an enterprise initiative for improving health and health care through interoperable electronic health record (EHR) innovations. MATERIALS AND METHODS: We developed a unifying mission and vision, established multidisciplinary governance, and formulated a strategic plan. Key elements of our strategy include establishing a world-class team; creating shared infrastructure to support individual innovations; developing and implementing innovations with high anticipated impact and a clear path to adoption; incorporating best practices such as the use of Fast Healthcare Interoperability Resources (FHIR) and related interoperability standards; and maximizing synergies across research and operations and with partner organizations. RESULTS: University of Utah Health launched the ReImagine EHR initiative in 2016. Supportive infrastructure developed by the initiative include various FHIR-related tooling and a systematic evaluation framework. More than 10 EHR-integrated digital innovations have been implemented to support preventive care, shared decision-making, chronic disease management, and acute clinical care. Initial evaluations of these innovations have demonstrated positive impact on user satisfaction, provider efficiency, and compliance with evidence-based guidelines. Return on investment has included improvements in care; over $35 million in external grant funding; commercial opportunities; and increased ability to adapt to a changing healthcare landscape. DISCUSSION: Key lessons learned include the value of investing in digital innovation initiatives leveraging FHIR; the importance of supportive infrastructure for accelerating innovation; and the critical role of user-centered design, implementation science, and evaluation. CONCLUSION: EHR-integrated digital innovation initiatives can be key assets for enhancing the EHR user experience, improving patient care, and reducing provider burnout.

Troponin Testing and Coronary Syndrome in Geriatric Patients With Nonspecific Complaints: Are We Overtesting?

BACKGROUND: Elderly patients presenting to the emergency department (ED) with nonspecific complaints (NSCs) often undergo troponin testing to assess for atypical acute coronary syndrome (ACS). However, the rate of ACS and utility of troponin testing in this population is unknown. We sought to determine the rate of ACS and diagnostic yield of troponin testing in elderly patients with NSCs. METHODS: We retrospectively identified all patients aged ≥ 65 years triaged in the ED with NSCs from January 1, 2017, to June 30, 2017. NSCs were defined a priori and included complaints such as weakness, dizziness, or fatigue. NSCs were verified in ED provider notes by trained abstractors blind to testing results. Exclusions were focal chief complaint in provider notes, fever, and no troponin ordered. ACS was strictly defined and independently adjudicated by two trained physician researchers blind to the study hypothesis. We calculated the proportion of patients with ACS within 30 days and the test characteristics of troponin to diagnose ACS. RESULTS: Screening identified 1,146 encounters, and 552 were excluded for fever or focal chief complaints in the provider notes. Of the remaining 594 patients, troponin was ordered in 412 (69%), comprising the study cohort. The mean (±SD) age was 78.7 (±8.3) years, with 58% female and 75% admitted. Troponin elevation occurred in 81 patients (20%). ACS occurred in 5 of 412 (1.2%). Troponin was 100% sensitive (95% confidence interval [CI] = 48% to 100%) and 81% specific (95% CI = 77% to 85%) for ACS. Of patients with elevated troponin, 93.8% were false positives (no ACS). All patients with troponin elevation were admitted, but only one underwent angiography and no patients received reperfusion therapy. CONCLUSIONS: While consideration for ACS is prudent in selected elderly patients with NSCs, ACS was rare and no patients received reperfusion therapy. Given the false-positive rate in our study, our results may not support routine troponin testing for ACS in this population.

Evidence of polyclonality in neurofibromatosis type 2-associated multilobulated vestibular schwannomas.

BACKGROUND: Neurofibromatosis type 2 (NF2) is a tumor syndrome that results from mutation of the NF2 tumor suppressor gene. The hallmark of NF2 is the presence of bilateral vestibular schwannoma (VS). Though NF2-associated and sporadic VS share identical histopathologic findings and cytogenetic alterations, NF2-associated VS often appears multilobulated, is less responsive to radiosurgery, and has worse surgical outcomes. Temporal bone autopsy specimens and MRI of the inner ear performed on NF2 patients suggest that multiple discrete tumors may be present within the labyrinth and cerebellopontine angle. METHODS: Treatment-naïve ears in patients enrolled in a prospective NF2 natural history study (NIH#08-N-0044) were included for MRI analysis. T2-weighted and postcontrast T1-weighted MRIs were evaluated for the presence of multiple discrete tumors or a multilobulated mass. Peripheral blood (germline) and regional samples of tumor tissue were procured from consecutive patients enrolled in this study undergoing resection of a multilobulated VS (MVS). Histopathologic evaluation and genetic analysis (single nucleotide polymorphism array analysis, NF2 sequencing) were performed on each specimen. RESULTS: Over half of NF2 ears harbored either an MVS (60/139 ears) or multiple discrete masses (19/139 ears). For 4 successive MVSs, genetic analysis revealed an admixture of cell populations, each with its own somatic NF2 mutation or deletion. CONCLUSIONS: These findings suggest that the majority of NF2-associated VSs are polyclonal, such that the tumor mass represents a collision of multiple, distinct tumor clones. This explains the characteristic lobulated gross appearance of NF2-associated VS, and may also explain the substantially different treatment outcomes compared with sporadic VS.