Explore the vast range of titles available.

The Internet can now be reached by a fingertip on a smartphone and has changed our lives on a fundamental level. However, is mental health enhanced or diminished by this digitisation of our world? Innovation, such as digital technology, is often greeted with fear and suspicion but there is currently little evidence to support this. This book examines the intersection of mental health and digital technology in order to make informed decisions about the new options provided by the internet and technology.

- Inappropriate laboratory test ordering has been shown to be as high as 30%. This can have an important impact on quality of care and costs because of downstream consequences such as additional diagnostics, repeat testing, imaging, prescriptions, surgeries, or hospital stays. - To evaluate the effect of computerized clinical decision support systems on appropriateness of laboratory test ordering. - We used MEDLINE, Embase, CINAHL, MEDLINE In-Process and Other Non-Indexed Citations, Clinicaltrials.gov, Cochrane Library, and Inspec through December 2015. Investigators independently screened articles to identify randomized trials that assessed a computerized clinical decision support system aimed at improving laboratory test ordering by providing patient-specific information, delivered in the form of an on-screen management option, reminder, or suggestion through a computerized physician order entry using a rule-based or algorithm-based system relying on an evidence-based knowledge resource. Investigators extracted data from 30 papers about study design, various study characteristics, study setting, various intervention characteristics, involvement of the software developers in the evaluation of the computerized clinical decision support system, outcome types, and various outcome characteristics. - Because of heterogeneity of systems and settings, pooled estimates of effect could not be made. Data showed that computerized clinical decision support systems had little or no effect on clinical outcomes but some effect on compliance. Computerized clinical decision support systems targeted at laboratory test ordering for multiple conditions appear to be more effective than those targeted at a single condition.

Context.—Laboratory information systems (LIS) are critical components of the operation of clinical laboratories. However, the functionalities of LIS have lagged significantly behind the capacities of current hardware and software technologies, while the complexity of the information produced by clinical laboratories has been increasing over time and will soon undergo rapid expansion with the use of new, high-throughput and high-dimensionality laboratory tests. In the broadest sense, LIS are essential to manage the flow of information between health care providers, patients, and laboratories and should be designed to optimize not only laboratory operations but also personalized clinical care. Objectives.—To list suggestions for designing LIS with the goal of optimizing the operation of clinical laboratories while improving clinical care by intelligent management of laboratory information. Data Sources.—Literature review, interviews with laboratory users, and personal experience and opinion. Conclusions.—Laboratory information systems can improve laboratory operations and improve patient care. Specific suggestions for improving the function of LIS are listed under the following sections: (1) Information Security, (2) Test Ordering, (3) Specimen Collection, Accessioning, and Processing, (4) Analytic Phase, (5) Result Entry and Validation, (6) Result Reporting, (7) Notification Management, (8) Data Mining and Cross-sectional Reports, (9) Method Validation, (10) Quality Management, (11) Administrative and Financial Issues, and (12) Other Operational Issues.

Objective. We examined completeness, an attribute of data quality, in the context of electronic laboratory reporting (ELR) of notifiable disease information to public health agencies. Methods. We extracted more than seven million ELR messages from multiple clinical information systems in two states. We calculated and compared the completeness of various data fields within the messages that were identified to be important to public health reporting processes. We compared unaltered, original messages from source systems with similar messages from another state as well as messages enriched by a health information exchange (HIE). Our analysis focused on calculating completeness (i.e., the number of nonmissing values) for fields deemed important for inclusion in notifiable disease case reports. Results. The completeness of data fields for laboratory transactions varied across clinical information systems and jurisdictions. Fields identifying the patient and test results were usually complete (97%—100%). Fields containing patient demographics, patient contact information, and provider contact information were suboptimal (6%—89%). Transactions enhanced by the HIE were found to be more complete (increases ranged from 2% to 25%) than the original messages. Conclusion. ELR data from clinical information systems can be of suboptimal quality. Public health monitoring of data sources and augmentation of ELR message content using HIE services can improve data quality.

