Janet Stifter

Nurses' Numeracy and Graphical Literacy: Informing Studies of Clinical Decision Support Interfaces.

We present findings of a comparative study of numeracy and graph literacy in a representative group of 60 practicing nurses. This article focuses on a fundamental concern related to the effectiveness of numeric information displayed in various features in the electronic health record during clinical workflow. Our findings suggest the need to consider numeracy and graph literacy when presenting numerical information as well as the potential for tailoring numeric display types to an individuals cognitive strengths.

Use of Simulation to Study Nurses' Acceptance and Nonacceptance of Clinical Decision Support Suggestions.

Our long-term goal was to ensure nurse clinical decision support works as intended before full deployment in clinical practice. As part of a broader effort, this pilot project explored factors influencing acceptance/nonacceptance of eight clinical decision support suggestions displayed in an electronic health record–based nursing plan of care software prototype. A diverse sample of 21 nurses participated in this high-fidelity clinical simulation experience and completed a questionnaire to assess reasons for accepting/not accepting the clinical decision support suggestions. Of 168 total suggestions displayed during the experiment (eight for each of the 21 nurses), 123 (73.2%) were accepted, and 45 (26.8%) were not accepted. The mode number of acceptances by nurses was seven of eight, with only two of 21 nurses accepting all. The main reason for clinical decision support acceptance was the nurse’s belief that the suggestions were good for the patient (100%), with other features providing secondary reinforcement. Reasons for nonacceptance were less clear, with fewer than half of the subjects indicating low confidence in the evidence. This study provides preliminary evidence that high-quality simulation and targeted questionnaires about specific clinical decision support selections offer a cost-effective means for testing before full deployment in clinical practice.

Nurse Continuity and Hospital-Acquired Pressure Ulcers: A Comparative Analysis Using an Electronic Health Record "big Data" Set

BackgroundLittle research demonstrating the association between nurse continuity and patient outcomes exists despite an intuitive belief that continuity makes a difference in care outcomes. ObjectiveThe aim of this study was to examine the association of nurse continuity with the prevention of hospital-acquired pressure ulcers (HAPU). MethodsA secondary use of data from the Hands on Automated Nursing Data System (HANDS) was performed for this comparative study. The HANDS is a nursing plan of care data set containing 42,403 episodes documented by 787 nurses, on nine units, in four hospitals and includes nurse staffing and patient characteristics. The HANDS data set resides in a “big data” relational database consisting of 89 tables and 747 columns of data. Via data mining, we created an analytic data set of 840 care episodes, 210 with and 630 without HAPUs, matched by nursing unit, patient age, and patient characteristics. Logistic regression analysis determined the association of nurse continuity and additional nurse-staffing variables on HAPU occurrence. ResultsPoor nurse continuity (unit mean continuity index = .21–.42 [1.0 = optimal continuity]) was noted on all nine study units. Nutrition, mobility, perfusion, hydration, and skin problems on admission, as well as patient age, were associated with HAPUs (p < .001). Controlling for patient characteristics, nurse continuity, and the interactions between nurse continuity and other nurse-staffing variables were not significantly associated with HAPU development. DiscussionPatient characteristics including nutrition, mobility, and perfusion were associated with HAPUs, but nurse continuity was not. We demonstrated a high level of variation in the degree of continuity between patient episodes in the HANDS data, showing that it offers rich potential for future study of nurse continuity and its effect on patient outcomes.

Death anxiety in hospitalized end-of-life patients as captured from a structured electronic health record: differences by patient and nurse characteristics

The nursing outcomes of hospitalized patients whose plans of care include death anxiety, which is a diagnosis among patients at the end-of-life, are obscure. The authors of the current article applied data mining techniques to nursing plan-of-care data for patients diagnosed with death anxiety, as defined by North American Nursing Diagnosis Association International, from four different hospitals to examine nursing care outcomes and associated factors. Results indicate that <50% of patients met the expected outcome of comfortable death. Gerontology unit patients were more likely to meet the expected outcome than patients from other unit types, although results were not statistically significant. Younger patients (i.e., age <65) had a lower chance of meeting the outcome compared with older patients (i.e., age ≥65) (χ(2)(1) = 9.266, p < 0.004). Longer stays improved the chances of meeting the outcome (χ(2)(2) = 6.47, p < 0.04). Results indicate that death anxiety outcomes are suboptimal and suggest the need to better educate clinicians about diagnosing and treating death anxiety among patients who face the end-of-life transition.

Infomarkers for transition to goals consistent with palliative care in dying patients

OBJECTIVE Electronic health records (EHRs) may contain infomarkers that identify patients near the end of life for whom it would be appropriate to shift care goals to palliative care. Discovery and use of such infomarkers could be used to conduct effectiveness research that ultimately could help to reduce the monumental cost of caring for the dying. The aim of our study was to identify changes in the plans of care that represent infomarkers, which signal a transition of care goals from nonpalliative care ones to those consistent with palliative care. METHOD Using an existing electronic health record database generated during a two-year longitudinal study of nine diverse medical-surgical units from four Midwest hospitals and a known group approach, we evaluated patient care episodes for 901 patients who died (mean age = 74.5 ± 14.6 years). We used ANOVA and Tukeys post-hoc tests to compare patient groups. RESULTS We identified 11 diagnoses, including Death Anxiety and Anticipatory Grieving, whose addition to the care plan, some of which also occurred with removal of nonpalliative care diagnoses, represent infomarkers of transition to palliative care goals. There were four categories of patients, those who had: no infomarkers on plans (n = 507), infomarkers added on the admission plan (n = 194), infomarkers added on a post-admission plan (minor transitions, n = 109), and infomarkers added and nonpalliative care diagnoses removed on a post-admission plan (major transition, n = 91). Age, length of stay, and pain outcomes differed significantly for these four categories of patients. SIGNIFICANCE OF RESULTS EHRs contain pertinent infomarkers that if confirmed in future studies could be used for timely referral to palliative care for improved focus on comfort outcomes and to identify palliative care subjects from data repositories in order to conduct big-data research, comparative effectiveness studies, and health-services research.

