Yul Ha Min

Daily collection of self-reporting sleep disturbance data via a smartphone app in breast cancer patients receiving chemotherapy: a feasibility study.

Background Improvements in mobile telecommunication technologies have enabled clinicians to collect patient-reported outcome (PRO) data more frequently, but there is as yet limited evidence regarding the frequency with which PRO data can be collected via smartphone applications (apps) in breast cancer patients receiving chemotherapy. Objective The primary objective of this study was to determine the feasibility of an app for sleep disturbance-related data collection from breast cancer patients receiving chemotherapy. A secondary objective was to identify the variables associated with better compliance in order to identify the optimal subgroups to include in future studies of smartphone-based interventions. Methods Between March 2013 and July 2013, patients who planned to receive neoadjuvant chemotherapy for breast cancer at Asan Medical Center who had access to a smartphone app were enrolled just before the start of their chemotherapy and asked to self-report their sleep patterns, anxiety severity, and mood status via a smartphone app on a daily basis during the 90-day study period. Push notifications were sent to participants daily at 9 am and 7 pm. Data regarding the patients’ demographics, interval from enrollment to first self-report, baseline Beck’s Depression Inventory (BDI) score, and health-related quality of life score (as assessed using the EuroQol Five Dimensional [EQ5D-3L] questionnaire) were collected to ascertain the factors associated with compliance with the self-reporting process. Results A total of 30 participants (mean age 45 years, SD 6; range 35-65 years) were analyzed in this study. In total, 2700 daily push notifications were sent to these 30 participants over the 90-day study period via their smartphones, resulting in the collection of 1215 self-reporting sleep-disturbance data items (overall compliance rate=45.0%, 1215/2700). The median value of individual patient-level reporting rates was 41.1% (range 6.7-95.6%). The longitudinal day-level compliance curve fell to 50.0% at day 34 and reached a nadir of 13.3% at day 90. The cumulative longitudinal compliance curve exhibited a steady decrease by about 50% at day 70 and continued to fall to 45% on day 90. Women without any form of employment exhibited the higher compliance rate. There was no association between any of the other patient characteristics (ie, demographics, and BDI and EQ5D-3L scores) and compliance. The mean individual patient-level reporting rate was higher for the subgroup with a 1-day lag time, defined as starting to self-report on the day immediately after enrollment, than for those with a lag of 2 or more days (51.6%, SD 24.0 and 29.6%, SD 25.3, respectively; P=.03). Conclusions The 90-day longitudinal collection of daily self-reporting sleep-disturbance data via a smartphone app was found to be feasible. Further research should focus on how to sustain compliance with this self-reporting for a longer time and select subpopulations with higher rates of compliance for mobile health care.

Development of an Obesity Management Ontology Based on the Nursing Process for the Mobile-Device Domain

Background Lifestyle modification is the most important factor in the management of obesity. It is therefore essential to enhance client participation in voluntary and continuous weight control. Objective The aim of this study was to develop an obesity management ontology for application in the mobile-device domain. We considered the concepts of client participation in behavioral modification for obesity management and focused on minimizing the amount of information exchange between the application and the database when providing tailored interventions. Methods An obesity management ontology was developed in seven phases: (1) defining the scope of obesity management, (2) selecting a foundational ontology, (3) extracting the concepts, (4) assigning relationships between these concepts, (5) evaluating representative layers of ontology content, (6) representing the ontology formally with Protégé, and (7) developing a prototype application for obesity management. Results Behavioral interventions, dietary advice, and physical activity were proposed as obesity management strategies. The nursing process was selected as a foundation of ontology, representing the obesity management process. We extracted 127 concepts, which included assessment data (eg, sex, body mass index, and waist circumference), inferred data to represent nursing diagnoses and evaluations (eg, degree of and reason for obesity, and success or failure of lifestyle modifications), and implementation (eg, education and advice). The relationship linking concepts were “part of”, “instance of”, “derives of”, “derives into”, “has plan”, “followed by”, and “has intention”. The concepts and relationships were formally represented using Protégé. The evaluation score of the obesity management ontology was 4.5 out of 5. An Android-based obesity management application comprising both agent and client parts was developed. Conclusions We have developed an ontology for representing obesity management with the nursing process as a foundation of ontology.

