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Objectives To describe the education, experience, skills, and knowledge required for health informatics jobs in the United States. Methods Health informatics job postings (n = 206) from Indeed.com on April 14, 2020 were analyzed in an empirical analysis, with the abstraction of attributes relating to requirements for average years and types of experience, minimum and desired education, licensure, certification, and informatics skills. Results A large percentage (76.2%) of posts were for clinical informaticians, with 62.1% of posts requiring a minimum of a bachelor's education. Registered nurse (RN) licensure was required for 40.8% of posts, and only 7.3% required formal education in health informatics. The average experience overall was 1.6 years (standard deviation = 2.2), with bachelor's and master's education levels increasing mean experience to 3.5 and 5.8 years, respectively. Electronic health record support, training, and other clinical systems were the most sought-after skills. Conclusion This cross-sectional study revealed the importance of a clinical background as an entree into health informatics positions, with RN licensure and clinical experience as common requirements. The finding that informatics-specific graduate education was rarely required may indicate that there is a lack of alignment between academia and industry, with practical experience preferred over specific curricular components. Clarity and shared understanding of terms across academia and industry are needed for defining and advancing the preparation for and practice of health informatics.

SummaryVisualization can reduce the cognitive load of information, allowing users to easily interpret and assess large amounts of data. The purpose of our study was to examine home health data using visual analysis techniques to discover clinically salient associations between patient characteristics with problem-oriented health outcomes of older adult home health patients during the home health service period.Knowledge, Behavior and Status ratings at discharge as well as change from admission to discharge that was coded using the Omaha System was collected from a dataset on 988 deidentified patient data from 15 home health agencies. SPSS Visualization Designer v1.0 was used to visually analyze patterns between independent and outcome variables using heat maps and histograms. Visualizations suggesting clinical salience were tested for significance using correlation analysis.The mean age of the patients was 80 years, with the majority female (66%). Of the 150 visualizations, 69 potentially meaningful patterns were statistically evaluated through bivariate associations, revealing 21 significant associations. Further, 14 associations between episode length and Charlson co-morbidity index mainly with urinary related diagnoses and problems remained significant after adjustment analyses. Through visual analysis, the adverse association of the longer home health episode length and higher Charlson co-morbidity index with behavior or status outcomes for patients with impaired urinary function was revealed.We have demonstrated the use of visual analysis to discover novel patterns that described high-needs subgroups among the older home health patient population. The effective presentation of these data patterns can allow clinicians to identify areas of patient improvement, and time periods that are most effective for implementing home health interventions to improve patient outcomes.Citation: Radhakrishnan K, Monsen KA, Bae S-H, Zhang W. Visual analytics for pattern discovery in home care: Clinical relevance for quality improvement.

SummaryBackground: There is a critical need to reduce hospitalizations for Medicare patients and electronic health record (EHR) home care data provide new opportunities to evaluate risk of hospitalization for patients.Objectives: The objectives of this study were to 1) develop a measure to predict risk of hospitalization among home care patients, the Hospitalization Risk Score (HRS), and 2) compare it with an existing severity of illness measure, the Charlson Index of Comorbidity (CIC).Methods: A convenience sample of clinical data from 14 home care agencies’ EHRs, representing 1,643 home care patient episodes was used for the study. The development of the HRS was based on review of the literature, and expert panel evaluation to construct the HRS. Descriptive statistics and generalized linear models were used for comparative analysis; areas under curve (AUC) values were compared for receiver operating curves (ROC), and cut points predicting hospitalization were evaluated.Results: The HRS for this sample ranged from 0 to 5.6, with a median of 1.25. The CIC for this sample ranged from 0 to 9 and with a median of 0. Nearly three fourths of the sample was hospitalized at an HRS of 2, and a CIC of 1. AUC values for ROC were 0.63 for HRS and 0.59 for the CIC. The ROC curves were significantly different (t = -7.59, p Conclusions: This preliminary study demonstrates the potential value of the HRS using Omaha System EHR data. There was a statistically significant difference for predicting hospitalization of home care patients with the HRS versus the CIC; however the AUC values for both were low. Continued research is needed to further refine the HRS, determine whether it is more sensitive for particular subgroups of patients, and combine it with additional risk factors in understanding rehospitalization.Citation: Monsen KA, Swanberg HL, Oancea SC, Westra BL. Exploring the value of clinical data standards to predict hospitalization of home care patients. Appl Clin Inf 2012; 3: 419–436http://dx.doi.org/10.4338/ACI-2012-05-RA-0016

