Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach.

Background: The medical subdomain of a clinical note, such as cardiology or neurology, is useful content-derived metadata for developing machine learning downstream applications. To classify the medical subdomain of a note accurately, we have constructed a machine learning-based natural language processing (NLP) pipeline and developed medical subdomain classifiers based on the content of the note.

Methods: We constructed the pipeline using the clinical NLP system, clinical Text Analysis and Knowledge Extraction System (cTAKES), the Unified Medical Language System (UMLS) Metathesaurus, Semantic Network, and learning algorithms to extract features from two datasets - clinical notes from Integrating Data for Analysis, Anonymization, and Sharing (iDASH) data repository (n = 431) and Massachusetts General Hospital (MGH) (n = 91,237), and built medical subdomain classifiers with different combinations of data representation methods and supervised learning algorithms.

Non-visit-based cancer screening using a novel population management system.

Background: Advances in information technology (IT) now permit population-based preventive screening, but the best methods remain uncertain. We evaluated whether involving primary care providers (PCPs) in a visit-independent population management IT application led to more effective cancer screening.

Methods: We conducted a cluster-randomized trial involving 18 primary care practice sites and 169 PCPs from June 15, 2011, to June 14, 2012.

Development and implementation of a real-time 30-day readmission predictive model.

Hospitals are under great pressure to reduce readmissions of patients. Being able to reliably predict patients at increased risk for rehospitalization would allow for tailored interventions to be offered to them. This requires the creation of a functional predictive model specifically designed to support real-time clinical operations.

Applying operations research to optimize a novel population management system for cancer screening.

Objective: To optimize a new visit-independent, population-based cancer screening system (TopCare) by using operations research techniques to simulate changes in patient outreach staffing levels (delegates, navigators), modifications to user workflow within the information technology (IT) system, and changes in cancer screening recommendations.

Materials And Methods: TopCare was modeled as a multiserver, multiphase queueing system. Simulation experiments implemented the queueing network model following a next-event time-advance mechanism, in which systematic adjustments were made to staffing levels, IT workflow settings, and cancer screening frequency in order to assess their impact on overdue screenings per patient.

Impact of a computer-based diagnostic decision support tool on the differential diagnoses of medicine residents.

Background: Computer-based medical diagnostic decision support systems have been used for decades, initially as stand-alone applications. More recent versions have been tested for their effectiveness in enhancing the diagnostic ability of clinicians.

Objective: To determine if viewing a rank-ordered list of diagnostic possibilities from a medical diagnostic decision support system improves residents' differential diagnoses or management plans.

Assessing hospital readmission risk factors in heart failure patients enrolled in a telemonitoring program.

The purpose of this study was to validate a previously developed heart failure readmission predictive algorithm based on psychosocial factors, develop a new model based on patient-reported symptoms from a telemonitoring program, and assess the impact of weight fluctuations and other factors on hospital readmission. Clinical, demographic, and telemonitoring data was collected from 100 patients enrolled in the Partners Connected Cardiac Care Program between July 2008 and November 2011. 38% of study participants were readmitted to the hospital within 30 days.

Efficacy of a clinical decision-support system in an HIV practice: a randomized trial.

Background: Data to support improved patient outcomes from clinical decision-support systems (CDSSs) are lacking in HIV care.

Objective: To test the efficacy of a CDSS in improving HIV outcomes in an outpatient clinic.

Design: Randomized, controlled trial.

Linking electronic health record-extracted psychosocial data in real-time to risk of readmission for heart failure.

Background: Knowledge of psychosocial characteristics that helps to identify patients at increased risk for readmission for heart failure (HF) may facilitate timely and targeted care.

Objective: We hypothesized that certain psychosocial characteristics extracted from the electronic health record (EHR) would be associated with an increased risk for hospital readmission within the next 30 days.

Methods: We identified 15 psychosocial predictors of readmission.

A Clinical Database-Driven Approach to Decision Support: Predicting Mortality Among Patients with Acute Kidney Injury.

In exploring an approach to decision support based on information extracted from a clinical database, we developed mortality prediction models of intensive care unit (ICU) patients who had acute kidney injury (AKI) and compared them against the Simplified Acute Physiology Score (SAPS). We used MIMIC, a public de-identified database of ICU patients admitted to Beth Israel Deaconess Medical Center, and identified 1400 patients with an ICD9 diagnosis of AKI and who had an ICU stay > 3 days. Multivariate regression models were built using the SAPS variables from the first 72 hours of ICU admission.

A cluster-randomized trial of a primary care informatics-based system for breast cancer screening.

Background: Information technology offers the promise, as yet unfulfilled, of delivering efficient, evidence-based health care.

Objective: To evaluate whether a primary care network-based informatics intervention can improve breast cancer screening rates.

Design: Cluster-randomized controlled trial of 12 primary care practices conducted from March 20, 2007 to March 19, 2008.

Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2).

Informatics for Integrating Biology and the Bedside (i2b2) is one of seven projects sponsored by the NIH Roadmap National Centers for Biomedical Computing (http://www.ncbcs.org).

Queuing theory to guide the implementation of a heart failure inpatient registry program.

