021-0018.pdf

https://www.ijhsr.org/IJHSR_Vol.8_Issue.5_May2018/IJHSR_Abstract.044.html, 2018

The inculcation of information technology into healthcare delivery has impacted all arenas from population health to financial and administrative management. It has proven to be a valuable tool in today's very complicated and diverse health environment. This paper explores various publications (Research, Opinions, and Articles) on the impact of decision systems on different aspects of healthcare, especially patient safety. Furthermore, it goes into details of how data inculcation in support systems can impact health processes in reducing the cost associated with care provision in the United States. The papers evaluated in this paper all provide different perspectives on how CDSS are changing healthcare and how to utilize them effectively and efficiently in achieving organizational goals. In a recent article published in Harvard Business Review on the impact of Big Data on healthcare, 63 percent of industry professionals have seen some effect on population health and 60 percent believe it has one way or another impacted preventive care. These valuable data assimilation with CDSS would have a positive impact on outcome research. As more technology are incorporated into healthcare activities and processes, the potential for changes in approach and benefit will continue to evolve.

Implementation science : IS, 2011

The use of computerized clinical decision support systems (CCDSSs) may improve chronic disease management, which requires recurrent visits to multiple health professionals, ongoing disease and treatment monitoring, and patient behavior modification. The objective of this review was to determine if CCDSSs improve the processes of chronic care (such as diagnosis, treatment, and monitoring of disease) and associated patient outcomes (such as effects on biomarkers and clinical exacerbations). We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews database, Inspec, and reference lists for potentially eligible articles published up to January 2010. We included randomized controlled trials that compared the use of CCDSSs to usual practice or non-CCDSS controls. Trials were eligible if at least one component of the CCDSS was designed to support chronic disease management. We considered studies 'positive' if they sh...

Jordan Medical …, 2010

Computerized Clinical Decision Support Systems (CDSSs) are defined as computer-based tools using scientific knowledge to generate patient specific advice or interpretation to help health professionals in making clinical decisions. The use of computers has been driven not only by the increasing need to manage large amounts of information, but also by the imperative to make evidence-based and costeffective decisions on a daily basis. Computer aided medical tools address the growing information needs of the busy health care provider, decrease medical errors and improve healthcare processes as well as patient outcomes. So this paper aimed at presenting different issues related to application, benefits and recent advances of CDSSs within the health care delivery system.

Computer Methods and Programs in Biomedicine, 2020

A Clinical Decision Support System (CDSS) aims to assist physicians, nurses and other professionals in decision-making related to the patient's clinical condition. CDSSs deal with pertinent and critical data, and special care should be taken in their design to ensure the development of usable, secure and reliable tools. Objective: This paper aims to investigate existing literature dealing with the development process of CDSSs for monitoring chronic diseases, analysing their functionalities and characteristics, and the software engineering representation in their design. Methods: A systematic literature review (SLR) is conducted to analyse the literature on CDSSs for monitoring chronic diseases and the application of software engineering techniques in their design. Results: Fourteen included studies revealed that the most addressed disease was diabetes (42.8%) and the most commonly proposed approach was diagnostic (85.7%). Regarding data sources, the studies show a predominance on the use of databases (85.7%), with other data sources such as sensors (42.8%) and self-report (28.6%) also being considered. Analysing the representation for engineering techniques, we found Behaviour diagrams (42.8%) to be the most frequent, closely followed by Structural diagrams (35.7%) and others (78.6%) being largely mentioned. Some studies also approached the requirement specification (21.4%). The most common target evaluation was the performance of the system (64.2%) and the most common metric was accuracy (57.1%). Conclusion : We conclude that software engineering, in its completeness, has scarce representation in studies focused on the development of CDSSs for chronic diseases.

