A pragmatic cluster randomised controlled trial of a Diabetes REcall And Management system: the DREAM trial

BACKGROUND: Following the introduction of a computerised diabetes register in part of the northeast of England, care initially improved but then plateaued. We therefore enhanced the existing diabetes register to address these problems. The aim of the trial was to evaluate the effectiveness and efficiency of an area wide 'extended,' computerised diabetes register incorporating a full structured recall and management system, including individualised patient management prompts to primary care clinicians based on locally-adapted, evidence-based guidelines. METHODS: The study design was a pragmatic, cluster randomised controlled trial, with the general practice as the unit of randomisation. Set in 58 general practices in three Primary Care Trusts in the northeast of England, the study outcomes were the clinical process and outcome variables held on the diabetes register, patient-reported outcomes, and service and patient costs. The effect of the intervention was estimated using generalised linear models with an appropriate error structure. To allow for the clustering of patients within practices, population averaged models were estimated using generalized estimating equations. RESULTS: Patients in intervention practices were more likely to have at least one diabetes appointment recorded (OR 2.00, 95% CI 1.02, 3.91), to have a recording of a foot check (OR 1.87, 95% CI 1.09, 3.21), have a recording of receiving dietary advice (OR 2.77, 95% CI 1.22, 6.29), and have a recording of blood pressure (BP) (OR 2.14, 95% CI 1.06, 4.36). There was no difference in mean HbA1c or BP levels, but the mean cholesterol level in patients from intervention practices was significantly lower (-0.15 mmol/l, 95% CI -0.25, -0.06). There were no differences in patient-reported outcomes or in patient-reported use of drugs, or uptake of health services. The average cost per patient was not significantly different between the intervention and control groups. Costs incurred in administering the system at the register and in general practice were in addition to these. CONCLUSION: This study has shown benefits from an area-wide, computerised diabetes register incorporating a full structured recall and individualised patient management system. However, these benefits were achieved at a cost. In future, these costs may fall as electronic data exchange becomes a reliable reality. Trial registration: International Standard Randomised Controlled Trial Number (ISRCTN) Register, ISRCTN32042030

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

Syndromic surveillance: reports from a national conference, 2003

Overview of Syndromic Surveillance -- What is Syndromic Surveillance? -- Linking Better Surveillance to Better Outcomes -- Review of the 2003 National Syndromic Surveillance Conference - Lessons Learned and Questions To Be Answered -- -- System Descriptions -- New York City Syndromic Surveillance Systems -- Syndrome and Outbreak Detection Using Chief-Complaint Data - Experience of the Real-Time Outbreak and Disease Surveillance Project -- Removing a Barrier to Computer-Based Outbreak and Disease Surveillance - The RODS Open Source Project -- National Retail Data Monitor for Public Health Surveillance -- National Bioterrorism Syndromic Surveillance Demonstration Program -- Daily Emergency Department Surveillance System - Bergen County, New Jersey -- Hospital Admissions Syndromic Surveillance - Connecticut, September 2001-November 2003 -- BioSense - A National Initiative for Early Detection and Quantification of Public Health Emergencies -- Syndromic Surveillance at Hospital Emergency Departments - Southeastern Virginia -- -- Research Methods -- Bivariate Method for Spatio-Temporal Syndromic Surveillance -- Role of Data Aggregation in Biosurveillance Detection Strategies with Applications from ESSENCE -- Scan Statistics for Temporal Surveillance for Biologic Terrorism -- Approaches to Syndromic Surveillance When Data Consist of Small Regional Counts -- Algorithm for Statistical Detection of Peaks - Syndromic Surveillance System for the Athens 2004 Olympic Games -- Taming Variability in Free Text: Application to Health Surveillance -- Comparison of Two Major Emergency Department-Based Free-Text Chief-Complaint Coding Systems -- How Many Illnesses Does One Emergency Department Visit Represent? Using a Population-Based Telephone Survey To Estimate the Syndromic Multiplier -- Comparison of Office Visit and Nurse Advice Hotline Data for Syndromic Surveillance - Baltimore-Washington, D.C., Metropolitan Area, 2002 -- Progress in Understanding and Using Over-the-Counter Pharmaceuticals for Syndromic Surveillance -- -- Evaluation -- Evaluation Challenges for Syndromic Surveillance - Making Incremental Progress -- Measuring Outbreak-Detection Performance By Using Controlled Feature Set Simulations -- Evaluation of Syndromic Surveillance Systems - Design of an Epidemic Simulation Model -- Benchmark Data and Power Calculations for Evaluating Disease Outbreak Detection Methods -- Bio-ALIRT Biosurveillance Detection Algorithm Evaluation -- ESSENCE II and the Framework for Evaluating Syndromic Surveillance Systems -- Conducting Population Behavioral Health Surveillance by Using Automated Diagnostic and Pharmacy Data Systems -- Evaluation of an Electronic General-Practitioner-Based Syndromic Surveillance System -- National Symptom Surveillance Using Calls to a Telephone Health Advice Service - United Kingdom, December 2001-February 2003 -- Field Investigations of Emergency Department Syndromic Surveillance Signals - New York City -- Should We Be Worried? Investigation of Signals Generated by an Electronic Syndromic Surveillance System - Westchester County, New York -- -- Public Health Practice -- Public Health Information Network - Improving Early Detection by Using a Standards-Based Approach to Connecting Public Health and Clinical Medicine -- Information System Architectures for Syndromic Surveillance -- Perspective of an Emergency Physician Group as a Data Provider for Syndromic Surveillance -- SARS Surveillance Project - Internet-Enabled Multiregion Surveillance for Rapidly Emerging Disease -- Health Information Privacy and Syndromic Surveillance SystemsPapers from the second annual National Syndromic Surveillance Conference convened by the New York City Department of Health and Mental Hygiene, the New York Academy of Medicine, and the CDC in New York City during Oct. 23-24, 2003. Published as the September 24, 2004 supplement to vol. 53 of MMWR. Morbidity and mortality weekly report.1571461

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers