Academic Medical Center In Taiwan Research Articles

The incidence and risk factors of proximal lower extremity deep vein thrombosis without pharmacologic prophylaxis in critically ill surgical Taiwanese patients: A prospective study.

Venous thromboembolism (VTE) in critically ill patients has been well-studied in Western countries. Many studies have developed risk assessments and established pharmacological protocols to prevent deep venous thrombosis (DVT). However, the DVT rate and need for pharmacologic VTE prophylaxis in critically ill Taiwanese patients are limited. This study aimed to prospectively determine the DVT incidence, risk factors, and outcomes in critically ill Taiwanese patients who do not receive pharmacologic VTE prophylaxis. We conducted a prospective study in a surgical intensive care unit (SICU) of a tertiary academic medical center in Taiwan. Adult patients admitted to SICU from March 2021 to June 2022 received proximal lower extremities DVT surveillance with venous duplex ultrasound. No patient received pharmacologic VTE prophylaxis. The outcomes were the incidence and risk factors of DVT. Among 501 enrolled SICU patients, 21 patients (4.2%) were diagnosed with proximal lower extremities DVT. In a multivariate regression analysis, hypoalbuminemia (odd ratio (OR) = 6.061, 95% confidence interval (CI): 1.067-34.421), femoral central venous catheter (OR = 4.515, 95% CI: 1.547-13.174), ICU stays more than 10 days (OR = 4.017, 95% CI: 1.270-12.707), and swollen leg (OR = 3.427, 95% CI: 1.075-10.930) were independent risk factors for DVT. In addition, patients with proximal lower extremities DVT have more extended ventilator days (p = 0.045) and ICU stays (p = 0.044). Our findings indicate critically ill Taiwanese patients have a higher incidence of DVT than results from prior retrospective studies in the Asian population. Physicians who care for this population should consider the specific risk factors for DVT and prescribe pharmacologic prophylaxis in high-risk groups.

Does Hospitalist Care Enhance Palliative Care and Reduce Aggressive Treatments for Terminally Ill Patients? A Propensity Score-Matched Study.

Information on the use of palliative care and aggressive treatments for terminally ill patients who receive care from hospitalists is limited. This three-year, retrospective, case-control study was conducted at an academic medical center in Taiwan. Among 7037 patients who died in the hospital, 41.7% had a primary diagnosis of cancer. A total of 815 deceased patients who received hospitalist care before death were compared with 3260 patients who received non-hospitalist care after matching for age, gender, catastrophic illness, and Charlson comorbidity score. Regression models with generalized estimating equations were performed. Patients who received hospitalist care before death, compared to those who did not, had a higher probability of palliative care consultation (odds ratio (OR) = 3.41, 95% confidence interval (CI): 2.63-4.41), and a lower probability to undergo invasive mechanical ventilation (OR = 0.13, 95% CI: 0.10-0.17), tracheostomy (OR = 0.14, 95% CI: 0.06-0.31), hemodialysis (OR = 0.70, 95% CI: 0.55-0.89), surgery (OR = 0.25, 95% CI: 0.19-0.31), and intensive care unit admission (OR = 0.11, 95% CI: 0.08-0.14). Hospitalist care was associated with reductions in length of stay (coefficient (B) = -0.54, 95% CI: -0.62--0.46) and daily medical costs. Hospitalist care is associated with an improved palliative consultation rate and reduced life-sustaining treatments before death.

The LACE index and risk factors of 14-day versus 30-day readmissions in children.

Risk factors for readmissions in children differ from those for adults, yet little is known about whether the LACE index (Length of stay, Acute admission, Charlson comorbidity index, Emergency department visits in the previous 6 months) developed for adults retains its validity when applied to the prediction of readmissions in children or within shorter intervals of time after discharge. In this retrospective cohort analysis of 4 256 patients aged≤18 hospitalized at one academic medical center in Taiwan, 2019, we first evaluated the performance of a LACE index model and the three other multivariate logistic regression models in their predictions of hospital readmissions in children using the same time intervals of 30 days. We then used multinomial logistic regression to analyze the characteristics and risk factors for readmissions that occur in the first 14 days with those that occur between 15 to 30 days after discharge. The optimal cutoff of LACE index score for children in the current study was 7, which is less than the optimal score of 10 in the original derivation for adults. The predictive model with the least discriminatory power was based on LACE score alone, whereas our model that included only two variables (length of stay and the number of admissions in the past one year) was found to outperform LACE index. Multinomial logistic regression results revealed that children who had supplemental private health insurance had lower readmission rates than those insured by National Health Insurance program only, suggesting a disparity by insurance status. Some risk factors of readmission within 14 days such as acute admissions and supplemental private health insurance were not found to be relevant for longer-term predictions, while age, which was a valid predictor of readmission within 15 to 30 days, did not affect the prediction of shorter-term readmissions. Prior healthcare utilizations and a higher illness burden were found to be greater contributors to readmissions beyond 14 days. Our simpler model outperformed the more complex LACE tool in identifying children at risk of readmission. Shorter-term readmissions can be attributed to different causes than 30-day readmissions, suggesting a need for different screening tools, interventions, and care support. There remains some socioeconomic disparity associated with readmission rates in the current healthcare system.