Depression Screening Using Daily Mental-Health Ratings from a Smartphone Application for Breast Cancer Patients

Background Mobile mental-health trackers are mobile phone apps that gather self-reported mental-health ratings from users. They have received great attention from clinicians as tools to screen for depression in individual patients. While several apps that ask simple questions using face emoticons have been developed, there has been no study examining the validity of their screening performance. Objective In this study, we (1) evaluate the potential of a mobile mental-health tracker that uses three daily mental-health ratings (sleep satisfaction, mood, and anxiety) as indicators for depression, (2) discuss three approaches to data processing (ratio, average, and frequency) for generating indicator variables, and (3) examine the impact of adherence on reporting using a mobile mental-health tracker and accuracy in depression screening. Methods We analyzed 5792 sets of daily mental-health ratings collected from 78 breast cancer patients over a 48-week period. Using the Patient Health Questionnaire-9 (PHQ-9) as the measure of true depression status, we conducted a random-effect logistic panel regression and receiver operating characteristic (ROC) analysis to evaluate the screening performance of the mobile mental-health tracker. In addition, we classified patients into two subgroups based on their adherence level (higher adherence and lower adherence) using a k-means clustering algorithm and compared the screening accuracy between the two groups. Results With the ratio approach, the area under the ROC curve (AUC) is 0.8012, indicating that the performance of depression screening using daily mental-health ratings gathered via mobile mental-health trackers is comparable to the results of PHQ-9 tests. Also, the AUC is significantly higher (P=.002) for the higher adherence group (AUC=0.8524) than for the lower adherence group (AUC=0.7234). This result shows that adherence to self-reporting is associated with a higher accuracy of depression screening. Conclusions Our results support the potential of a mobile mental-health tracker as a tool for screening for depression in practice. Also, this study provides clinicians with a guideline for generating indicator variables from daily mental-health ratings. Furthermore, our results provide empirical evidence for the critical role of adherence to self-reporting, which represents crucial information for both doctors and patients.

Implementation of a Next-Generation Electronic Nursing Records System Based on Detailed Clinical Models and Integration of Clinical Practice Guidelines

Objectives The purpose of this paper is to describe the components of a next-generation electronic nursing records system ensuring full semantic interoperability and integrating evidence into the nursing records system. Methods A next-generation electronic nursing records system based on detailed clinical models and clinical practice guidelines was developed at Seoul National University Bundang Hospital in 2013. This system has two components, a terminology server and a nursing documentation system. Results The terminology server manages nursing narratives generated from entity-attribute-value triplets of detailed clinical models using a natural language generation system. The nursing documentation system provides nurses with a set of nursing narratives arranged around the recommendations extracted from clinical practice guidelines. Conclusions An electronic nursing records system based on detailed clinical models and clinical practice guidelines was successfully implemented in a hospital in Korea. The next-generation electronic nursing records system can support nursing practice and nursing documentation, which in turn will improve data quality.

Development of detailed clinical models for nursing assessments and nursing interventions.

Objectives The aim of this study was to develop and validate Detailed Clinical Models (DCMs) for nursing assessments and interventions. Methods First, we identified the nursing assessment and nursing intervention entities. Second, we identified the attributes and the attribute values in order to describe the entities in more detail. The data type and optionality of the attributes were then defined. Third, the entities, attributes and value sets in the DCMs were mapped to the International Classification for Nursing Practice Version 2 concepts. Finally, the DCMs were validated by domain experts and applied to case reports. Results In total 481 DCMs, 429 DCMs for nursing assessments and 52 DCMs for nursing interventions, were developed and validated. The DCMs developed in this study were found to be sufficiently comprehensive in representing the clinical concepts of nursing assessments and interventions. Conclusions The DCMs developed in this study can be used in electronic nursing records. These DCMs can be used to ensure the semantic interoperability of the nursing information documented in electronic nursing records.

Interaction between body mass index and hormone-receptor status as a prognostic factor in lymph-node-positive breast cancer

The aim of this study was to determine the relationship between the body mass index (BMI) at a breast cancer diagnosis and various factors including the hormone-receptor, menopause, and lymph-node status, and identify if there is a specific patient subgroup for which the BMI has an effect on the breast cancer prognosis. We retrospectively analyzed the data of 8,742 patients with non-metastatic invasive breast cancer from the research database of Asan Medical Center. The overall survival (OS) and breast-cancer-specific survival (BCSS) outcomes were compared among BMI groups using the Kaplan-Meier method and Cox proportional-hazards regression models with an interaction term. There was a significant interaction between BMI and hormone-receptor status for the OS (P = 0.029), and BCSS (P = 0.013) in lymph-node-positive breast cancers. Obesity in hormone-receptor-positive breast cancer showed a poorer OS (adjusted hazard ratio [HR] = 1.51, 95% confidence interval [CI] = 0.92 to 2.48) and significantly poorer BCSS (HR = 1.80, 95% CI = 1.08 to 2.99). In contrast, a high BMI in hormone-receptor-negative breast cancer revealed a better OS (HR = 0.44, 95% CI = 0.16 to 1.19) and BCSS (HR = 0.53, 95% CI = 0.19 to 1.44). Being underweight (BMI < 18.50 kg/m2) with hormone-receptor-negative breast cancer was associated with a significantly worse OS (HR = 1.98, 95% CI = 1.00–3.95) and BCSS (HR = 2.24, 95% CI = 1.12–4.47). There was no significant interaction found between the BMI and hormone-receptor status in the lymph-node-negative setting, and BMI did not interact with the menopause status in any subgroup. In conclusion, BMI interacts with the hormone-receptor status in a lymph-node-positive setting, thereby playing a role in the prognosis of breast cancer.

