Yearb Med Inform 2017; 26(01): 125-132
DOI: 10.15265/IY-2017-012
Section 5: Decision Support
Survey
Georg Thieme Verlag KG Stuttgart

Advances in Clinical Decision Support: Highlights of Practice and the Literature 2015-2016

R. A. Jenders
1  Center for Biomedical Informatics and Department of Medicine, Charles Drew University, Los Angeles, California, USA
2  Clinical and Translational Science Institute and Department of Medicine, University of California, Los Angeles, California, USA
› Author Affiliations
Further Information

Correspondence to:

Robert A. Jenders, MD, MS, FACP, FACMI
Center for Biomedical Informatics
Charles Drew University
University of California
Los Angeles
1731 E 120th Street
Los Angeles
CA 90059 USA

Publication History

18 August 2017

Publication Date:
11 September 2017 (online)

 

Summary

Introduction: Advances in clinical decision support (CDS) continue to evolve to support the goals of clinicians, policymakers, patients and professional organizations to improve clinical practice, patient safety, and the quality of care.

Objectives: Identify key thematic areas or foci in research and practice involving clinical decision support during the 2015-2016 time period.

Methods: Thematic analysis consistent with a grounded theory approach was applied in a targeted review of journal publications, the proceedings of key scientific conferences as well as activities in standards development organizations in order to identify the key themes underlying work related to CDS.

Results: Ten key thematic areas were identified, including: 1) an emphasis on knowledge representation, with a focus on clinical practice guidelines; 2) various aspects of precision medicine, including the use of sensor and genomic data as well as big data; 3) efforts in quality improvement; 4) innovative uses of computer-based provider order entry (CPOE) systems, including relevant data displays; 5) expansion of CDS in various clinical settings; 6) patient-directed CDS; 7) understanding the potential negative impact of CDS; 8) obtaining structured data to drive CDS interventions; 9) the use of diagnostic decision support; and 10) the development and use of standards for CDS.

Conclusions: Active research and practice in 2015-2016 continue to underscore the importance and broad utility of CDS for effecting change and improving the quality and outcome of clinical care.


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Introduction

Clinical decision support (CDS) has been defined as a process for enhancing health-related decisions and actions with pertinent, organized clinical knowledge, and patient information to improve health and health care delivery [[1]]. This broad process has been characterized as improving outcomes by addressing the five “rights”: delivering the right, evidence-based information to the right people, including clinicians and patients, in the right format through the right channels at right times [[2]]. Indeed, CDS is not simply an alert, reminder, or explicit care suggestion, but instead it encompasses a variety of tools, including alerts and reminders for patients and physicians, clinical guidelines, order sets, focused patient data reports, documentation templates, diagnostic support, and contextually relevant reference information [[3]]. Much of the activity in this area is driven by market and regulatory contexts with a resulting emphasis on using health information technology for quality improvement. This has included the previously implemented regulatory framework and incentive scheme of meaningful use in the USA. More specifically in this context, CDS has been defined as health information technology functionality that builds upon the foundation of an electronic health record system to provide persons involved in care processes with general and person-specific information, intelligently filtered and organized, at appropriate times, to enhance health and health care [[4]]. Clinical decision support has been shown to improve the process of care by improving health care provider performance, although its demonstrated effect on clinical outcomes has been much more limited, albeit in part because of methodological limitations in studies of CDS [[5]].

With this very broad context, CDS continued to play a significant and expanding role in health care, health science, and the practice of clinical informatics during the period 2015–2016. No single unifying theme unites this body of work, but a number of diverse themes run through it. In addition to quality improvement, researchers continue to address the challenges of big data in an era of precision medicine, including the provision of tools to help patients and clinicians make good decisions using large amounts of possibly unfamiliar data [[6]]. Continued expansion of this technology to different clinical contexts, such as emergency medicine, is another hallmark of activity in CDS [[7]]. Despite efforts over many years, researchers also continue to seek ways to implement clinical practice guidelines using health information technology, including expanding use of mobile technology [[8]].

In light of these myriad factors, motivations and incentives, and following the tradition of the IMIA Yearbook, the principal objective of the present article is to provide a survey of the most recent health science literature and the practice of clinical informatics with regard to CDS. Considering the past two years, 2015–2016, it aims at highlighting key themes in the field of CDS. This includes scientific research, clinical practice, and development of health information technology (HIT) standards to facilitate this activity. Ultimately, these diverse efforts will expand the boundaries of this technology in ways that will improve research, clinical practice, and health outcomes.


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Methods

Key sources of evidence related to CDS activity were reviewed, with a focus on the period 2015–2016. These included the proceedings of the World Congress of Medical Informatics (Medinfo 2015) and those of the annual symposium of American Medical Informatics Association (AMIA 2016) as premier forums for the presentation of contemporary, cutting edge work. MEDLINE references with a date of publication in the 2015–2016 time frame matching a text word search of “clinical decision support” were also reviewed with a focus on a publication type of review. In addition, standardization activities of Health Level Seven International (HL7), the premier standards development organization in the HIT space, were reviewed as a representation of the role HIT standards can play in CDS. Consistent with a grounded theory approach to the analysis of qualitative, narrative data [[9]], key codes or concepts in the text of publications from these sources that described the activity of CDS or its outcome were identified. These concepts then were grouped based on semantic similarity into broad categories in order to generate the CDS themes presented here. For example, different papers could contain concepts such as “mobile sensor data use”, “bedside instrumentation”, and “error reduction”, and these could be grouped in thematic analysis into the theme “precision medicine”. This approach facilitated aggregation of diverse subjects into key themes that help to identify the state of the art and science of CDS in recent years and to help forecast the direction of the field in the near future. To concretize and elaborate on each of these themes, a selected, small number of illustrations from the literature and practice are provided.