A machine learning algorithm for predicting prolonged postoperative opioid prescription after lumbar disc herniation surgery. An external validation study using 1,316 patients from a Taiwanese cohort

Preoperative prediction of prolonged postoperative opioid prescription helps identify patients for increased surveillance after surgery. The SORG machine learning model has been developed and successfully tested using 5,413 patients from the United States (US) to predict the risk of prolonged opioid prescription after surgery for lumbar disc herniation. However, external validation is an often-overlooked element in the process of incorporating prediction models in current clinical practice. This cannot be stressed enough in prediction models where medicolegal and cultural differences may play a major role. The authors aimed to investigate the generalizability of the US citizens prediction model SORG to a Taiwanese patient cohort. Retrospective study at a large academic medical center in Taiwan. Of 1,316 patients who were 20 years or older undergoing initial operative management for lumbar disc herniation between 2010 and 2018. The primary outcome of interest was prolonged opioid prescription defined as continuing opioid prescription to at least 90 to 180 days after the first surgery for lumbar disc herniation at our institution. Baseline characteristics were compared between the external validation cohort and the original developmental cohorts. Discrimination (area under the receiver operating characteristic curve and the area under the precision-recall curve), calibration, overall performance (Brier score), and decision curve analysis were used to assess the performance of the SORG ML algorithm in the validation cohort. This study had no funding source or conflict of interests. Overall, 1,316 patients were identified with sustained postoperative opioid prescription in 41 (3.1%) patients. The validation cohort differed from the development cohort on several variables including 93% of Taiwanese patients receiving NSAIDS preoperatively compared with 22% of US citizens patients, while 30% of Taiwanese patients received opioids versus 25% in the US. Despite these differences, the SORG prediction model retained good discrimination (area under the receiver operating characteristic curve of 0.76 and the area under the precision-recall curve of 0.33) and good overall performance (Brier score of 0.028 compared with null model Brier score of 0.030) while somewhat overestimating the chance of prolonged opioid use (calibration slope of 1.07 and calibration intercept of -0.87). Decision-curve analysis showed the SORG model was suitable for clinical use. Despite differences at baseline and a very strict opioid policy, the SORG algorithm for prolonged opioid use after surgery for lumbar disc herniation has good discriminative abilities and good overall performance in a Han Chinese patient group in Taiwan. This freely available digital application can be used to identify high-risk patients and tailor prevention policies for these patients that may mitigate the long-term adverse consequence of opioid dependence: https://sorg-apps.shinyapps.io/lumbardiscopioid/.

The role of patient records in research: A bibliometric analysis of publications from an academic medical center in Taiwan.

This study aimed to assess the use of medical record items in clinical research in one large academic medical center in Taiwan. A descriptive survey design was adopted to collect the data. Articles published in 2018 by Taipei Veterans General Hospital (TVGH) staff as the first author were obtained. The types of specialties and types of research were analyzed. To understand the conditions for the use of medical records, the retrospective research using hospital's medical records were analyzed. Each article was read in entirety to realize the use and number of patients and the medical record items. Among the 362 articles first-authored by TVGH staff in 2018, 219 (60.4%) were classified as clinical studies, 60 (16.6%) as basic studies, 53 (14.6%) as database studies, and 30 (8.2%) as other categories. About 50% of the retrospective research using TVGH medical records had a case number <100 (67 cases, 49.6%) with an average number of 41 cases and 13 studies (9.6%) had a case number >1000. Analysis of the number of medical record items used in 135 retrospective research studies based on TVGH medical records showed that 118 (87.4%) used basic patient information. In addition to basic information, notes written by professionals were used most frequently (73 cases, 54.0%), whereas medication information was used in 50 cases (37.0%); laboratory test data were used in 49 cases (36.2%); and body measurements was used in 27 cases (20%). More than one-third of publications utilized medical records, but the patient numbers and record items in use were relatively limited. In the era of digitalization and big data analytics, the potential of medical records in research deserves attention. Investment in establishing a more accessible database of medical records to access nonstructural, descriptive medical records could be considered.