JOURNAL OF DECISION SYSTEMS, 2018

In assessing the benefits of using e-health systems, the main goal of this study is to evaluate the real use of the clinical decision support system (CDSS) between 2007 and 2014 in Canada’s healthcare sector. The quantitative method was based on data collected by the National Physician Survey in Canada. Results indicate that 63.8% of healthcare providers were using a CDSS at work in 2014 to help them in the decision-making process, a sixfold improvement since 2007. As for usage rate by sex, we found a statistically significant difference between men and women, with women from the Canadian physicians’ group reporting greater CDSS use than men. In all age groups, a higher percentage of younger physicians used a CDSS in their practice. A number of suggestions are put forth to improve technological infrastructure and reduce the gap among age groups, genders and specialties.

Methods of Information in Medicine, 1997

Background: Even though a high demand for sector spanning communication exists, so far no eHealth platform for nephrology is established within Germany. This leads to insufficient communication between medical providers and therefore suboptimal nephrologic care. In addition, Clinical Decision Support Systems have not been used in Nephrology until now. Methods: The aim of NEPHRO-DIGITAL is to create a eHealth platform in the Hannover region that facilitates integrated, cross-sectoral data exchange and includes teleconsultation between outpatient nephrology, primary care, pediatricians and nephrology clinics to reduce communication deficits and prevent data loss, and to enable the creation and implementation of an interoperable clinical decision support system. This system will be based on input data from multiple sources for early identification of patients with cardiovascular comorbidity and progression of renal insufficiency. Especially patients will be able to enter and access their own data. A transfer to a second nephrology center (metropolitan region of Erlangen-Nuremburg) is included in the study to prove feasibility and scalability of the approach. Discussion: A decision support system should lead to earlier therapeutic interventions and thereby improve the prognosis of patients as well as their treatment satisfaction and quality of life. The system will be integrated in the data integration centres of two large German university medicine consortia (HiGHmed (highmed.org) and MIRACUM (miracum.org)). Trial registration: ISRCTN16755335 (09.07.2019).

AMCIS 2006 Proceedings, 2006

Health care in the United States faces an uncertain future with the rising costs of care, the growth of the aging population, the chronic health conditions associated with the aging, and a predicted shortage in the physician workforce, among other issues. Health care costs are rising at an annual rate of 13% to 15%; health insurance premiums continue to rise at an incredible rate as well. The number of persons living with chronic conditions is expected to increase by nearly 20% during the next ten to fifteen years; while at the same time there is an impending shortage of physicians due to a number of factors affecting the physician workforce, including a desire by many young physicians to reduce their workloads.

Implementation Science, 2011

Background Computerized clinical decision support systems (CCDSSs) are claimed to improve processes and outcomes of primary preventive care (PPC), but their effects, safety, and acceptance must be confirmed. We updated our previous systematic reviews of CCDSSs and integrated a knowledge translation approach in the process. The objective was to review randomized controlled trials (RCTs) assessing the effects of CCDSSs for PPC on process of care, patient outcomes, harms, and costs. Methods We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews Database, Inspec, and other databases, as well as reference lists through January 2010. We contacted authors to confirm data or provide additional information. We included RCTs that assessed the effect of a CCDSS for PPC on process of care and patient outcomes compared to care provided without a CCDSS. A study was considered to have a positive effect (i.e., CCDSS showed improve...

Journal of the …, 2009

The 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.

Improving Health Management through Clinical Decision Support Systems, 2000

Clinical Decision Support Systems (CDSS) are software designed to help clinicians to make decisions about patient diagnosis using technical devices such as desktops, laptops and iPads, and mobile devices, to obtain medical information and set up alert systems to monitor medication. A Clinical Decision Support System has been suggested by many as a key to a solution for improving patient safety together with Physician Based Computer Order Entry. This technology could prove to be very important in conditions such as chronic diseases where health outlay is high and where self-efficacy can affect health outcomes. However, the success of CDSS relies on technology, training and ongoing support. This chapter includes a historical overview and practical application of CDSS in medicine, and discusses challenges involved with implementation of such systems. It discusses new frontiers of CDSS and implications of selfmanagement using social computing technologies, in particular in the management of chronic disease.