Real-time Clinical Decision Support Research Articles

Pharmacy surveillance and low-literacy pharmacist intervention

McCoy et al. 1. McCoy A.B. Cox Z.L. Neal E.B. et al. Real-time pharmacy surveillance and clinical decision support to reduce adverse drug events in acute kidney injury: a randomized, controlled trial. Appl Clin Inform. 2012; 3: 221-238 Crossref PubMed Scopus (29) Google Scholar concluded that real-time “pharmacy surveillance had no incremental benefit” compared with preexisting clinical decision support (CDS) systems (e.g., computer alerts) in detecting and preventing medication errors. Nearly 400 acute kidney injury patients were studied in a 3-month randomized clinical trial. Adult patients were enrolled who experienced a ±0.5 mg/dL change in serum creatinine over 48 hours. They had an active order for one or more nephrotoxic or renally cleared medications. In addition to CDS alerts and usual care pharmacy services (e.g., rounding with medical and surgical teams), the intervention included real-time monitoring of drug prescribing and kidney function trends by pharmacists using a Web-based surveillance tool. Blinded judges determined that potential or actual adverse drug events (ADEs) occurred for 104 (8.0%) usual care and 99 (7.1%) intervention group patients (P = 0.4). Time until providers modified or discontinued targeted nephrotoxic or renally cleared medications did not differ between usual care and intervention patients (33.4 vs. 30.3 hours, P = 0.3).

Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy

Earlier on, a conceptual design on the real-time clinical decision support system (rt-CDSS) with data stream mining was proposed and published. The new system is introduced that can analyze medical data streams and can make real-time prediction. This system is based on a stream mining algorithm called VFDT. The VFDT is extended with the capability of using pointers to allow the decision tree to remember the mapping relationship between leaf nodes and the history records. In this paper, which is a sequel to the rt-CDSS design, several popular machine learning algorithms are investigated for their suitability to be a candidate in the implementation of classifier at the rt-CDSS. A classifier essentially needs to accurately map the events inputted to the system into one of the several predefined classes of assessments, such that the rt-CDSS can follow up with the prescribed remedies being recommended to the clinicians. For a real-time system like rt-CDSS, the major technological challenges lie in the capability of the classifier to process, analyze and classify the dynamic input data, quickly and upmost reliably. An experimental comparison is conducted. This paper contributes to the insight of choosing and embedding a stream mining classifier into rt-CDSS with a case study of diabetes therapy.

Biometrics and Biosecurity 2013

Biometrics and Biosecurity 2013

Real-time pharmacy surveillance and clinical decision support to reduce adverse drug events in acute kidney injury

OBJECTIVES: Clinical decision support (CDS), such as computerized alerts, improves prescribing in the setting of acute kidney injury (AKI), but considerable opportunity remains to improve patient safety. The authors sought to determine whether pharmacy surveillance of AKI patients could detect and prevent medication errors that are not corrected by automated interventions. METHODS: The authors conducted a randomized clinical trial among 396 patients admitted to an academic, tertiary care hospital between June 1, 2010 and August 31, 2010 with an acute 0.5 mg/dl change in serum creatinine over 48 hours and a nephrotoxic or renally cleared medication order. Patients randomly assigned to the intervention group received surveillance from a clinical pharmacist using a web-based surveillance tool to monitor drug prescribing and kidney function trends. CDS alerting and standard pharmacy services were active in both study arms. Outcome measures included blinded adjudication of potential adverse drug events (pADEs), adverse drug events (ADEs) and time to provider modification or discontinuation of targeted nephrotoxic or renally cleared medications. RESULTS: Potential ADEs or ADEs occurred for 104 (8.0%) of control and 99 (7.1%) of intervention patient-medication pairs (p=0.4). Additionally, the time to provider modification or discontinuation of targeted nephrotoxic or renally cleared medications did not differ between control and intervention patients (33.4 hrs vs. 30.3 hrs, p=0.3). CONCLUSIONS: Pharmacy surveillance had no incremental benefit over previously implemented CDS alerts.