A novel method to retrieve alerts from a homegrown Computerized Physician Order Entry (CPOE) system of an academic medical center: Comprehensive alert characteristic analysis.

BackgroundThe collection and analysis of alert logs are necessary for hospital administrators to understand the types and distribution of alert categories within the organization and reduce alert fatigue. However, this is not readily available in most homegrown Computerized Physician Order Entry (CPOE) systems.ObjectiveTo present a novel method that can collect alert information from a homegrown CPOE system (at an academic medical center in Taiwan) and conduct a comprehensive analysis of the number of alerts triggered and alert characteristics.MethodsAn alert log collector was developed using the Golang programming language and was implemented to collect all triggered interruptive alerts from a homegrown CPOE system of a 726-bed academic medical center from November 2017 to June 2018. Two physicians categorized the alerts from the log collector as either clinical or non-clinical (administrative).ResultsOverall, 1,625,341 interruptive alerts were collected and classified into 1,474 different categories based on message content. The sum of the top 20, 50, and 100 categories of most frequently triggered alerts accounted for approximately 80, 90 and 97 percent of the total triggered alerts, respectively. Among alerts from the 100 most frequently triggered categories, 1,266,818 (80.2%) were administrative and 312,593 (19.8%) were clinical alerts.ConclusionWe have successfully developed an alert log collector that can serve as an extended function to retrieve alerts from a homegrown CPOE system. The insight generated from the present study could also potentially bring value to hospital system designers and hospital administrators when redesigning their CPOE system.

Deployment of a computerized ward visitor registration system in coronavirus disease 2019 epidemic: Experiences of a large academic medical center in Taiwan.

Visitors to hospitalized patients during an epidemic might themselves be carriers and are therefore likely to spread the disease in wards. Although measures were taken to restrict hospital visits, traditional paper-based registration is insufficient to screen and monitor the numbers of visitors to a large hospital. Throughout March 2020, during the coronavirus disease 2019 crisis, a computer system was deployed in the 2800-bed Taipei Veterans General Hospital (Taipei, Taiwan) to register, screen, and monitor inpatient visitors. This system comprised three parts: online registration form, entrance check-in interface, and registration database. The early utilization of this newly deployed system was then analyzed. A total of 22,336 visits were recorded between March 11, 2020, and March 31, 2020, with 1064 a day on average. Out of these visits, 18.1% (n = 4049) had made online reservations within 48 hours. On the other hand, of all 4941 online reservations, 18.1% (n = 892) were no-shows. In the last 12 days of the study period, eight prospective visitors were identified as ineligible by the computer system, and so their visits were denied. Using a computer system, the hospital was able to enforce restrictions on hospital visits. Although the online registration system had not been fully used yet in the early phase of adoption, its superiority from the standpoint of disease control should enable hospital managers to consider abolishing on-site visitor registration.

Factors of missed appointments at an academic medical center in Taiwan.

Missed appointments mean appointments neither attended nor canceled by patients. Missed appointments belong to one of the important subjects of hospital management because they would incur the inactivity of medical professionals and devices, occupy the health resources for other patients, and thus impair the quality of healthcare services. The aim of this study was to explore the factors of missed appointments at the outpatient department of an academic medical center in Taiwan. This was a cross-sectional study based on registration records of an academic medical center in Northern Taiwan in 2015. Fifteen variables of patients, appointments, and weathers were taken into analysis. Logistic regression was used to calculate the adjusted odds ratio of each variable. For nonfirst visits, we further built a logistic regression model with the five most influential variables and the personal attendance pattern of the previous three appointments. Of 2 132 577 eligible appointments in 2015, the overall no-show rate was 16.9%. The influential factors included the following: (1) patient characteristics: younger than 40 years, ≤6 visits, and a no-show rate between 50% and 75% in the previous year; (2) appointment characteristics: Saturdays, evenings, on the last third of the waiting list, only one appointment on the same day, online appointments, appointment-to-visit intervals (wait time) in 7 to 14 days, appointments to obstetrics/gynecology or pediatrics, first-time appointments, and the interval between the appointed visit and the previous visit in <7 days; and (3) weather characteristics: warm weathers and heavy rains. For nonfirst appointments, the influences in decreasing order were heavy rain, shorter interval between the appointed visit and the previous visit to the same department, higher no-show rate in the previous year, total absence in the personal attendance pattern of the previous three appointments, longer wait time, and children. The factors of missed appointments were multifaceted. Further measures could be undertaken accordingly to enhance healthcare efficiency.

