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Nurse leaders working with large volumes of interdisciplinary healthcare data are in need of advanced guidance for conducting analytics to improve population outcomes. This article reports the development of a roadmap to help nursing leaders use data science principles and tools to inform decision-making, thus supporting research and approaches in clinical practice that improve healthcare for all. A consensus-building and iterative process was utilized based on the Cross-Industry Standard Process for Data Mining approach to big data science. Using the model, a set of components are described that combine and achieve a process for data science projects applicable to healthcare issues with the potential for improving population health outcomes. The roadmap was tested using a workshop format. The workshop was presented to two audiences: nurse leaders and informatics/healthcare leaders. Results were positive and included suggestions for how to further refine and communicate the roadmap.

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Social determinants of health (SDOH) are important considerations in diagnosis, prevention, and health outcomes. However, they are often not well documented in the EHR and found primarily in unstructured or semi-structured text. Building upon previous work, we analyzed all flowsheet data in 2013 for information related to the SDOH topic areas of Residence, Living Situation, and Living Conditions. Overall, 91 rows were identified as being related to the topics areas resulting in 604,616 unique observations. Individual rows contained SDOH data often covered multiple concepts especially free-text entries. These data included most often references to the residence, residence details, and with whom the patient lives. Very few contained living condition references. Additionally, there was significant duplication and inconsistency of row labels, as well as variation in value list content for rows collecting the same concepts. Our findings demonstrate significant opportunities to improve and achieve better standardization in documentation around these SDOH.

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Social determinants of health (SDOH) have an important role in diagnosis, prevention, health outcomes, and quality of life. Currently, SDOH information in electronic health record (EHR) systems is often contained in unstructured text. The objective of this study is to examine an important subset of SDOH documentation for Residence, Living Situation and Living Conditions in an enterprise EHR informed by previous model representations. In addition to two publically available clinical note sources, notes created by Social Work, Physical Therapy, and Occupational Therapy, along with free text Social Documentation entries were reviewed. Sentences were classified, annotated, and evaluated once mapped to element entities and attributes. Overall, 2,491 total notes yielded 616, 813, and 30 sentences related to Residence, Living Situation, and Living Conditions. This study demonstrated the need for additional elements in the model representation, more representative values and content culminating in a more comprehensive model representation for these key SDOH.

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Social determinants of health play an important role in diagnosis, prevention, health outcomes, and quality of life. The objective of this study was to examine existing standards, vocabularies, and terminologies for items related to Residence, Living Situation, and Living Conditions and to synthesize them into model representations. Sources were identified through literature and keyword searches, and an examination of commonly used resources. Each source was systematically analyzed by two reviewers, mapped to topic area(s), and further mapped to a model representation. A total of 27 sources were identified and reviewed. Seven of the sources had no items, i.e. concepts, elements, or values, related to the three topic areas while SNOMED-CT had the most items at 436 followed by the US Census at 174. While none of the identified sources encompassed a complete representation for documenting the three topic areas, their synthesis together results overall in more comprehensive representations.

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Immunization information systems (IIS) are population-based and confidential computerized systems maintained by public health agencies containing individual data on immunizations from participating health care providers. IIS hold comprehensive vaccination histories given across providers and over time. An important aspect to IIS is the clinical decision support for immunizations (CDSi), consisting of vaccine forecasting algorithms to determine needed immunizations. The study objective was to analyze the CDSi presentation by IIS in Minnesota (Minnesota Immunization Information Connection [MIIC]) through direct access by IIS interface and by access through electronic health records (EHRs) to outline similarities and differences. The immunization data presented were similar across the three systems examined, but with varying ability to integrate data across MIIC and EHR, which impacts immunization data reconciliation. Study findings will lead to better understanding of immunization data display, clinical decision support, and user functionalities with the ultimate goal of promoting IIS CDSi to improve vaccination rates.

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The debate regarding potential negative health effects of electronic nicotine delivery systems (ENDS), which include electronic cigarettes, has received much recent attention. Currently, it is unknown whether ENDS pose a real health risk to users or those passively exposed to their vapor. With the increased use of these devices, the goal of this study was to examine if and how their use is being documented in the electronic health record (EHR) and the associated implications for clinical research. Analysis of five years of progress notes and tobacco use comments revealed that ENDS use is documented at an increasing rate with variable associated information, most often consisting of the status, purpose, and side effects of ENDS use. These results highlight that improved and consistent EHR discrete data entry for ENDS with associated clinical standards for documentation and representation of potential exposures are needed for enabling effective population health surveillance and research.

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OBJECTIVE: To integrate data elements from multiple sources for informing comprehensive and standardized collection of family health history (FHH). MATERIALS AND METHODS: Three types of sources were analyzed to identify data elements associated with the collection of FHH. First, clinical notes from multiple resources were annotated for FHH information. Second, questions and responses for family members in patient-facing FHH tools were examined. Lastly, elements defined in FHH-related specifications were extracted for several standards development and related organizations. Data elements identified from the notes, tools, and specifications were subsequently combined and compared. RESULTS: In total, 891 notes from three resources, eight tools, and seven specifications associated with four organizations were analyzed. The resulting Integrated FHH Model consisted of 44 data elements for describing source of information, family members, observations, and general statements about family history. Of these elements, 16 were common to all three source types, 17 were common to two, and 11 were unique. Intra-source comparisons also revealed common and unique elements across the different notes, tools, and specifications. DISCUSSION: Through examination of multiple sources, a representative and complementary set of FHH data elements was identified. Further work is needed to create formal representations of the Integrated FHH Model, standardize values associated with each element, and inform context-specific implementations. CONCLUSIONS: There has been increased emphasis on the importance of FHH for supporting personalized medicine, biomedical research, and population health. Multi-source development of an integrated model could contribute to improving the standardized collection and use of FHH information in disparate systems.

