Factors Considered Important by Healthcare Professionals for the Management of Using Complementary Therapy in Diabetes: A Text-Mining Analysis.

Text-mining algorithms can identify the most prevalent factors of risk-benefit assessment on the use of complementary and integrative health approaches that are found in healthcare professionals' written notes. The aims of this study were to discover the key factors of decision-making on patients' complementary and integrative health use by healthcare professionals and to build a consensus-derived decision algorithm on the benefit-risk assessment of complementary and integrative health use in diabetes. The retrospective study of an archival dataset used a text-mining method designed to extract and analyze unstructured textual data from healthcare professionals' responses. The techniques of classification, clustering, and extraction were performed with 1398 unstructured clinical notes made by healthcare professionals between 2019 and 2020. The most important factor for decision-making by healthcare professionals about complementary and integrative health use in patients with diabetes was the ingredients of the product. Other important factors were the patient's diabetes control, the undesirable effects from complementary and integrative health, evidence-based complementary and integrative health, medical laboratory data, and the product's affordability. This exploratory text-mining study provides insight into how healthcare professionals decide complementary and integrative health use for patients with diabetes after a risk-benefit assessment from clinical narrative notes.

Development of the Benefit-Risk Assessment of Complementary and Alternative Medicine Use in People With Diabetes: A Delphi-Analytic Hierarchy Process Approach.

This study aimed to develop consensus on a decision-making algorithm for benefit-risk assessment of complementary and alternative medicine use in people with diabetes. Delphi-analytic hierarchy process was used with an anonymous voting scheme, based on a three-round procedure, to achieve consensus regarding the important criteria of decision-making algorithm to assess the benefit-risk ratio of complementary and alternative medicine use in people with diabetes. A total of five criteria were considered, namely, the safety of usage (weightage: 46.6%), diabetes-specific patient data (14.6%), complementary and alternative medicine attributes (14.2%), institutional culture in complementary and alternative medicine use (12.8%), and applicability of complementary and alternative medicine (11.8%). The consistency of this hierarchy structure was computed based on the following indices: λmax = 5.041, consistency index = 0.01; random consistency index =1.781; and consistency ratio = 0.009. All criteria to optimize decision-making in ensuring safe use of complementary and alternative medicine in patients with diabetes should be considered by healthcare professionals.

[The Relationship Between Symptoms and Nursing Diagnoses in Hospitalized Patients With Dengue Fever].

BACKGROUND: Supportive care is a primary method for treating dengue fever. Understanding the symptoms of dengue fever and its related nursing diagnosis is crucial for nurses as references for individual care. This research study was motivated by the few literature reviews available on this topic. PURPOSE: This study was developed to elucidate the symptoms experienced by hospitalized patients with dengue fever and to compare the consistency between symptoms and nursing diagnoses. METHODS: A retrospective descriptive research method was employed. The data were collected from the electronic medical records of patients in the data pools of two regional hospitals in Kaohsiung City. A total of 105 patient records were acquired covering the period 2014-2016. IBM SPSS Statistics v22 was used to examine the descriptive statistics of patient attributes and symptoms of dengue fever using averages and percentages and the inferential statistics of symptoms, hospitalization days, and nursing diagnosis using the Chi-square test and Kappa consistency coefficient. RESULTS: The average age of inpatients was 51.0 ± 27.3 years and the average length of hospital stays was 6.1 ± 3.6 days. The common symptoms were fever and headache. The consistency between nursing diagnosis and symptoms ranged up to 45.4%, including hyperthermia, acute pain, nausea, risk of ineffective gastrointestinal perfusion, and risk of bleeding. Inconsistency of nursing diagnosis was found to be 27.3%, including anxiety, deficient fluid volume, and risk of falls. The rate of undiagnosed symptoms was found to be 27.3%, including diarrhea, risk of infection, and impaired oral mucous membrane. CONCLUSIONS / IMPLICATIONS FOR PRACTICE: The reasons for the inconsistency between symptoms and nursing diagnoses may relate to insufficient nursing knowledge of dengue fever and inadequate nursing diagnosis education resulting in insufficient clinical experience / poor judgment amongst nursing staff. The findings of this study suggest the need for continuity of education to make the use of a dengue-fever-symptom checklist more widespread in patient care.

Development and validation of traditional & complementary medicine (TCM) scales for nurses: Using structural equation modelling (SEM).