Comparison of complaints to the intensive care units and those to the general wards: an analysis using the Healthcare Complaint Analysis Tool in an academic medical center in Taiwan

BackgroundThe management of complaints in the setting of intensive care may provide opportunities to understand patient and family experiences and needs. However, there are limited reports on the structured application of complaint analysis tools and comparisons between healthcare complaints in the critical care setting and other settings.MethodsFrom the complaint management database of a university-affiliated medical center in Taiwan, we retrospectively identified the records of healthcare complaints to the intensive care units (ICUs) from 2008 to 2016. Complaints to the general wards in the same period were randomly selected from the database with twice the number of that of the ICU complaints. We coded, typed, and compared the complaints from the two settings according to the Healthcare Complaint Analysis Tool.ResultsWe identified 343 complaints to the ICUs and randomly selected 686 complaints to the general wards during the 9-year study period. Most (94.7%) of the complaints to the ICUs came from the family members, whereas more complaints to the general wards came from the patients (44.2%). A total of 1529 problems (441 from ICU and 818 from general wards) were identified. Compared with the general ward complaints, in the ICU there were more complaints with multiple problems (25.1% vs. 16.9%, p = 0.002), complaints were referred more frequently to the nurses (28.1% vs. 17.5%, p < 0.001), and they focused more commonly on the care on the ICU/ward (60.5% vs. 54.2%, p = 0.029). The proportions of the three domains (clinical, management, and relationship) of complaints were similar between the ICU and general ward complaints (p = 0.121). However, in the management domain, the problems from ICU complaints focused more on the environment than on the institutional processes (90.9% vs. 74.5%, p < 0.001), whereas in the relationship domain, the problems focused more on communication (17.9% vs. 8.0%) and less on listening (34.6% vs. 46.5%) (p = 0.002) than the general ward complaints.ConclusionsA structured typing and systematic analysis of the healthcare complaints to the ICUs may provide valuable insights into the improvement of care quality, especially to the perceptions of the ICU environment and communications of the patients and their families.

A Decision for Predicting Successful Extubation of Patients in Intensive Care Unit.

Approximately 40% of patients admitted to the medical intensive care unit (ICU) require mechanical ventilation. An accurate prediction of successful extubation in patients is a key clinical problem in ICU due to the fact that the successful extubation is highly associated with prolonged ICU stay. The prolonged ICU stay is also associated with increasing cost and mortality rate in healthcare system. This study is retrospective in the aspect of ICU. Hence, a total of 41 patients were selected from the largest academic medical center in Taiwan. Our experimental results show that predicting successful rate of 87.8% is obtained from the proposed predicting function. Based on several types of statistics analysis, including logistic regression analysis, discriminant analysis, and bootstrap method, three major successful extubation predictors, namely, rapid shallow breathing index, respiratory rate, and minute ventilation, are revealed. The prediction of successful extubation function is proposed for patients, ICU, physicians, and hospital for reference.

Reduction in Hospital-Wide Clinical Laboratory Specimen Identification Errors following Process Interventions: A 10-Year Retrospective Observational Study.