Real-time clinical decision support system with data stream mining.

This research aims to describe a new design of data stream mining system that can analyze medical data stream and make real-time prediction. The motivation of the research is due to a growing concern of combining software technology and medical functions for the development of software application that can be used in medical field of chronic disease prognosis and diagnosis, children healthcare, diabetes diagnosis, and so forth. Most of the existing software technologies are case-based data mining systems. They only can analyze finite and structured data set and can only work well in their early years and can hardly meet today's medical requirement. In this paper, we describe a clinical-support-system based data stream mining technology; the design has taken into account all the shortcomings of the existing clinical support systems.

Real-time automated paging and decision support for critical laboratory abnormalities

BackgroundFor patients with critical laboratory abnormalities, timely clinical alerts with decision support could improve management and reduce adverse events.MethodsThe authors developed a real-time clinical alerting system for critical laboratory abnormalities....

Development of a real-time clinical decision support system upon the web mvc-based architecture for prostate cancer treatment

BackgroundA real-time clinical decision support system (RTCDSS) with interactive diagrams enables clinicians to instantly and efficiently track patients' clinical records (PCRs) and improve their quality of clinical care. We propose a RTCDSS to process online clinical informatics from multiple databases for clinical decision making in the treatment of prostate cancer based on Web Model-View-Controller (MVC) architecture, by which the system can easily be adapted to different diseases and applications.MethodsWe designed a framework upon the Web MVC-based architecture in which the reusable and extractable models can be conveniently adapted to other hospital information systems and which allows for efficient database integration. Then, we determined the clinical variables of the prostate cancer treatment based on participating clinicians' opinions and developed a computational model to determine the pretreatment parameters. Furthermore, the components of the RTCDSS integrated PCRs and decision factors for real-time analysis to provide evidence-based diagrams upon the clinician-oriented interface for visualization of treatment guidance and health risk assessment.ResultsThe resulting system can improve quality of clinical treatment by allowing clinicians to concurrently analyze and evaluate the clinical markers of prostate cancer patients with instantaneous clinical data and evidence-based diagrams which can automatically identify pretreatment parameters. Moreover, the proposed RTCDSS can aid interactions between patients and clinicians.ConclusionsOur proposed framework supports online clinical informatics, evaluates treatment risks, offers interactive guidance, and provides real-time reference for decision making in the treatment of prostate cancer. The developed clinician-oriented interface can assist clinicians in conveniently presenting evidence-based information to patients and can be readily adapted to an existing hospital information system and be easily applied in other chronic diseases.

Clinical Decision Support Capabilities of Commercially-available Clinical Information Systems

BackgroundThe most effective decision support systems are integrated with clinical information systems, such as inpatient and outpatient electronic health records (EHRs) and computerized provider order entry (CPOE) systems. PurposeThe goal of this project was to describe and quantify the results of a study of decision support capabilities in Certification Commission for Health Information Technology (CCHIT) certified electronic health record systems. MethodsThe authors conducted a series of interviews with representatives of nine commercially available clinical information systems, evaluating their capabilities against 42 different clinical decision support features. ResultsSix of the nine reviewed systems offered all the applicable event-driven, action-oriented, real-time clinical decision support triggers required for initiating clinical decision support interventions. Five of the nine systems could access all the patient-specific data items identified as necessary. Six of the nine systems supported all the intervention types identified as necessary to allow clinical information systems to tailor their interventions based on the severity of the clinical situation and the user's workflow. Only one system supported all the offered choices identified as key to allowing physicians to take action directly from within the alert. DiscussionThe principal finding relates to system-by-system variability. The best system in our analysis had only a single missing feature (from 42 total) while the worst had eighteen.This dramatic variability in CDS capability among commercially available systems was unexpected and is a cause for concern. ConclusionsThese findings have implications for four distinct constituencies: purchasers of clinical information systems, developers of clinical decision support, vendors of clinical information systems and certification bodies.