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The objective of this study was to examine the utility of using electronic health record (EHR) data for periodic community health surveillance of cardiovascular disease (CVD) risk factors through 2 research questions. First, how many years of EHR data are needed to produce reliable estimates of key population-level CVD health indicators for a community? Second, how comparable are the EHR estimates relative to those from community screenings? The study takes place in the context of the Heart of New Ulm Project, a 10-year population health initiative designed to reduce myocardial infarctions and CVD risk factor burden in a rural community. The community is served by 1 medical center that includes a clinic and hospital. The project screened adult residents of New Ulm for CVD risk factors in 2009. EHR data for 3 years prior to the heart health screenings were extracted for patients from the community. Single- and multiple-year EHR prevalence estimates were compared for individuals ages 40-79 years (N=5918). EHR estimates also were compared to screening estimates (N=3123). Single-year compared with multiyear EHR data prevalence estimates were sufficiently precise for this rural community. EHR and screening prevalence estimates differed significantly-systolic blood pressure (BP) (124.0 vs. 128.9), diastolic BP (73.3 vs. 79.2), total cholesterol (186.0 vs. 201.0), body mass index (30.2 vs. 29.5), and smoking (16.6% vs. 8.2%)-suggesting some selection bias depending on the method used. Despite differences between data sources, EHR data may be a useful source of population health surveillance to inform and evaluate local population health initiatives.

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Administrative data have been used to identify patients with various diseases, yet no prior study has determined the utility of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)-based codes to identify CLI patients. CLI cases (n=126), adjudicated by a vascular specialist, were carefully defined and enrolled in a hospital registry. Controls were frequency matched to cases on age, sex and admission date in a 2:1 ratio. ICD-9-CM codes for all patients were extracted. Algorithms were developed using frequency distributions of these codes, risk factors and procedures prevalent in CLI. The sensitivity for each algorithm was calculated and applied within the hospital system to identify CLI patients not included in the registry. Sensitivity ranged from 0.29 to 0.92. An algorithm based on diagnosis and procedure codes exhibited the best overall performance (sensitivity of 0.92). Each algorithm had differing CLI identification characteristics based on patient location. Administrative data can be used to identify CLI patients within a health system. The algorithms, developed from these data, can serve as a tool to facilitate clinical care, research, quality improvement, and population surveillance.

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Recent initiatives have emphasized the potential role of Electronic Health Record (EHR) systems for improving tobacco use assessment and cessation. In support of these efforts, the goal of the present study was to examine tobacco use documentation in the EHR with an emphasis on free-text. Three coding schemes were developed and applied to analyze 525 tobacco use entries, including structured fields and a free-text comment field, from the social history module of an EHR system to characterize: (1) potential reasons for using free-text, (2) contents within the free-text, and (3) data quality issues. Free-text was most commonly used due to limitations for describing tobacco use amount (23.2%), frequency (26.9%), and start or quit dates (28.2%) as well as secondhand smoke exposure (17.9%) using a variety of words and phrases. The collective results provide insights for informing system enhancements, user training, natural language processing, and standards for tobacco use documentation.

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Despite increased functionality for obtaining family history in a structured format within electronic health record systems, clinical notes often still contain this information. We developed and evaluated an Unstructured Information Management Application (UIMA)-based natural language processing (NLP) module for automated extraction of family history information with functionality for identifying statements, observations (e.g., disease or procedure), relative or side of family with attributes (i.e., vital status, age of diagnosis, certainty, and negation), and predication ("indicator phrases"), the latter of which was used to establish relationships between observations and family member. The family history NLP system demonstrated F-scores of 66.9, 92.4, 82.9, 57.3, 97.7, and 61.9 for detection of family history statements, family member identification, observation identification, negation identification, vital status, and overall extraction of the predications between family members and observations, respectively. While the system performed well for detection of family history statements and predication constituents, further work is needed to improve extraction of certainty and temporal modifications.

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While studies have been conducted to assess nurse perception of electronic health records, once electronic health record systems are up and running, there is little to guide the use of features within the electronic health record for nursing practice. Alerts are a promising tool for implementing best practice for patient care in inpatient settings. Yet the use of alerts for inpatient nursing is understudied. This study examined nurse attitudes and reactions to alerts in the inpatient setting. Focus groups were conducted at three hospitals with 50 nurses. Nurses were asked about five different alert features. For each alert, participants were asked about their feelings and reactions to the alert, how alerts help or hinder work, and suggestions for improvements. Findings include clear preferences for alert types and content. Nurses preferred a dashboard style alert with functions included to accomplish tasks directly in the alert. While nurses reported positive reactions to certain alert pages, they also reported low use of those features and occasional distrust of the data included in alerts. Findings provide guidance for future use of alerts and design of new alerts. Findings also identify the important challenge of designing and implementing alerts for integration with nursing workflow.

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