BackgroundAccurate patient identification and specimen labeling at the time of collection are crucial steps in the prevention of medical errors, thereby improving patient safety.MethodsAll patient specimen identification errors that occurred in the outpatient department (OPD), emergency department (ED), and inpatient department (IPD) of a 3,800-bed academic medical center in Taiwan were documented and analyzed retrospectively from 2005 to 2014. To reduce such errors, the following series of strategies were implemented: a restrictive specimen acceptance policy for the ED and IPD in 2006; a computer-assisted barcode positive patient identification system for the ED and IPD in 2007 and 2010, and automated sample labeling combined with electronic identification systems introduced to the OPD in 2009.ResultsOf the 2000345 specimens collected in 2005, 1023 (0.0511%) were identified as having patient identification errors, compared with 58 errors (0.0015%) among 3761238 specimens collected in 2014, after serial interventions; this represents a 97% relative reduction. The total number (rate) of institutional identification errors contributed from the ED, IPD, and OPD over a 10-year period were 423 (0.1058%), 556 (0.0587%), and 44 (0.0067%) errors before the interventions, and 3 (0.0007%), 52 (0.0045%) and 3 (0.0001%) after interventions, representing relative 99%, 92% and 98% reductions, respectively.ConclusionsAccurate patient identification is a challenge of patient safety in different health settings. The data collected in our study indicate that a restrictive specimen acceptance policy, computer-generated positive identification systems, and interdisciplinary cooperation can significantly reduce patient identification errors.

Elevated neutrophil to lymphocyte ratio predicts mortality in medical inpatients with multiple chronic conditions.

Neutrophil to lymphocyte ratio (NLR) is an easy measurable laboratory marker used to evaluate systemic inflammation. Elevated NLR is associated with poor survival and increased morbidity in cancer and cardiovascular disease. However, the usefulness of NLR to predict morbidity and mortality in a hospital setting for patients with multiple chronic conditions has not been previously examined. In this study, we investigate the association between NLR and mortality in multimorbid medical inpatients. Two hundred thirty medical in-patients with chronic conditions were selected from a single academic medical center in Taiwan. Retrospective NLRs were calculated from routine full blood counts previously obtained during the initial hospital admission and at the time of discharge. Self-rated health (using a single-item question), medical disorders, depressive symptoms, and medical service utilization over a 1-year period were included in the analyses. Mortality outcomes were ascertained by reviewing electronic medical records and follow-up. The mortality rate at 2-year follow-up was 23%. Depression (odds ratio [OR] 1.9 [95% CI 1.0–3.7]), poor self-rated health (OR 2.1 [95% CI 1.1–3.9]), being hospitalized 2 or more times in the previous year (OR 2.3 [95% CI 1.2–4.6]), metastatic cancer (OR 4.7 [95% CI 2.3–9.7]), and chronic liver disease (OR 4.3 [95% CI 1.5–12.1]) were associated with 2-year mortality. The median (interquartile range) NLR at admission and discharge were 4.47 (2.4–8.7) and 3.65 (2.1–6.5), respectively. Two-year mortality rates were higher in patients with an elevated NLR at admission (NLR <3 = 15.5%, NLR >3 = 27.6%) and discharge (NLR < 3 = 14.7%, NLR >3 = 29.1%). Multivariate logistic regression demonstrated that an elevated NLR >3.0 at admission (OR 2.3 [95% CI 1.0–5.2]) and discharge (OR 2.3 [95% CI 1.1–5.0]) were associated with mortality independent of baseline age, sex, education, metastatic cancer, liver disease, depression, and previous hospitalization. Increased NLR is associated with mortality among medical inpatients with multiple chronic conditions. NLR may provide added value to predict both risk of mortality for the inpatients with chronic conditions, in addition to allowing predictions of likely hospital service needs such as re-admissions that are associated with long-term mortality.

Poor Self-Rated Health Influences Hospital Service Use in Hospitalized Inpatients With Chronic Conditions in Taiwan.

Our aim was to investigate the association between self-rated health (SRH) and use of hospital services (ie, medical outpatient department, emergency department, and general ward. admissions). Cross-sectional study data were collected from 230 consecutive patients admitted to medical departments of a 2000-bed academic medical center in Taiwan using standardized operating procedures for data collection of SRH (ie, a single-item question inquiring overall perceived health status), medical disorders, depressive symptoms, and combined service utilization over a 1-year period (ie, number of visits to outpatient department, number of visits to emergency department, and number of hospitalizations). Electronic medical records were retrieved, with self-reported external medical visits added to in-hospital frequencies of service use to provide better estimation of health service utilization. Fifty-two percent of study patients rated their health as poor or very poor. Poor SRH was associated with more visits to medical outpatient department, emergency department, and hospital admission. Multivariate logistic regression demonstrated an independent association between poor SRH and services utilization after adjustment for age, gender, hypertension, diabetes, metastatic cancer, number of chronic illness, life-threatening event, life-time suicidal ideation, and depression. SRH may be a useful research tool to model medical service use for inpatients with chronic conditions.