A component-based evaluation protocol for clinical decision support interfaces

In this paper we present our experience in designing and applying an evaluation protocol for assessing usability of a clinical decision support (CDS) system. The protocol is based on component-based usability testing, cognitive interviewing, and a rigorous coding scheme cross-referenced to a component library. We applied this protocol to evaluate alternate designs of a CDS interface for a nursing plan of care tool. The protocol allowed us to aggregate and analyze usability data at various granularity levels, supporting both validation of existing components and providing guidance for targeted redesign.

Toward a More Robust and Efficient Usability Testing Method of Clinical Decision Support for Nurses Derived From Nursing Electronic Health Record Data

PURPOSE To develop methods for rapid and simultaneous design, testing, and management of multiple clinical decision support (CDS) features to aid nurse decision-making. METHODS We used quota sampling, think-aloud and cognitive interviews, and deductive and inductive coding of synchronized audio video data and archival libraries. FINDINGS Our methods and organizational tools allowed us to rapidly improve the usability, understandability, and usefulness of CDS in a generalizable sample of practicing nurses. CONCLUSIONS The method outlined allows the rapid integration of nursing terminology based electronic health record data into routine workflow and holds strong potential for improving patient outcomes. IMPLICATIONS FOR NURSING PRACTICE The methods and organizational tools for development of multiple CDS system features can be used to translate knowledge into practice.

Validation of the quality of diagnoses, interventions, and outcomes (Q-DIO) instrument for use in Brazil and the United States

Purpose To assess the reliability and validity of Quality of Diagnoses, Interventions, and Outcomes (Q-DIO) in Brazil and in the United States. Methods The sample comprised 180 records: centers 1 (electronic records and standardized language) and 2 (paper-based records without standardized language in Brazil, and center 3 (electronic records without standardized language) in the United States. Reliability and discriminant construct validity was analyzed. Findings Cronbachs alpha for all 29 Q-DIO items were greater than or equal to 0.70 for all centers; construct validity was significantly different between the three study centers. Conclusions These results indicate that Q-DIO is valid and reliable for assessing the quality of nursing records. Implications for Nursing Practice The Q-DIO may be useful to assess the quality and accuracy of nursing records.PURPOSE To assess the reliability and validity of Quality of Diagnoses, Interventions, and Outcomes (Q-DIO) in Brazil and in the United States. METHODS The sample comprised 180 records: centers 1 (electronic records and standardized language) and 2 (paper-based records without standardized language in Brazil, and center 3 (electronic records without standardized language) in the United States. Reliability and discriminant construct validity was analyzed. FINDINGS Cronbachs alpha for all 29 Q-DIO items were greater than or equal to 0.70 for all centers; construct validity was significantly different between the three study centers. CONCLUSIONS These results indicate that Q-DIO is valid and reliable for assessing the quality of nursing records. IMPLICATIONS FOR NURSING PRACTICE The Q-DIO may be useful to assess the quality and accuracy of nursing records.

Outcomes for End-of-life Patients With Anticipatory Grieving: Insights From Practice With Standardized Nursing Terminologies Within an Interoperable Internet-based Electronic Health Record

Anticipatory grieving, grief associated with an impending loss, is common for patients facing end of life or for their families. There is little research on the outcomes of interventions for anticipatory grieving among hospitalized patients. A descriptive, comparative analysis of an existing valid and reliable data set that was obtained through routine nursing clinical practice using standardized nursing terminologies was completed. We applied data mining techniques on a targeted data set consisting of hospital episodes for end-of-life patients who were given a diagnosis of anticipatory grieving. Less than 50% of the patients given a diagnosis of anticipatory grieving met the expected ratings of monitored nursing outcomes at the time of death or discharge. Specifically, for the spiritual health outcome, only more than 50% of the patients met the expected outcome rating. For the comfortable death outcome, only 45.9% of the patients met the outcome rating. For the comfortable death outcome, patients were significantly more likely not to meet the expected outcome rating if they were also given a diagnosis belonging to the physical comfort class (&khgr;2(1) = 8.99, P < .003). These results demonstrate that expected outcomes are not being met and suggest the need of better education for the clinicians about the diagnosis and treatment of anticipatory grieving.

Predictive Modeling for End-of-Life Pain Outcome Using Electronic Health Records

Electronic health record (EHR) systems are being widely used in the healthcare industry nowadays, mostly for monitoring the progress of the patients. EHR data analysis has become a big data problem as data is growing rapidly. Using a nursing EHR system, we built predictive models for determining what factors influence pain in end-of-life (EOL) patients. Utilizing different modeling techniques, we developed coarse-grained and fine-grained models to predict patient pain outcomes. The coarse-grained models help predict the outcome at the end of each hospitalization, whereas fine-grained models help predict the outcome at the end of each shift, thus providing a trajectory of predicted outcomes over the entire hospitalization. These models can help in determining effective treatments for individuals and groups of patients and support standardization of care where appropriate. Using these models may also lower the cost and increase the quality of end-of-life care. Results from these techniques show significantly accurate predictions.