Clinical Decision Support Capabilities of Commercially-available Clinical Information Systems

Wright, Adam; Sittig, Dean F.; Ash, Joan S.; Sharma, Sapna; Pang, Justine E.; Middleton, Blackford

doi:10.1197/jamia.m3111

Cited by 126 publications

(78 citation statements)

References 24 publications

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“…A survey of the CDSS capabilities of major commercial clinical information systems (CISs) in the USA found that most have small-scale, in-built functionalities -mostly comprised of alerts and reminders -with scant support for more complex decision-making tasks. 26 The reason for the emphasis on simpler functionality is clear: in order for commercial systems to be viable, they need to scale to different contexts and work environments, so providers focus on simpler tasks that are homogeneous across institutions (eg computerised order entry). The development of CDSSs to support more complex decision making (eg prediction of patient states or classifi cation of diseases) is signifi cantly more diffi cult and time consuming, and such efforts have largely remained confi ned to academic environments, where researchers have the advanced computational expertise and time required to create appropriate solutions.…”

Section: Challenges To Implementation and Adoption Of Clinical Decisimentioning

confidence: 99%

Decision time for clinical decision support systems

et al. 2014

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KEYWORDS: Clinical decision support systems, decision-making, diagnosis ContextInformation technology (IT) is now commonplace in almost every branch of healthcare. Electronic health records, eprescribing and digital medical imaging are well known to clinicians and have been implemented with varying degrees of success. 1 In addition, clinicians increasingly make use of online repositories such as PubMed and Google Scholar, 2 and specialised search engines such as FindZebra 3 to help answer clinical questions. One often overlooked set of IT tools are clinical decision support systems (CDSSs), which have been defi ned as systems that 'provide clinicians or patients with computer-generated clinical knowledge and patient-related information, intelligently fi ltered or presented at appropriate times, to enhance patient care'. 4 Such CDSSs have been the subject of academic computer science research for more than ABSTRACT 50 years 5 and offer the potential for better supported decision making by clinicians, improved compliance with medical standards and improved clinical effi ciency and safety. 6,7 Nonetheless, utilisation of CDSSs remains limited, and most healthcare IT systems do not include robust CDSS functions that can be widely employed across organisations, clinical presentations and domains. 8 Some of the challenges to implementation of CDSSs relate to the volume of high-quality data required for state-of-the-art systems, the translation of such data to machine-readable states and the mapping of CDSS processes to fi t with existing clinical workfl ows. As a result, successful implementation of CDSSs has tended to be site and domain specifi c, with major diffi culties replicating these successes more extensively throughout healthcare systems. 9 This is in contrast to commercial fi elds such as fi nance, where decision support technologies have been widely deployed. For example, risk-profi ling tools for fi nancial experts have been developed as easy-to-use programs that can assimilate information and guide users through complex fi nancial information and associated decisions tailored to individual customer needs. Healthcare decision making is signifi cantly more complex than fi nancial planning; however, some of the challenges in both domains are similar: large quantities of data need to be linked, integrated and translated to machine readable formats, and expert knowledge is required to contextualise and apply the data in a meaningful way. We discuss some reasons for the limited dissemination and adoption of CDSSs to date and refl ect on the major barriers that need to be overcome for these useful tools to be adopted more widely.

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Section: Challenges To Implementation and Adoption Of Clinical Decisimentioning

confidence: 99%

Decision time for clinical decision support systems

et al. 2014

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“…The lack of standard metrics for quality of service assessment impedes the evaluation and obscures the progress of technology adoption and utility. 1,5,6 From the patient's perspective, the usability and ease-of-access to technologies are obstructed by the lack of technology integration, interoperability, and standardization. For example, though telecommunication vendors and vendor resources could provide low-cost solutions as data are transmitted through their ubiquitous networks, they might also limit expansion of applications and affordability for cell phone text and data transmission for economic reasons.…”

Section: Major Barriers To Progressmentioning

confidence: 99%

“…A pervasive barrier for these endusers to secure new tools and technologies is the limited financing available for implementation, maintenance, and sustainability. [2][3][4][5][6] From the data perspective, limited access and limited data flow impede progress. Data flow into practice and hospital centers is strictly regulated, making the main data repository's (EHR=EMR systems) difficult to access and utilize for their true value with ongoing data mining and automated decision support.…”

Section: Major Barriers To Progressmentioning

confidence: 99%

Developing Next-Generation Telehealth Tools and Technologies: Patients, Systems, and Data Perspectives

Ackerman

Filart

Burgess

et al. 2010

Telemedicine and e-Health

131

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The major goals of telemedicine today are to develop next-generation telehealth tools and technologies to enhance healthcare delivery to medically underserved populations using telecommunication technology, to increase access to medical specialty services while decreasing healthcare costs, and to provide training of healthcare providers, clinical trainees, and students in health-related fields. Key drivers for these tools and technologies are the need and interest to collaborate among telehealth stakeholders, including patients, patient communities, research funders, researchers, healthcare services providers, professional societies, industry, healthcare management= economists, and healthcare policy makers. In the development, marketing, adoption, and implementation of these tools and technologies, communication, training, cultural sensitivity, and end-user customization are critical pieces to the process. Next-generation tools and technologies are vehicles toward personalized medicine, extending the telemedicine model to include cell phones and Internet-based telecommunications tools for remote and home health management with video assessment, remote bedside monitoring, and patient-specific care tools with event logs, patient electronic profile, and physician note-writing capability. Telehealth is ultimately a system of systems in scale and complexity. To cover the full spectrum of dynamic and evolving needs of end-users, we must appreciate system complexity as telehealth moves toward increasing functionality, integration, interoperability, outreach, and quality of service. Toward that end, our group addressed three overarching questions: (1) What are the highimpact topics? (2) What are the barriers to progress? and (3) What roles can the National Institutes of Health and its various institutes and centers play in fostering the future development of telehealth?

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“…The embedded knowledge component in a CDSS combines patient data and generates meaningful interpretations that aid clinical decision making (Liu et al, 2006). An effective CDSS also summarizes the outcomes, appraises and criticizes the caring plans, assists clinicians in ordering necessary medications or diagnostic tests, and initiates a disease management plan after a specific disease is identified (Colombet et al, 2005;Friedlin et al, 2007;Garg et al, 2005;Wadhwa, 2008;Wright et al, 2009). …”

Section: Characteristics Of Clinical Decision Support Systemsmentioning

confidence: 99%

Clinical Decision Support Systems: An Effective Pathway to Reduce Medical Errors and Improve Patient Safety

Jao¹,

Hier²

2010

Decision Support Systems

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Clinical Decision Support Capabilities of Commercially-available Clinical Information Systems

Abstract: These 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.

Cited by 126 publications

References 24 publications

Decision time for clinical decision support systems

Decision time for clinical decision support systems

Developing Next-Generation Telehealth Tools and Technologies: Patients, Systems, and Data Perspectives

Clinical Decision Support Systems: An Effective Pathway to Reduce Medical Errors and Improve Patient Safety

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