Does the Elderly’s Number of Prescribed Medications across Months Matter? National Cohort versus Single-Center Cohort

ObjectivesTo investigate the monthly number of prescribed medication (NPM) patterns among different elderly populations and the factors associated with monthly NPM changes. MethodsThis retrospective cohort study was conducted using the databases obtained from National Health Insurance Research Databases and a 2000-bed academic medical center in Taiwan (i.e., single-center cohort). We compared the monthly NPMs, demographic characteristics, disease states, and health care contacts among the National Health Insurance elderly cohorts in 2006 and 2007, and for those elderly in the national and single-center cohorts who had outpatient visits from November to October in 2006 to 2007 and 2007 to 2008, respectively. Generalized estimating equation analyses of repeated measures were performed for monthly NPMs. ResultsThe average monthly NPMs among the National Health Insurance elderly cohort was 2.33 in 2006 and 4.39 in 2007, respectively. After controlling for other factors, the increment in the proportion of monthly NPMs among the older elderly patients, in certain months and for those patients with hypertension and dyslipidemia, was statistically significant among the single-center cohort but was not observed in the national elderly cohort. The proportional changes decreased significantly among patients who made visits to emergency rooms and who were hospitalized during a 1-year period. ConclusionsThere was an incremental trend of monthly NPMs among the national cohort from 2006 to 2007. Although acute exacerbations and hospitalization might be the protecting factors of increasing monthly NPMs, more attention should be paid toward high-utilization patients with specific diseases during certain months for different elderly cohorts.

Physicians Failed to Write Flawless Prescriptions When Computerized Physician Order Entry System Crashed

PurposeClinical care has become increasingly dependent on computerized physician order entry (CPOE) systems. No study has reported the adverse effect of CPOE on physicians’ ability to handwrite prescriptions. This study took advantage of an extensive crash of the CPOE system at a large hospital to assess the completeness, legibility, and accuracy of physicians’ handwritten prescriptions. MethodsThe CPOE system had operated at the outpatient department of an academic medical center in Taiwan since 1993. During an unintentional shutdown that lasted 3.5 hours in 2010, physicians were forced to write prescriptions manually. These handwritten prescriptions, together with clinical medical records, were later audited by clinical pharmacists with respect to 16 fields of the patient’s, prescriber’s, and drug data. FindingsA total of 1418 prescriptions with 3805 drug items were handwritten by 114 to 1369 patients. Not a single prescription had all necessary fields filled in. Although the field of age was most frequently omitted (1282 [90.4%] of 1418 prescriptions) among the patient’s data, the field of dosage form was most frequently omitted (3480 [91.5%] of 3805 items) among the drug data. In contrast, the scale of illegibility was rather small. The highest percentage reached only 1.5% (n = 57) in the field of drug frequency. Inaccuracies of strength, dose, and drug name were observed in 745 (19.6%), 517 (13.6%), and 435 (11.4%) prescribed drug items, respectively. ImplicationsThe unintentional shutdown of a long-running CPOE system revealed that physicians fail to handwrite flawless prescriptions in the digital era. The contingency plans for computer disasters at health care facilities might include preparation of stand-alone e-prescribing software so that the service delay could be kept to the minimum. However, guidance on prescribing should remain an essential part of medical education.

Utilization of inpatient electroconvulsive therapy in an academic medical center in Taiwan

Utilization of inpatient electroconvulsive therapy in an academic medical center in Taiwan

Patient severity matters for night-shift workload for internal medicine residents in Taiwan.

BackgroundAlthough work hour is an important factors for resident workload, other contributing factors, such as patient severity, with regards to resident workload have been scarcely studied.MethodsA prospective observational cohort study was conducted in a general medicine unit in an academic medical center in Taiwan. Every event for which the nurses needed to call the on-call residents was recorded. To quantify the workload, the responses of on-duty residents to calls were analyzed. To allow comparisons of patient factors to be made, we classified all patients by assigning them stable, unstable, or do-not-resuscitate (DNR) codes. The reasons for the calls were categorized to facilitate the comparisons across these three groups.ResultsFrom October 2009 to September 2011, a total of 2,518 patients were admitted to the general medicine unit. The nurses recorded a total of 847 calls from 730 call nights, ranging from 0 to 7 per night. Two peaks of calls, at 0-2 am and 6-7 am, were noted. Calls from stable, unstable, and DNR patients were 442 (52.2%), 95 (11.2%), and 298 (35.2%), respectively. For both unstable and DNR patients, the leading reason was abnormal vital signs (62.1% and 67.1%, respectively), while only 36.2% for stable patients. Both unstable and DNR patients required more bedside evaluation and management compared to stable patients.ConclusionBeyond work hours and patient census, patients with different clinical severity and palliative goal produce different workload for on-call residents.