SummaryBackground: EHR clinical document synthesis by clinicians may be time-consuming and error-prone due to the complex organization of narratives, excessive redundancy within documents, and, at times, inadvertent proliferation of data inconsistencies. Development of EHR systems that are easily adaptable to the user’s work processes requires research into visualization techniques that can optimize information synthesis at the point of care.Objective: To evaluate the effect of a prototype visualization tool for clinically relevant new information on clinicians’ synthesis of EHR clinical documents and to understand how the tool may support future designs of clinical document user interfaces.Methods: A mixed methods approach to analyze the impact of the visualization tool was used with a sample of eight medical interns as they synthesized EHR clinical documents to accomplish a set of four pre-formed clinical scenarios using a think-aloud protocol.Results: Differences in the missing (unretrieved) patient information (2.3±1.2 [with the visualization tool] vs. 6.8±1.2 [without the visualization tool], p = 0.08) and accurate inferences (1.3±0.3 vs 2.3±0.3, p = 0.09) were not statistically significant but suggest some improvement with the new information visualization tool. Despite the non-significant difference in total times to task completion (43±4 mins vs 36±4 mins, p = 0.35) we observed shorter times for two scenarios with the visualization tool, suggesting that the time-saving benefits may be more evident with certain clinical processes. Other observed effects of the tool include more intuitive navigation between patient details and increased efforts towards methodical synthesis of clinical documents.Conclusion: Our study provides some evidence that new information visualization in clinical notes may positively influence synthesis of patient information from EHR clinical documents. Our findings provide groundwork towards a more effective display of EHR clinical documents using advanced visualization applications.Citation: Farri O, Rahman A, Monsen KA, Zhang R, Pakhomov SV, Pieckiewicz DS, Speedie SM, Melton GB. Impact of a prototype visualization tool for new information in EHR clinical documents. Appl Clin Inf 2012; 3: 404–418http://dx.doi.org/10.4338/ACI-2012-05-RA-0017

Summary Background: Public health nurse (PHN) home visiting programs have been widely employed to improve life course trajectories for high risk mothers. Home visiting programs are often lengthy, during which PHNs simultaneously address multiple problems using diverse interventions over several client encounters. To manage PHN caseloads it is critical to understand the trajectory of client improvement and the optimal duration or services. PHN documentation data enable intervention trajectory research for specific client problems. A new metric called problem stabilization is proposed for evaluating interim improvement during PHN home visiting. Problem stabilization is an intervention pattern for a client problem that is characterized by co-occurring actions (i.e. teaching, guidance, and counseling; treatments and procedures; case management; and/or surveillance) during a client encounter; followed by surveillance actions only for that problem during a subsequent client encounter. The purpose of the study was to investigate problem stabilization during home visiting services for high risk mothers. Methods: A retrospective cohort was created using family home visiting intervention documentation data from a local Midwest public health agency over a six year period (2000–2005). The data set consisted of Omaha System interventions for 720 high risk mothers. Analysis was conducted using descriptive statistics and Kaplan Meier curves. Results: On average 30.1% of the time, client problems stabilized before discharge. Stabilization patterns differed by problem. Time to stabilization was longest for Caretaking/parenting and Ante-partum/postpartum problems, and shortest for Residence and Mental health problems. Conclusions: Problem stabilization, a proposed intermediate outcome of PHN home visiting care, appears to be meaningful in describing client response to PHN intervention. This metric is an example of meaningful use of structured clinical electronic health record data for program evaluation and clinical decision support.

SummaryInterface terminologies used in electronic health records must be re-evaluated and revised to reflect current health care practice and knowledge. To enable future revisions of the Omaha System Intervention Scheme, investigators evaluated formal semantic structure of target terms and concept duplication of problem and target terms. Using linguistic principles and qualitative analysis, five themes were found. A multidimensional formal semantic structure for the intervention target term was proposed. Concept duplication was examined for 16 problem-target matches. Clinical data enabled assessment of the validity of a proposed formal semantic structure and concept duplication. Recommendations are suggested for future development of the Omaha System Intervention Scheme.

Summary Background: The Omaha System is a standardized interface terminology that is used extensively by public health nurses in community settings to document interventions and client outcomes. Researchers using Omaha System data to analyze the effectiveness of interventions have typically calculated p-values to determine whether significant client changes occurred between admission and discharge. However, p-values are highly dependent on sample size, making it difficult to distinguish statistically significant changes from clinically meaningful changes. Effect sizes can help identify practical differences but have not yet been applied to Omaha System data. Methods: We compared p-values and effect sizes (Cohen’s d) for mean differences between admission and discharge for 13 client problems documented in the electronic health records of 1,016 young low-income parents. Client problems were documented anywhere from 6 (Health Care Supervision) to 906 (Caretaking/parenting) times. Results: On a scale from 1 to 5, the mean change needed to yield a large effect size (Cohen’s d 0.80) was approximately 0.60 (range = 0.50 – 1.03) regardless of p-value or sample size (i.e., the number of times a client problem was documented in the electronic health record). Conclusions: Researchers using the Omaha System should report effect sizes to help readers determine which differences are practical and meaningful. Such disclosures will allow for increased recognition of effective interventions.

SummaryMeaningful use is a multidimensional concept that incorporates complex processes; workflow; interoperability; decision support; performance evaluation; and quality improvement. Meaningful use is congruent with the overall vision for information management in New Zealand. Health practitioners interface with patient information at many levels, and are pivotal to meaningful use at the interface between service providers, patients, and the electronic health record. Advancing towards meaningful use depends on implementing a meaningful interface terminology within the electronic health record. The Omaha System is an interface terminology that is integrated within Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT®), and has the capacity to disseminate and capture information at the point of care because its codes are simple defined terms. Two community nursing and allied health providers who are considering using the Omaha System in clinical systems for gathering intervention and outcomes data within the personal EHR include Nurse Maude and the Royal New Zealand Plunket Society. Help4U is investigating using the Omaha System as a way to standardise health terminology for consumer use. The Omaha System is also a good fit with the Midwifery and Maternity Providers Organisation (MMPO) existing clinical information system to describe and capture data about interventions currently recorded as free text. As a country that promotes access to affordable primary care and free hospital care, within an environment constrained by resource limitations, maximizing the use of data is key to demonstrating health outcomes for the population.