OBJECTIVES: This study aimed to develop and validate scales to assess attitudes towards patient' s use of TCM (APUTCM) and to measure a communicative competence in TCM (CCTCM) among nurses. METHODS: The instrument development process was conducted from Sep 2013 to Jul 2014, using the following steps: 1) item development; 2) internal review and refinement; 3) face and content validation; 4) instrument administration to a development sample; and 5) evaluation of validity and reliability. A convenience sample was used to recruit registered and advanced practice nurses who worked in different regions throughout Taiwan. A total of 755 respondents completed the online questionnaire. Statistical analyses were performed using the software of SPSS Version 21.0 and Analysis of Moment Structures (AMOS) version 24.0. RESULTS: The scale-level indexes (S-CVI) of content validity for both scales were over 80%. The reliabilities for the 13-item APUTCM scale and for the five-item CCTCM scale were 0.88 and 0.84, respectively. The model suitability for both scales was assessed, and the findings revealed suitable parameters for all indicators: GFI = 0.954, AGFI = 0.932, CFI = 0.959, RMSEA = 0.62, and chi-square/df = 3.891 for APUTCM; and GFI = 0.992, AGFI = 0.969, CFI = 0.992, RMSEA = 0.63, and chi-square/df = 4.04 for CCTCM. The convergent and divergent validity of scores on both scales provided evidence in the expected direction. CONCLUSION: This scale development study provides preliminary evidence that suggests that the 13-item APUTCM and the five-item CCTCM are reliable and valid scales for assessing attitudes toward patient's TCM use and communicative competence in TCM.

Predicting Factors and Risk Stratification for Return Visits to the Emergency Department Within 72 Hours in Pediatric Patients.

OBJECTIVES: A return visit (RV) to the emergency department (ED) is usually used as a quality indicator for EDs. A thorough comprehension of factors affecting RVs is beneficial to enhancing the quality of emergency care. We performed this study to identify pediatric patients at high risk of RVs using readily available characteristics during an ED visit. METHODS: We retrospectively collected data of pediatric patients visiting 6 branches of an urban hospital during 2007. Potential variables were analyzed using a multivariable logistic regression analysis to determine factors associated with RVs and a classification and regression tree technique to identify high-risk groups. RESULTS: Of the 35,435 visits from which patients were discharged home, 2291 (6.47%) visits incurred an RV within 72 hours. On multivariable analysis, younger age, weekday visits, diagnoses belonging to the category of symptoms, signs, and ill-defined conditions, and being seen by a female physician were associated with a higher probability of RVs. Children younger than 6.5 years who visited on weekdays or between midnight and 8:00 AM on weekends or holidays had the highest probability of returning to the ED within 72 hours. CONCLUSIONS: Our study reexamined several important factors that could affect RVs of pediatric patients to the ED and identified high-risk groups of RVs. Further intervention studies or qualitative research could be targeted on these at-risk groups.

Integrating Fuzzy Multiobjective Programming and System Dynamics to Develop an Approach for Talent Retention Policy Selection: Case on Health-Care Industry.

The demand for medical services has been increasing yearly in aging countries. Medical institutions must hire a large number of staff members to provide efficient and effective health-care services. Because of high workload and pressure, high turnover rates exist among health-care staff members, especially those in nonurban areas, which are characterized by limited resources and a predominance of elderly people. Turnover in health-care institutions is influenced by complex factors, and high turnover rates result in considerable direct and indirect costs for such institutions (Lo and Tseng 2019). Therefore, health-care institutions must adopt appropriate strategies for talent retention. Because institutions cannot determine the most effective talent retention strategy, many of them simply passively adopt a single human resource (HR) policy and make minor adjustments to the selected policy. In the present study, system dynamics modeling was combined with fuzzy multiobjective programming to develop a method for simulating HR planning systems and evaluating the suitability of different HR policies in an institution. We also considered the external insurance policy to be the parameter for the developed multiobjective decision-making model. The simulation results indicated that reducing the turnover rate of new employees in their trial period is the most effective policy for talent retention. The developed procedure is more efficient, effective, and cheaper than the traditional trial-and-error approaches for HR policy selection.

Evaluating Organ Donation Decision in ICU Patients' Families by Analytic Network Process Approach.

The imbalance between supply and demand for organs has been a global crisis, despite the efforts of transplant coordinators from healthcare institutions to promote donor registration. Because the patient's family has legal rights over the patient's remains, they can easily undermine any efforts spent on organ procurement by simply refusing the patient's consent before death in practice. Most related studies seldom mention the decision-making on organ donation from patients' families. The objectives of this study are to find what are the priorities of those factors acting as the pillars of organ donation by patients' families. This study applied the analytic network process (ANP) to the prioritization factors contributing toward the willingness of families to donate organs of intensive care unit patients. The purposive sampling method used structured questionnaires and ANP questionnaires to enroll 180 patients' families from five intensive care units who met the criteria in the regional teaching hospital of southern Taiwan. Through the ANP analysis, it was found that when family members made organ donation decisions, the weights of the four domains are as follows: psychology-47.6%, externality-20.3%, spirituality-19.7%, and physiology-12.3%. The main decision-making factors that influenced the weighting factors were "attitude" (31.5%), "physician's experience" (0.88%), "religion" (19.3%), and "organ selection" (31.9%). These results could assist organ donation teams to take the best strategies for persuading people to agree with organ donation and formulating an individual organ donation plan.

A Fact-Finding Procedure Integrating Machine Learning and AHP Technique to Predict Delayed Diagnosis of Bladder Patients with Hematuria.