We established a patient-oriented biobank, BioBank Japan, with information on approximately 200,000 patients, suffering from any of 47 common diseases. This follow-up survey focused on 32 diseases, potentially associated with poor vital prognosis, and collected patient survival information, including cause of death. We performed a survival analysis for all subjects to get an overview of BioBank Japan follow-up data. A total of 141,612 participants were included. The survival data were last updated in 2014. Kaplan–Meier survival analysis was performed after categorizing subjects according to sex, age group, and disease status. Relative survival rates were estimated using a survival-rate table of the Japanese general population. Of 141,612 subjects (56.48% male) with 1,087,434 person-years and a 97.0% follow-up rate, 35,482 patients died during follow-up. Mean age at enrollment was 64.24 years for male subjects and 63.98 years for female subjects. The 5-year and 10-year relative survival rates for all subjects were 0.944 and 0.911, respectively, with a median follow-up duration of 8.40 years. Patients with pancreatic cancer had the least favorable prognosis (10-year relative survival: 0.184) and patients with dyslipidemia had the most favorable prognosis (1.013). The most common cause of death was malignant neoplasms. A number of subjects died from diseases other than their registered disease(s). This is the first report to perform follow-up survival analysis across various common diseases. Further studies should use detailed clinical and genomic information to identify predictors of mortality in patients with common diseases, contributing to the implementation of personalized medicine. •141,612 participants with any of 32 diseases were included in the follow-up survey.•Subject characteristics at enrollment for the follow-up survey were identified.•The relative survival analysis showed the worst prognosis in pancreatic cancer.•The most common cause of death in all subjects was malignant neoplasms.

BackgroundLarge datasets exist in Australia that make de-identified primary healthcare data extracted from clinical information systems available for research use. This study reviews these datasets for their capacity to provide insight into chronic disease care for Aboriginal and Torres Strait Islander peoples, and the extent to which the principles of Indigenous Data Sovereignty are reflected in data collection and governance arrangements.MethodsDatasets were included if they collect primary healthcare clinical information system data, collect data nationally, and capture Aboriginal and Torres Strait Islander peoples. We searched PubMed and the public Internet for data providers meeting the inclusion criteria. We developed a framework to assess data providers across domains, including representativeness, usability, data quality, adherence with Indigenous Data Sovereignty and their capacity to provide insights into chronic disease. Datasets were assessed against the framework based on email interviews and publicly available information.ResultsWe identified seven datasets. Only two datasets reported on chronic disease, collected data nationally and captured a substantial number of Aboriginal and Torres Strait Islander patients. No dataset was identified that captured a significant number of both mainstream general practice clinics and Aboriginal Community Controlled Health Organisations.ConclusionsIt is critical that more accurate, comprehensive and culturally meaningful Aboriginal and Torres Strait Islander healthcare data are collected. These improvements must be guided by the principles of Indigenous Data Sovereignty and Governance. Validated and appropriate chronic disease indicators for Aboriginal and Torres Strait Islander peoples must be developed, including indicators of social and cultural determinants of health.

Background:Yaoundé Central Hospital (YCH), located in the capital of Cameroon, is one of the leading referral hospitals in Cameroon. The hospital has several departments, including the Department of Gynecology-Obstetrics (hereinafter referred to as “the Maternity”). This clinical department has faced numerous problems with clinical information management, including the lack of high-quality and reliable clinical information, lack of access to this information, and poor use of this information.Objective:We aim to improve the management of clinical information generated at the Maternity at YCH and to describe the challenges, success factors, and lessons learned during its implementation and use.Methods:Based on an open-source hospital information system (HIS), this intervention implemented a clinical information system (CIS) at the Maternity at YCH and was carried out using the HERMES model—the first part aimed to cover outpatient consultations, billing, and cash management of the Maternity. Geneva University Hospitals supported this project, and several outcomes were measured at the end. The following outcomes were assessed: project management, technical and organizational aspects, leadership, change management, user training, and system use.Implementation (Results):The first part of the project was completed, and the CIS was deployed in the Maternity at YCH. The main technical activities were adapting the open-source HIS to manage outpatient consultations and develop integrated billing and cash management software. In addition to technical aspects, we implemented several other activities. They consisted of the implementation of appropriate project governance or management, improvement of the organizational processes at the Maternity, promotion of the local digital health leadership and performance of change management, and implementation of the training and support of users. Despite barriers encountered during the project, the 6-month evaluation showed that the CIS was effectively used during the first 6 months.Conclusions:Implementation of the HIS or CIS is feasible in a resource-limited setting such as Cameroon. The CIS was implemented based on good practices at the Maternity at YCH. This project had successes but also many challenges. Beyond project management and technical and financial aspects, the other main problems of implementing health information systems or HISs in Africa lie in digital health leadership, governance, and change management. This digital health leadership, governance, and change management should prioritize data as a tool for improving productivity and managing health institutions, and promote a data culture among health professionals to support a change in mindset and the acquisition of information management skills. Moreover, in countries with a highly centralized political system like ours, a high-level strategic and political anchor for such projects is often necessary to guarantee their success.