OBJECTIVE The authors previously implemented an electronic heart failure registry at a large academic hospital to identify heart failure patients and to connect these patients with appropriate discharge services. Despite significant improvements in patient identification and connection rates, time to connection remained high, with an average delay of 3.2 days from the time patients were admitted to the time connections were made.

Physician ability to assess rheumatoid arthritis disease activity using an electronic medical record-based disease activity calculator.

Objective: To assess physicians' concordance with Disease Activity Score in 28 joints (DAS28) categories calculated by an electronic medical record (EMR)-embedded disease activity calculator, as well as attitudes toward this application.

Methods: Fifteen rheumatologists used the EMR-embedded disease activity calculator to predict a rheumatoid arthritis (RA) DAS28 disease activity category at the time of each clinical encounter.

Results: Physician-predicted DAS28 disease activity categories ranged from high (>5.

A rheumatology-specific informatics-based application with a disease activity calculator.

Objective: To design a rheumatology-specific tool with a disease activity calculator integrated into the electronic medical records (EMRs) at our institution and assess physicians' attitudes toward the use of this tool.

Methods: The Rheumatology OnCall (ROC) application culls rheumatology-pertinent data from our institution's laboratory, microbiology, pathology, radiology, and pharmacy information systems. Attending rheumatologists and rheumatology fellows accessed the ROC and disease activity calculator during outpatient visits at the time of the clinical encounter.

Mammography FastTrack: an intervention to facilitate reminders for breast cancer screening across a heterogeneous multi-clinic primary care network.

Health care information technology can be a means to improve quality and efficiency in the primary care setting. However, merely applying technology without addressing how it fits into provider workflow and existing systems is unlikely to achieve improvement goals. Improving quality of primary care, such as cancer screening rates, requires addressing barriers at system, provider, and patient levels.

Quicksilver: a novel publish and subscribe clinical messaging platform.

The authors introduce and describe the features of Quicksilver, a novel clinical messaging platform deployed in a multidisciplinary academic primary care clinic. The system follows a publication-subscription messaging paradigm employing dynamic role-based addressing. Quicksilver leverages the open-source XMPP, a powerful and extensible protocol for efficient asynchronous and synchronous communication commonly used in instant messaging applications.

Architecture of the open-source clinical research chart from Informatics for Integrating Biology and the Bedside.

The Informatics for Integrating Biology and the Bedside (i2b2) is one of the sponsored initiatives of the NIH Roadmap National Centers for Biomedical Computing (http://www.bisti.nih.

Designing healthcare information technology to catalyse change in clinical care.

The gap between best practice and actual patient care continues to be a pervasive problem in our healthcare system. Efforts to improve on this knowledge-performance gap have included computerised disease management programs designed to improve guideline adherence. However, current computerised reminder and decision support interventions directed at changing physician behaviour have had only a limited and variable effect on clinical outcomes.

Lessons from implementing a combined workflow-informatics system for diabetes management.

Shortcomings surrounding the care of patients with diabetes have been attributed largely to a fragmented, disorganized, and duplicative health care system that focuses more on acute conditions and complications than on managing chronic disease. To address these shortcomings, we developed a diabetes registry population management application to change the way our staff manages patients with diabetes. Use of this new application has helped us coordinate the responsibilities for intervening and monitoring patients in the registry among different users.

Diabetes information technology: designing informatics systems to catalyze change in clinical care.

Current computerized reminder and decision support systems intended to improve diabetes care have had a limited effect on clinical outcomes. Increasing pressures on health care networks to meet standards of diabetes care have created an environment where information technology systems for diabetes management are often created under duress, appended to existing clinical systems, and poorly integrated into the existing workflow. After defining the components of diabetes disease management, the authors present an eight-step conceptual framework to guide the development of more effective diabetes information technology systems for translating clinical information into clinical action.

A self-scaling, distributed information architecture for public health, research, and clinical care.

Objective: This study sought to define a scalable architecture to support the National Health Information Network (NHIN). This architecture must concurrently support a wide range of public health, research, and clinical care activities.

Study Design: The architecture fulfils five desiderata: (1) adopt a distributed approach to data storage to protect privacy, (2) enable strong institutional autonomy to engender participation, (3) provide oversight and transparency to ensure patient trust, (4) allow variable levels of access according to investigator needs and institutional policies, (5) define a self-scaling architecture that encourages voluntary regional collaborations that coalesce to form a nationwide network.

Calculating the benefits of a Research Patient Data Repository.

The Research Patient Data Repository (RPDR) is a clinical data registry that gathers medical records from various hospital systems and stores them centrally in one data warehouse. Research investigators can obtain aggregate total of patients that meet specific query criteria and can obtain patient identifiers and complete electronic medical records through the RPDR with IRB approval. The existence of the RPDR is a critical resource to the Partners HealthCare System research community and supports many millions of dollars in clinical research.

Integration of clinical and genetic data in the i2b2 architecture.

The Informatics for Integrating Biology and the Bedside (i2b2) is one of the sponsored initiatives of the NIH Roadmap National Centers for Biomedical Computing (http://www.bisti.nih.