Computerised Pill Counting – how will technology support research into medication adherence?

Computerised Pill Counting – how will technology support research into medication adherence?

Information technology–based approaches to reducing repeat drug exposure in patients with known drug allergies

There is increasing interest internationally in ways of reducing the high disease burden resulting from errors in medicine management. Repeat exposure to drugs to which patients have a known allergy has been a repeatedly identified error, often with disastrous consequences. Drug allergies are immunologically mediated reactions that are characterized by specificity and recurrence on reexposure. These repeat reactions should therefore be preventable. We argue that there is insufficient attention being paid to studying and implementing system-based approaches to reducing the risk of such accidental reexposure. Drawing on recent and ongoing research, we discuss a number of information technology-based interventions that can be used to reduce the risk of recurrent exposure. Proven to be effective in this respect are interventions that provide real-time clinical decision support; also promising are interventions aiming to enhance patient recognition, such as bar coding, radiofrequency identification, and biometric technologies.

Potential Impact of Advanced Clinical Information Technology on Cancer Care in 2015

New clinical information technologies now sporadically available will soon be in routine clinical use, bringing many changes to all phases of the cancer care continuum. For example, new technologies such as: (1) The next generation Internet; (2) Real-time clinical decision support systems; (3) Off-line, population-based systems; (4) Large, integrated, individual patient-level phenotypic and genotypic databases with intelligent data mining capabilities; (5) Wireless, invasive and non-invasive physiologic monitoring devices; (6) Natural Language Processing (NLP) systems; and (7) Mathematical models of complex biological systems all have the potential to impact significantly the provision of cancer care throughout its continuum. While new information management and communication techniques and technologies will reduce many of the inefficiencies and inaccuracies of our present systems, there will be an equal, and potentially far more dangerous, set of unintended consequences. Informatics investigators, cancer specialists, and health system administrators must focus on the study of what is working and what is not, as well as, on development and testing of the new clinical information management and communication technologies, if we are to be ready for the future.

Improved operational efficiency through elimination of waste and redundancy

Improved operational efficiency through elimination of waste and redundancy

10 real-time alerts!

A computerized patient record system that provides real-time clinical decision support lets you set rules for clinical alerts. Learn the 10 ways you can make the system work for you.

The measurement of quality of care in the Veterans Health Administration.

The Veterans Health Administration (VHA) is committed to continual refinement of its system of quality measurement. The VHA organizational structure for quality measurement has three levels. At the national level, the Associate Chief Medical Director for Quality Management provides leadership, sets policy, furnishes measurement tools, develops and distributes measures of quality, and delivers educational programs. At the intermediate level, VHA has four regional offices with staff responsible for reviewing risk management data, investigating quality problems, and ensuring compliance with accreditation requirements. At the hospital level, staff reporting directly to the chief of staff or the hospital director are responsible for implementing VHA quality management policy. The Veterans Health Administration's philosophy of quality measurement recognizes the agency's moral imperative to provide America's veterans with care that meets accepted standards. Because the repair of faulty systems is more efficient than the identification of poor performers, VHA has integrated the techniques of total quality into a multifaceted improvement program that also includes the accreditation program and traditional quality assurance activities. VHA monitors its performance by maintaining adverse incident databases, conducting patient satisfaction surveys, contracting for external peer review of 50,000 records per year, and comparing process and outcome rates internally and when possible with external benchmarks. The near-term objectives of VHA include providing medical centers with a quality matrix that will permit local development of quality indicators, construction of a report card for VHA's customers, and implementing the Malcolm W. Baldrige system for quality improvement as the road map for systemwide continuous improvement. Other goals include providing greater access to data, creating a patient-centered database, providing real-time clinical decision support, and expanding the databases.