Medical information management

With progress in information and communication technology, medical information has been converted to digital formats and stored and managed using computer systems. The construction, management, and operation of medical information systems and regional medical liaison systems are the main components of the clinical tasks of medical informatics departments. Research using medical information accumulated in these systems is also a task for medical informatics department. Recently, medical real-world data (RWD) accumulated in medical information systems has become a focus not only for primary use but also for secondary uses of medical information. However, there are many problems, such as standardization, collection, cleaning, and analysis of them. The internet of things and artificial intelligence are also being applied in the collection and analysis of RWD and in resolving the above problems. Using these new technologies, progress in medical care and clinical research is about to enter a new era

Accuracy of an Artificial Intelligence–Based Model for Estimating Leftover Liquid Food in Hospitals : Validation Study

Background: An accurate evaluation of the nutritional status of malnourished hospitalized patients at a higher risk of complications, such as frailty or disability, is crucial. Visual methods of estimating food intake are popular for evaluating the nutritional status in clinical environments. However, from the perspective of accurate measurement, such methods are unreliable. Objective: The accuracy of estimating leftover liquid food in hospitals using an artificial intelligence (AI)–based model was compared to that of visual estimation. Methods: The accuracy of the AI-based model (AI estimation) was compared to that of the visual estimation method for thin rice gruel as staple food and fermented milk and peach juice as side dishes. A total of 576 images of liquid food (432 images of thin rice gruel, 72 of fermented milk, and 72 of peach juice) were used. The mean absolute error, root mean squared error, and coefficient of determination (R2) were used as metrics for determining the accuracy of the evaluation process. Welch t test and the confusion matrix were used to examine the difference of mean absolute error between AI and visual estimation. Results: The mean absolute errors obtained through the AI estimation approach were 0.63 for fermented milk, 0.25 for peach juice, and 0.85 for the total. These were significantly smaller than those obtained using the visual estimation approach, which were 1.40 (P<.001) for fermented milk, 0.90 (P<.001) for peach juice, and 1.03 (P=.009) for the total. By contrast, the mean absolute error for thin rice gruel obtained using the AI estimation method (0.99) did not differ significantly from that obtained using visual estimation (0.99). The confusion matrix for thin rice gruel showed variation in the distribution of errors, indicating that the errors in the AI estimation were biased toward the case of many leftovers. The mean squared error for all liquid foods tended to be smaller for the AI estimation than for the visual estimation. Additionally, the coefficient of determination (R2) for fermented milk and peach juice tended to be larger for the AI estimation than for the visual estimation, and the R2 value for the total was equal in terms of accuracy between the AI and visual estimations. Conclusions: The AI estimation approach achieved a smaller mean absolute error and root mean squared error and a larger coefficient of determination (R2) than the visual estimation approach for the side dishes. Additionally, the AI estimation approach achieved a smaller mean absolute error and root mean squared error compared to the visual estimation method, and the coefficient of determination (R2) was similar to that of the visual estimation method for the total. AI estimation measures liquid food intake in hospitals more precisely than visual estimation, but its accuracy in estimating staple food leftovers requires improvement

Impact of a learning health system on acute care and medical complications after intracerebral hemorrhage

Introduction: Patients with stroke often experience pneumonia during the acute stage after stroke onset. Oral care may be effective in reducing the risk of stroke‐associated pneumonia (SAP). We aimed to determine the changes in oral care, as well as the incidence of SAP, in patients with intracerebral hemorrhage, following implementation of a learning health system in our hospital. Methods: We retrospectively analyzed the data of 1716 patients with intracerebral hemorrhage who were hospitalized at a single stroke center in Japan between January 2012 and December 2018. Data were stratified on the basis of three periods of evolving oral care: period A, during which conventional, empirically driven oral care was provided (n = 725); period B, during which standardized oral care was introduced, with SAP prophylaxis based on known risk factors (n = 469); and period C, during which oral care was risk‐appropriate based on learning health system data (n = 522). Logistic regression analysis was performed to evaluate associations between each of the three treatment approaches and the risk of SAP. Results: Among the included patients, the mean age was 71.3 ± 13.6 years; 52.6% of patients were men. During the course of each period, the frequency of oral care within 24 hours of admission increased (P < .001), as did the adherence rate to oral care ≥3 times per day (P < .001). After adjustment for confounding factors, a change in the risk of SAP was not observed in period B; however, the risk significantly decreased in period C (odds ratio 0.61; 95% confidence interval 0.43‐0.87) compared with period A. These associations were maintained for SAP diagnosed using strict clinical criteria or after exclusion of 174 patients who underwent neurosurgical treatment. Conclusions: Risk‐appropriate care informed by the use of learning health system data could improve care and potentially reduce the risk of SAP in patients with intracerebral hemorrhage in the acute stage

Predictive factors of mortality of patients with fragility hip fractures at 1 year after discharge : A multicenter, retrospective study in the northern Kyushu district of Japan

