Background: The increasing prevalence of multiple chronic conditions has accentuated the importance of coordinating and integrating health care services. Patients with better continuity of care (COC) have a lower utilization rate of emergency department (ED) services, lower hospitalization and better care outcomes. Previous COC studies have focused on the care outcome of patients with a single chronic condition or that of physician-patient relationships; few studies have investigated the care outcome of patients with multiple chronic conditions. Using multi-chronic patients as subjects, this study proposes an integrated continuity of care (ICOC) index to verify the association between COC and care outcomes for two scopes of chronic conditions, at physician and medical facility levels.Methods: This study used a dataset of 280,840 subjects, obtained from the Longitudinal Health Insurance Database (LHID 2005), compiled by the National Health Research Institutes, of the National Health Insurance Bureau of Taiwan. Principal Component Analysis (PCA) was used to integrate the indices of density, dispersion and sequence into ICOC to measure COC outcomes - the utilization rate of ED services and hospitalization. A Generalized Estimating Equations model was used to verify the care outcomes.Results: We discovered that the higher the COC at medical facility level, the lower the utilization rate of ED services and hospitalization for patients; by contrast, the higher the COC at physician level, the higher the utilization rate of ED services (odds ratio > 1; Exp(β) = 2.116) and hospitalization (odds ratio > 1; Exp(β) = 1.688). When only those patients with major chronic conditions with the highest number of medical visits were considered, it was found that the higher the COC at both medical facility and physician levels, the lower the utilization rate of ED services and hospitalization.Conclusions: The study shows that ICOC is more stable than single indices and it can be widely used to measure the care outcomes of different chronic conditions to accumulate empirical evidence. Concentrated care of multi-chronic patients by a single physician often results in unsatisfactory care outcomes. This highlights the need for referral mechanisms and integration of specialties inside or outside medical facilities, in order to optimize patient-centered care.

How to effectively use the finite resources of an intensive care unit (ICU) for neurosurgical patients is a critical decision-making process. Mortality prediction models are effective tools for allocating facilities. This study intended to distinguish the prediction power of the Acute Physiology and Chronic Health Evaluation II (APACHE II), the Simplified Acute Physiology Score II (SAPS II), and the Glasgow Coma Scale (GCS) for neurosurgical patients. According to the definitions of the APACHE II, this study recorded both APACHE II and SAPS II scores of 154 neurosurgical patients in the ICU of a 600-bed general hospital. Linear regression models of GCS (GCS-mr) were constructed. The t test, receiver operating characteristic (ROC) curve and Wilcoxon signed rank test were used as the statistical evaluation methods. There were 50 (32.5%) females and 104 (67.5%) males in this study. Among them, 108 patients survived and 46 patients died. The areas under the ROC curves (AUC) of SAPS II and APACHE II were 0.872 and 0.846, respectively. The AUC of GCS-mr was 0.866, and the R(2) was 0.389. The evaluation powers of SAPS II, GCS-mr and APACHE II were the same (p > 0.05). Patients with GCS 3 (p < 0.01). The predictive powers of SAPS II, APACHE II and GCS-mr were the same. The GCS-mr is more convenient for predicting mortality in neurosurgical patients. Both GCS <or= 5 and GCS-M <or= 3 are good indicators of mortality in these patients.

How to decide the proper time to do laparotomies for acute appendicitis patients is sometimes very difficult, especially in areas with no imaging diagnostic tools. The Alvarado scoring system (ASS) is a convenient and inexpensive decision making tool; however, its accuracy needs to be improved. The decision tree is the most frequently used data mining technology for diagnostic model building. This study used a decision tree to modify the ASS and to prioritize the variables. We collected 532 patients who underwent appendectomy. Patients who had undergone incidental appendectomy were excluded from the study. The decision tree algorithm was constructed with the data mining workbench Clementine version 8.1. It is a top-down algorithm designed to generate a decision tree model with entropy. The algorithm chooses the best decision node with which to separate different classes from empirical data. The Wilcoxon signed rank test, Student t test and chi(2) test were used for statistical analysis. Among the 532 patients recruited into the study, 420 had acute appendicitis and 112 had normal appendix. Women with acute appendicitis were older than their male counterparts (p < 0.001). All patients had right lower quadrant tenderness. The new model was constructed with decision tree technology, and the accuracy of the diagnostic rate was better than that of ASS (p < 0.001). The sensitivity and specificity of the new model were 0.945 and 0.805, respectively. The new model is more convenient and accurate than ASS. Right lower quadrant tenderness is an inclusion criterion for acute appendicitis diagnosis. Migrating pain and neutrophil count > 75% were significant factors for acute appendicitis diagnosis if ASS score < 6. Although the criteria of nausea/vomiting and white blood cell count > 10,000/dL were significantly different between acute appendicitis and normal appendix, there was no significant contribution of entropy change below the "neutrophil count > 75%" nodes in the model. So they were erased from the decision tree model. Further studies need to be conducted to investigate why older women are at higher risk for acute appendicitis.

Physicians have to deal with a broad range of medical problems in clinical practice, thus making the timely acquisition of relevant information is a critical skill for physicians to improve care quality. The current national study investigates how physicians search for medical information and analyses how they use online medical databases. A structured questionnaire survey was conducted, with 457 valid returns collected. Internet-based resources (Web portals, online databases, and electronic journals) were more often accessed by physicians to look for medical information than personal or paper ones. Almost universally, physicians have accessed online databases. MEDLINE was the most frequently accessed database. Furthermore, physicians under 50 years old tended to access online databases more often than their elder colleagues (OR = 5.27, 95% CI = 1.96-14.14 for age <35; OR = 4.68, 95% CI = 2.07-10.60 for ages 35-50). In addition, physicians with faculty position were more often accessing online databases (OR = 3.32; 95% CI = 1.75-6.30). Other factors - including clinical experience, administrative position, gender, academic degree, and professional specialty - carried no significant differences. These data may assist in determining how to promote the use of online evidence-based medical information for clinical services.

Introduction. Length of stay (LOS) in the intensive care unit (ICU) of spontaneous intracerebral hemorrhage (sICH) patients is one of the most important issues. The disease severity, psychosocial factors, and institutional factors will influence the length of ICU stay. This study is used in the Taiwan National Health Insurance Research Database (NHIRD) to define the threshold of a prolonged ICU stay in sICH patients. Methods. This research collected the demographic data of sICH patients in the NHIRD from 2005 to 2009. The threshold of prolonged ICU stay was calculated using change point analysis. Results. There were 1599 sICH patients included. A prolonged ICU stay was defined as being equal to or longer than 10 days. There were 436 prolonged ICU stay cases and 1163 nonprolonged cases. Conclusion. This study showed that the threshold of a prolonged ICU stay is a good indicator of hospital utilization in ICH patients. Different hospitals have their own different care strategies that can be identified with a prolonged ICU stay. This indicator can be improved using quality control methods such as complications prevention and efficiency of ICU bed management. Patients stay in ICUs and in hospitals will be shorter if integrated care systems are established.