Geriatric comanagement has been shown to improve outcomes of older surgical inpatients. Furthermore, the choice of discharge location, that is, continuity of care, can have a fundamental impact on convalescence. These challenges and demands have led to the SURGE-Ahead project that aims to develop a clinical decision support system (CDSS) for geriatric comanagement in surgical clinics including a decision support for the best continuity of care option, supported by artificial intelligence (AI) algorithms. This qualitative study aims to explore the current challenges and demands in surgical geriatric patient care. Based on these challenges, the study explores the attitude of interviewees toward the introduction of an AI-supported CDSS (AI-CDSS) in geriatric patient care in surgery, focusing on technical and general wishes about an AI-CDSS, as well as ethical considerations. In this study, 15 personal interviews with physicians, nurses, physiotherapists, and social workers, employed in surgical departments at a university hospital in Southern Germany, were conducted in April 2022. Interviews were conducted in person, transcribed, and coded by 2 researchers (AU, LB) using content and thematic analysis. During the analysis, quotes were sorted into the main categories of geriatric patient care, use of an AI-CDSS, and ethical considerations by 2 authors (AU, LB). The main themes of the interviews were subsequently described in a narrative synthesis, citing key quotes. In total, 399 quotes were extracted and categorized from the interviews. Most quotes could be assigned to the primary code challenges in geriatric patient care (111 quotes), with the most frequent subcode being medical challenges (45 quotes). More quotes were assigned to the primary code chances of an AI-CDSS (37 quotes), with its most frequent subcode being holistic patient overview (16 quotes), then to the primary code limits of an AI-CDSS (26 quotes). Regarding the primary code technical wishes (37 quotes), most quotes could be assigned to the subcode intuitive usability (15 quotes), followed by mobile availability and easy access (11 quotes). Regarding the main category ethical aspects of an AI-CDSS, most quotes could be assigned to the subcode critical position toward trust in an AI-CDSS (9 quotes), followed by the subcodes respecting the patient's will and individual situation (8 quotes) and responsibility remaining in the hands of humans (7 quotes). Support regarding medical geriatric challenges and responsible handling of AI-based recommendations, as well as necessity for a holistic approach focused on usability, were the most important topics of health care professionals in surgery regarding development of an AI-CDSS for geriatric care. These findings, together with the wish to preserve the patient-caregiver relationship, will help set the focus for the ongoing development of AI-supported CDSS.

The global cancer burden is rapidly increasing, with 20 million new cases estimated in 2022. The world population aged ≥65 years is also increasing, projected to reach 15.9% by 2050, making cancer control for older patients urgent. Surgical resection is important for cancer treatment; however, predicting postoperative disability and mortality in older patients is crucial for surgical decision-making, considering the quality of life and care burden. Currently, no model directly predicts postoperative functional disability in this population. We aimed to develop and validate machine-learning models to predict postoperative functional disability (≥5-point decrease in the Barthel Index) or in-hospital death in patients with cancer aged ≥ 65 years. This retrospective cohort study included patients aged ≥65 years who underwent surgery for major cancers (lung, stomach, colorectal, liver, pancreatic, breast, or prostate cancer) between April 2016 and March 2023 in 70 Japanese hospitals across 6 regional groups. One group was randomly selected for external validation, while the remaining 5 groups were randomly divided into training (70%) and internal validation (30%) sets. Predictor variables were selected from 37 routinely available preoperative factors through electronic medical records (age, sex, income, comorbidities, laboratory values, and vital signs) using crude odds ratios (P<.1) and the least absolute shrinkage and selection operator method. We developed 6 machine-learning models, including category boosting (CatBoost), extreme gradient boosting (XGBoost), logistic regression, neural networks, random forest, and support vector machine. Model predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC) with 95% CI. We used the Shapley additive explanations (SHAP) method to evaluate contribution to the predictive performance for each predictor variable. This study included 33,355 patients in the training, 14,294 in the internal validation, and 6711 in the external validation sets. In the training set, 1406/33,355 (4.2%) patients experienced worse discharge. A total of 24 predictor variables were selected for the final models. CatBoost and XGBoost achieved the largest AUCs among the 6 models: 0.81 (95% CI 0.80-0.82) and 0.81 (95% CI 0.80-0.82), respectively. In the top 15 influential factors based on the mean absolute SHAP value, both models shared the same 14 factors such as dementia, age ≥85 years, and gastrointestinal cancer. The CatBoost model showed the largest AUCs in both internal (0.77, 95% CI 0.75-0.79) and external validation (0.72, 95% CI 0.68-0.75). The CatBoost model demonstrated good performance in predicting postoperative outcomes for older patients with cancer using routinely available preoperative factors. The robustness of these findings was supported by the identical top influential factors between the CatBoost and XGBoost models. This model could support surgical decision-making while considering postoperative quality of life and care burden, with potential for implementation through electronic health records.