Purpose: Fragility hip fractures (FHFs) are associated with a high risk of mortality, but the relative contribution of various factors remains controversial. This study aimed to evaluate predictive factors of mortality at 1 year after discharge in Japan. Methods: A total of 497 patients aged 60 years or older who sustained FHFs during follow-up were included in this study. Expected variables were finally assessed using multivariable Cox proportional hazards models. Results: The 1-year mortality rate was 9.1% (95% confidence interval: 6.8–12.0%, n = 45). Log-rank test revealed that previous fractures (p = 0.003), Barthel index (BI) at discharge (p = 0.011), and place-to-discharge (p = 0.004) were significantly associated with mortality for male patients. Meanwhile, body mass index (BMI; p = 0.023), total Charlson comorbidity index (TCCI; p = 0.005), smoking (p = 0.007), length of hospital stay (LOS; p = 0.009), and BI (p = 0.004) were the counterparts for females. By multivariate analyses, previous vertebral fractures (hazard ratio (HR) 3.33; p = 0.044), and BI <30 (HR 5.42, p = 0.013) were the predictive variables of mortality for male patients. BMI <18.5 kg/m2 (HR 2.70, p = 0.023), TCCI ≥5 (HR 2.61, p = 0.032), smoking history (HR 3.59, p = 0.018), LOS <14 days (HR 13.9; p = 0.007), and BI <30 (HR 2.76; p = 0.049) were the counterparts for females. Conclusions: Previous vertebral fractures and BI <30 were the predictive variables of mortality for male patients, and BMI <18.5 kg/m2, TCCI ≥5, smoking history, LOS <14 days, and BI <30 were those for females. Decreased BI is one of the independent and preventable risk factors. A comprehensive therapeutic approach should be considered to prevent deterioration of activities of daily living and a higher risk of mortality

Prehospital Lactated Ringer's Solution Treatment and Survival in Out-of-Hospital Cardiac Arrest: A Prospective Cohort Analysis

<div><p>Background</p><p>No studies have evaluated whether administering intravenous lactated Ringer's (LR) solution to patients with out-of-hospital cardiac arrest (OHCA) improves their outcomes, to our knowledge. Therefore, we examined the association between prehospital use of LR solution and patients' return of spontaneous circulation (ROSC), 1-month survival, and neurological or physical outcomes at 1 month after the event.</p> <p>Methods and Findings</p><p>We conducted a prospective, non-randomized, observational study using national data of all patients with OHCA from 2005 through 2009 in Japan. We performed a propensity analysis and examined the association between prehospital use of LR solution and short- and long-term survival. The study patients were ≥18 years of age, had an OHCA before arrival of EMS personnel, were treated by EMS personnel, and were then transported to hospitals. A total of 531,854 patients with OHCA met the inclusion criteria. Among propensity-matched patients, compared with those who did not receive pre-hospital intravenous fluids, prehospital use of LR solution was associated with an increased likelihood of ROSC before hospital arrival (odds ratio [OR] adjusted for all covariates [95% CI] = 1.239 [1.146–1.339] [<i>p</i><0.001], but with a reduced likelihood of 1-month survival with minimal neurological or physical impairment (cerebral performance category 1 or 2, OR adjusted for all covariates [95% CI] = 0.764 [0.589–0.992] [<i>p</i> = 0.04]; and overall performance category 1 or 2, OR adjusted for all covariates [95% CI] = 0.746 [0.573–0.971] [<i>p</i> = 0.03]). There was no association between prehospital use of LR solution and 1-month survival (OR adjusted for all covariates [95% CI] = 0.960 [0.854–1.078]).</p> <p>Conclusion</p><p>In Japanese patients experiencing OHCA, the prehospital use of LR solution was independently associated with a decreased likelihood of a good functional outcome 1 month after the event, but with an increased likelihood of ROSC before hospital arrival. Prehospital use of LR solution was not associated with 1-month survival. Further study is necessary to verify these findings.</p> <p> <i>Please see later in the article for the Editors' Summary</i></p> </div

<b>Table 1.</b> Baseline characteristics of patients with OHCA according to LR solution use: 2005–2009 national data in Japan (<i>n = </i>531,854).

<p>ALS, advanced life support; PEA, pulseless electrical activity; SD, standard deviation; VT, ventricular tachycardia.</p

<b>Table 1.</b> Baseline characteristics of patients with OHCA according to LR solution use: 2005–2009 national data in Japan (<i>n = </i>531,854).

<p>ALS, advanced life support; PEA, pulseless electrical activity; SD, standard deviation; VT, ventricular tachycardia.</p

Conditional logistic regression analysis of prehospital LR solution use and outcomes among propensity-matched patients with OHCA: 2005–2009 national data in Japan (<i>n = </i>152,586).

a<p>Significant variables in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001394#pmed-1001394-t002" target="_blank">Table 2</a> included cases by year, bystander eyewitness, emergency life-saving technician in ambulance, time from call to arrival at hospital, and epinephrine use.</p>b<p>Selected variables included age, sex, relationship of bystander to patient, bystander chest compression, bystander rescue breathing, use of public-access AED by bystander, first documented rhythm, origin of OHCA, and time from call to arrival at the scene. These variables have been shown to be factors in resuscitation outcome.</p>c<p>All covariates included all variables except for endpoint variables in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001394#pmed-1001394-t002" target="_blank">Table 2</a> and 46 dummy variables for the 47 prefectures in Japan.</p

Results of conditional logistic regression analyses comparing prehospital LR solution use versus no prehospital LR solution use in propensity-matched patients with OHCA.

<p>Results of conditional logistic regression analyses comparing prehospital LR solution use versus no prehospital LR solution use in propensity-matched patients with OHCA.</p