Integration of Evidence into a Detailed Clinical Model-based Electronic Nursing Record System.

Objectives The purpose of this study was to test the feasibility of an electronic nursing record system for perinatal care that is based on detailed clinical models and clinical practice guidelines in perinatal care. Methods This study was carried out in five phases: 1) generating nursing statements using detailed clinical models; 2) identifying the relevant evidence; 3) linking nursing statements with the evidence; 4) developing a prototype electronic nursing record system based on detailed clinical models and clinical practice guidelines; and 5) evaluating the prototype system. Results We first generated 799 nursing statements describing nursing assessments, diagnoses, interventions, and outcomes using entities, attributes, and value sets of detailed clinical models for perinatal care which we developed in a previous study. We then extracted 506 recommendations from nine clinical practice guidelines and created sets of nursing statements to be used for nursing documentation by grouping nursing statements according to these recommendations. Finally, we developed and evaluated a prototype electronic nursing record system that can provide nurses with recommendations for nursing practice and sets of nursing statements based on the recommendations for guiding nursing documentation. Conclusions The prototype system was found to be sufficiently complete, relevant, useful, and applicable in terms of content, and easy to use and useful in terms of system user interface. This study has revealed the feasibility of developing such an ENR system.

Evaluation of the Clinical Data Dictionary (CiDD).

Objectives The purpose of the study was to evaluate content coverage and data quality of the Clinical Data Dictionary (CiDD) developed by the Center for Interoperable EHR (CiEHR). Methods A total of 12,994 terms were collected from 98 clinical forms of a tertiary cancer center hospital with 500 beds. After data cleaning, 9,418 terms were mapped with the data items of the CiDD by the research team, and validated by 30 doctors and nurses at the research hospital. Results Mapping results were classified into five categories: lexically mapped; semantically mapped; mapped to either a broader term or a narrower term; mapped to more than one term and not mapped. In terms of coverage, out of 9,418 terms, 6,750 (71.7%) terms were mapped; 4,319 (45.9%) terms were lexically mapped; 2,431 (25.8%) were semantically mapped; 281 (3.0%) terms were mapped to a broader term; 43 (0.5%) were mapped to a narrower term; and 550 (5.8%) were mapped to more than one term. In terms of data quality, the CiDD has problems such as errors in concept namingand representation, redundancy in synonyms, inadequate synonyms, and ambiguity in meaning. Conclusions Although the CiDD has terms covering 72% of local clinical terms, the CiDD can be improved by cleaning up errors and redundancies, adding textual definitions or use cases of the concept, and arranging the concepts in a hierarchy.

Applicability of the ISO Reference Terminology Model for Nursing to the Detailed Clinical Models of Perinatal Care Nursing Assessments

Objectives The purpose of this study was to examine the applicability of the International Organization for Standardization (ISO) reference terminology model for nursing to describe the terminological value domain content regarding the entities and attributes of the detailed clinical models (DCMs) used for nursing assessments. Methods The first author mapped 52 DCM entities and 45 DCM attributes used for perinatal care nursing assessments to semantic domains and their qualifiers to the ISO model. The mapping results of the entity and attribute concepts were classified into four categories: mapped to a semantic domain qualifier, mapped to a semantic domain, mapped to a broader semantic domain concept, and not mapped. The DCM mapping results were classified into three categories: fully mapped, partially mapped, and not mapped. The second author verified the mapping. Results All of the entities and 53.3% of the attribute concepts of the DCMs were mapped to semantic domains or semantic domain qualifiers of the ISO model, 37.8% of the attributes were mapped to the broader semantic domain concept, and 8.9% of the attributes were not mapped. At the model level, 48.1% of the DCMs were fully mapped to semantic domains or semantic domain qualifiers of the ISO model, and 51.9% of the DCMs were partially mapped. Conclusions The findings of this study demonstrate that the ISO reference terminology model for nursing is applicable in representing the DCM structure for perinatal care nursing assessment. However, more qualifiers of the Judgment semantic domain are required in order to clearly and fully represent all of the entities and attributes of the DCMs used for nursing assessment.