After-hours physician care for patients with do-not-resuscitate orders: An observational cohort study

Background: Medical care at night for patients with do-not-resuscitate orders and the practice patterns of the on-call residents have rarely been reported. Aim: To evaluate the after-hours physician care for patients with do-not-resuscitate orders in the general medicine ward. Design: Observational study. Setting/participants: This study was conducted at an urban, university-affiliated academic medical center in Taiwan. The night shift nurses consecutively recorded every event that required calling the duty residents. Patients with and without a do-not-resuscitate order were compared in demographics, reasons for calling, residents’ response, and nurses’ satisfaction. A standard report form was established for the nurses to record events. Results: From October 2009 to September 2010, 1379 inpatients contributed to 456 after-hours calls. do-not-resuscitate patients accounted for 256 (18.7%) of all inpatients, and 160 (35.1%) of all after-hours calls. The leading reason for calls was abnormal vital signs, which was significantly higher for patients with do-not-resuscitate orders compared to patients without a do-not-resuscitate order (64.4% vs 36.1%, p < 0.001). The pattern of residents’ responses showed a significant difference with more bedside visits for patients with do-not-resuscitate orders (p < 0.001). The nurses were usually satisfied with the residents’ management of both groups. Conclusion: Abnormal vital sign, rather than symptom, was the leading reason for after-hours calls. The existence of do-not-resuscitate order produced different medical needs and physician workload. Patients with do-not-resuscitate orders accounted for one-third of night calls and nearly half of bedside visits by on-call residents and may require a different care approach.

Application of the balanced scorecard to an academic medical center in Taiwan: The effect of warning systems on improvement of hospital performance

BackgroundThe balanced scorecard (BSC) is considered to be a useful tool for management in a variety of business environments. The purpose of this article is to utilize the experimental data produced by the incorporation and implementation of the BSC in hospitals and to investigate the effects of the BSC red light tracking warning system on performance improvement. MethodsThis research was designed to be a retrospective follow-up study. The linear mixed model was applied for correcting the correlated errors. The data used in this study were secondary data collected by repeated measurements taken between 2004 and 2010 by 67 first-line medical departments of a public academic medical center in Taipei, Taiwan. The linear mixed model of analysis was applied for multilevel analysis. ResultsImprovements were observed with various time lags, from the subsequent month to three months after red light warning. During follow-up, the red light warning system more effectively improved controllable costs, infection rates, and the medical records completion rate. This further suggests that follow-up management promotes an enhancing and supportive effect to the red light warning. ConclusionThe red light follow-up management of BSC is an effective and efficient tool where improvement depends on ongoing and consistent attention in a continuing effort to better administer medical care and control costs.

Prevalence of geriatric conditions: A hospital-wide survey of 455 geriatric inpatients in a tertiary medical center

The aim of this study was to investigate the prevalence of common geriatric conditions in a tertiary medical center. We conducted a cross-sectional, hospital-wide survey of 455 inpatients, aged 65 and older, from 24 medical and surgical units of a 2200-bed urban academic medical center in Taiwan. Patients were screened in face-to-face interviews for 15 geriatric conditions. The prevalence of geriatric conditions was determined and compared by medical versus surgical services. Our sample of participants had a mean age of 75.3 ± 6.1 years (±S.D.), range = 65–92. The prevalence of geriatric conditions ranged from 5% (pressure ulcers) to 57% (polypharmacy; taking > 5 prescriptions). The majority was visually impaired (74%) and complained of sleep disturbance during their hospital stay (58%). Prevalence rates of certain geriatric conditions differed significantly between medical and surgical units, suggesting that care should address not only common conditions but also those with higher rates on different units. Furthermore, high rates of geriatric conditions indicate strong needs for care that does not fit into traditional disease models of medicine. Care should be better targeted to address different risks for geriatric conditions of medical versus surgical geriatric inpatients in acute care settings.