Sookyung Hyun

Mobilenurse: hand-held information system for point of nursing care

Healthcare information travels with patients and clinicians and therefore the need for information to be ubiquitously available is key to reliable patient care and reliable medical systems. We have implemented MobileNurse, a prototype point-of-care system using PDA. MobileNurse has four modules each of which performs: (1) patient information management; (2) medical order check; (3) nursing recording; and (4) nursing care plan. MobileNurse provides easy input interface and various outputs for nursing records. The system consists of PDAs and a mobile support system (MSS) which supports clinical data exchange between PDAs and hospital information system. Two synchronization modules have been developed to keep the patient data consistent between PDAs and MSS. Clinical trials were performed with six volunteered nurses. They tried MobileNurse for 1-day caring-simulated patients. According to the survey after the trials, most of volunteers agreed that MobileNurse is more helpful and convenient than other non-mobile care systems to check medical orders and retrieve the results of recent clinical tests at the bedside. Through the involvement, we found out that ease-to-use interface is the most critical successful factor for mobile patient care systems.

Predictive Validity of the Braden Scale for Patients in Intensive Care Units

BACKGROUND Patients in intensive care units are at higher risk for development of pressure ulcers than other patients. In order to prevent pressure ulcers from developing in intensive care patients, risk for development of pressure ulcers must be assessed accurately. OBJECTIVES To evaluate the predictive validity of the Braden scale for assessing risk for development of pressure ulcers in intensive care patients by using 4 years of data from electronic health records. Methods Data from the electronic health records of patients admitted to intensive care units between January 1, 2007, and December 31, 2010, were extracted from the data warehouse of an academic medical center. Predictive validity was measured by using sensitivity, specificity, positive predictive value, and negative predictive value. The receiver operating characteristic curve was generated, and the area under the curve was reported. RESULTS A total of 7790 intensive care patients were included in the analysis. A cutoff score of 16 on the Braden scale had a sensitivity of 0.954, specificity of 0.207, positive predictive value of 0.114, and negative predictive value of 0.977. The area under the curve was 0.672 (95% CI, 0.663-0.683). The optimal cutoff for intensive care patients, determined from the receiver operating characteristic curve, was 13. CONCLUSIONS The Braden scale shows insufficient predictive validity and poor accuracy in discriminating intensive care patients at risk of pressure ulcers developing. The Braden scale may not sufficiently reflect characteristics of intensive care patients. Further research is needed to determine which possibly predictive factors are specific to intensive care units in order to increase the usefulness of the Braden scale for predicting pressure ulcers in intensive care patients.

Exploring the ability of natural language processing to extract data from nursing narratives.

Natural Language Processing (NLP) offers an approach for capturing data from narratives and creating structured reports for further computer processing. We explored the ability of a NLP system, Medical Language Extraction and Encoding (MedLEE), on nursing narratives. MedLEE extracted 490 concepts from narrative text in a sample of 553 oncology nursing process notes. The most frequently monitored and recorded signs and symptoms were related to chemotherapy care, such as adverse reactions, shortness of breath, nausea, pain, and bleeding. In terms of nursing interventions, chemotherapy, blood culture, medication, and blood transfusion were commonly recorded in free text. NLP may provide a feasible approach to extract data related to patient safety/quality measures and nursing outcomes by capturing nursing concepts that are not recorded through structured data entry. For better NLP performance in the domain of nursing, additional nursing terms and abbreviations must be added to MedLEEs lexicon.

Response to a Mobile Health Decision Support System for Screening and Management of Tobacco Use

PURPOSE/OBJECTIVES To describe the predictors of nurse actions in response to a mobile health decision-support system (mHealth DSS) for guideline-based screening and management of tobacco use. DESIGN Observational design focused on an experimental arm of a randomized, controlled trial. SETTING Acute and ambulatory care settings in the New York City metropolitan area. SAMPLE 14,115 patient encounters in which 185 RNs enrolled in advanced practice nurse (APN) training were prompted by an mHealth DSS to screen for tobacco use and select guideline-based treatment recommendations. METHODS Data were entered and stored during nurse documentation in the mHealth DSS and subsequently stored in the study database where they were retrieved for analysis using descriptive statistics and logistic regressions. MAIN RESEARCH VARIABLES Predictor variables included patient gender, patient race or ethnicity, patient payer source, APN specialty, and predominant payer source in clinical site. Dependent variables included the number of patient encounters in which the nurse screened for tobacco use, provided smoking cessation teaching and counseling, or referred patients for smoking cessation for patients who indicated a willingness to quit. FINDINGS Screening was more likely to occur in encounters where patients were female, African American, and received care from a nurse in the adult nurse practitioner specialty or in a clinical site in which the predominant payer source was Medicare, Medicaid, or State Childrens Health Insurance Program. In encounters where the patient payer source was other, nurses were less likely to provide tobacco cessation teaching and counseling. CONCLUSIONS mHealth DSS has the potential to affect nurse provision of guideline-based care. However, patient, nurse, and setting factors influence nurse actions in response to an mHealth DSS for tobacco cessation. IMPLICATIONS FOR NURSING The combination of a reminder to screen and integration of guideline-based recommendations into the mHealth DSS may reduce racial or ethnic disparities to screening, as well as clinician barriers related to time, training, and familiarity with resources.