Bladder cancer, the ninth most common cancer worldwide, requires fast diagnosis and treatment to prevent disease progression and improve patient survival. However, patients with bladder cancer often experience considerable delays in diagnosis. One reason for such delays is that hematuria, a major symptom of bladder cancer, has a high probability of also being a warning sign for urinary tract diseases. Another reason is that the sensitivity of the body parts affected by bladder cancer deters patients from undergoing cystoscopy and influences patients' "physician shopping" behavior. In this study, the analytic hierarchy process was used to determine critical variables influencing delayed diagnosis; moreover, the variables were used to construct models for predicting delayed diagnosis in patients with hematuria by using several machine learning techniques. Furthermore, the critical variables associated with delayed diagnosis of bladder cancer in patients with hematuria were evaluated using GainRatio technology. The study sample was selected from a population-based database. The model evaluation results indicated that the prediction model established using decision tree algorithms outperformed the other models. The critical risk factors for delayed diagnosis of bladder cancer were as follows: (1) cystoscopy performed 6 months after hematuria diagnosis and (2) physician shopping.

Applying Data Mining Techniques for Predicting Prognosis in Patients with Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a systematic chronic inflammatory disease. The disease mechanism remains unclear and may have resulted from autoimmune problems caused by genetic predisposing and pathogen infection. In clinical practice, selection of the initial treatment is based on the degree of disease activity, and treatment plans will be added gradually according to increased severity of the disease. However, treatment results can be unclear and treatment process uncertain and ambiguous, which can cause healthcare quality to become worse. This study attempts to combine expert opinions to construct various classifiers using a number of data mining techniques to analyze the different prognosis of two patient groups, by predicting whether the inflammatory indicator erythrocyte sedimentation rates of these two groups will be within the normal range with different medication strategies. Clinical data were collected for construction of different classifiers and we evaluate the prediction accuracy rate of each classifier afterwards. The optimum prediction model is selected from these classifiers to predict the prognosis of RA within these treatment strategies and analyze various results. The results show the accuracy rate of the prediction model by Logistic, SVM and DT module were 0.7927, 07829 and 0.9094, respectively. In the RA complications dataset, the accuracy rate of were 0.9393, 0.9290 and 0.9812, respectively. Futhermore, gain ratio was used to further analyze the rules and to discover which branch nodes are the most importance factor. The results of this study are helpful for formulation and development of guidelines for clinical RA treatments, and implementation of a decision support system by using the prediction model can assist medical staff to make correct decisions in the disease's early stage.

Does Medical Students' Personality Traits Influence Their Attitudes toward Medical Errors?

This study examined medical students' perceptions towards medical errors and the policy of the hospital within the internship curriculum, and explored how aspects of personality traits of medical students relate to their attitude toward medical errors. Based on the theory of the Five-Factor-Model (FFM) and related literature review, this study adopted a self-devised structured questionnaire to distribute to 493 medical students in years five to seven in the top three medical schools, representing a 56.7% valid questionnaire response rate. Results showed that agreeableness is more important than other personality traits, and medical students with high agreeableness are good communicators and have a more positive attitude to avoid errors in the future. On the contrary, students with low neuroticism tended to be more relaxed and gentle. If medical educators can recruit new students with high agreeableness, these students will be more likely to effectively improve the quality of medical care and enhance patient safety. This study anticipates that this method could be easily translated to nearly every medical department entry examination, particularly with regards to a consciousness-based education of future physicians.

Predicting warfarin dosage from clinical data: a supervised learning approach.

OBJECTIVE: Safety of anticoagulant administration has been a primary concern of the Joint Commission on Accreditation of Healthcare Organizations. Among all anticoagulants, warfarin has long been listed among the top ten drugs causing adverse drug events. Due to narrow therapeutic range and significant side effects, warfarin dosage determination becomes a challenging task in clinical practice. For superior clinical decision making, this study attempts to build a warfarin dosage prediction model utilizing a number of supervised learning techniques. METHODS AND MATERIALS: The data consists of complete historical records of 587 Taiwan clinical cases who received warfarin treatment as well as warfarin dose adjustment. A number of supervised learning techniques were investigated, including multilayer perceptron, model tree, k nearest neighbors, and support vector regression (SVR). To achieve higher prediction accuracy, we further consider both homogeneous and heterogeneous ensembles (i.e., bagging and voting). For performance evaluation, the initial dose of warfarin prescribed by clinicians is established as the baseline. The mean absolute error (MAE) and standard deviation of errors (σ(E)) are considered as evaluation indicators. RESULTS: The overall evaluation results show that all of the learning based systems are significantly more accurate than the baseline (MAE=0.394, σ(E)=0.558). Among all prediction models, both Bagged Voting (MAE=0.210, σ(E)=0.357) with four classifiers and Bagged SVR (MAE=0.210, σ(E)=0.366) are suggested as the two most effective prediction models due to their lower MAE and σ(E). CONCLUSION: The investigated models can not only facilitate clinicians in dosage decision-making, but also help reduce patient risk from adverse drug events.