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Results

Like any aspect of health care delivery, research and practice, the development and use of CDS respond to its environment. In particular, this includes regulatory regimes established by governments, financial incentives, and the standards of professional societies and other stakeholder organizations. These influences, such as the US Medicare and Children’s Health Insurance Program Authorization Act of 2015 (MACRA) and its effect on efforts related to the drive for meaningful use of electronic health record systems in the USA and for quality improvement [[10]], can shape the use of CDS through the use of financial and regulatory incentives. Other regulatory efforts, both in North America and the European Union, focusing on decision support software, including that incorporated in or classified as medical devices, can stimulate or otherwise may affect work in CDS [[11]]. Further, directed research funding, such as that provided by the European Union for the Platform for European Preparedness Against Re-emerging Epidemics (PREPARE) that helps to promote a learning health system that merges clinical trials and decision support into a continuous learning process, also can shape the practice of CDS [[12]].

These environmental factors in turn affect the practice of CDS and the body of research published about CDS. Taking these effects into account, workers have defined models that delineate the key types of CDS systems and interventions that have emerged as a response [[13], [14]]. Rather than duplicate these models, the present survey of recent works in CDS identifies ten thematic areas that involve CDS systems or fortify the infrastructure necessary for them to be used to peak potential ([Table 1]). Certainly these thematic areas can be aggregated in a variety of ways, and individual projects may pertain to more than one of these thematic areas. Nevertheless, the large diversity of work in CDS in the recent past confirms it as an important domain for research and practice.

Table 1

Summary of the 10 key thematic areas of clinical decision support (CDS) work during the period 2015-2016 (HIT: health information technology; CPOE: computer-based provider order entry).

Theme

Summary

Knowledge Representation

Structuring knowledge for processing by computer-based decision support systems

Precision Medicine

Using large data sets, including genomic and sensor data, to provide highly tailored CDS

Quality Improvement

Enhancing the safety and outcomes of clinical care

CPOE Systems

Providing CDS at the time that clinicians write orders regarding diagnostic evaluation or treatment

Expansion of Clinical Settings

Using CDS in care settings such as the emergency department and in domains less emphasized in the past such as anesthesiology and nursing

Patient-Directed CDS

Exposing patients to CDS instead of, or in addition to, the more traditional clinician target

Negative Impact of CDS

Identifying ways that CDS can adversely affect clinical care and outcomes

Structuring Data for CDS

Coding data and employing controlled terminologies to make data more usable in CDS

Diagnostic Decision Support

Using CDS to improve the accuracy of diagnosis

HIT Standards for CDS

Providing standard interfaces, data models, and inference mechanisms to ease the implementation of CDS


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Knowledge Representation

Much activity in CDS continues to center on the exploration of ways to use the knowledge compiled in clinical practice guidelines in order to enhance adherence to them and thereby improve the quality of the process and outcomes of health care. This includes efforts to structure guidelines and improve their computability, as well as more generally how to manage scientific and expert knowledge for use by clinicians and patients.

For example, the treatment of asthma continues to be a clinical domain where clinical practice does not meet evidence-based guidelines. The ARIA international effort has led to the use of CDS technology to improve care of patients with asthma or rhinitis [[8]]. The prevention of venous thromboembolism remains a vexing clinical challenge, yet multidisciplinary efforts that leverage CDS, including synchronous alerting and order sets, have made progress in this area [[15]]. Some CDS efforts with guidelines have involved their use in somewhat non-traditional settings, such as educational efforts that have improved process outcomes in pharmacy practice [[16]]. A key part of implementing computable guidelines concerns identifying where, at any given moment, a particular patient is on the clinical pathway endorsed by a guideline, and recent work in modeling clinical trajectories has improved our knowledge in this regard [[17]]. A key aspect of knowledge management is reusing computable knowledge, and work at Vanderbilt University, inter alia, is helping to elaborate ways to curate and reuse knowledge in CDS systems [[18]]. Even independent of the use of HIT, checklists have emerged as powerful instruments in health care and other industries, such as aviation, and recent work in CDS that facilitates the use of checklists to improve performance in critical care situations highlights this trend [[19]].

While the use of guidelines as part of knowledge representation has been important in recent work, other approaches have been employed. Work that combines different formalisms, such as CDS that can improve the ability of practitioners to diagnose cancer by combining case-based reasoning with a rule-based approach, offers promise [[20]]. Ontology-based CDS, such as a system to help manage patients with numerous chronic problems that potentially can involve multiple guidelines, continues to be used [[21]]. In addition, automated identification of temporal aspects of care pathways can help improve their use [[22]]. Implementation of guidelines in a way that uses dynamic adjustment according to a patient’s own laboratory data in order to detect potentially dangerous states addressed by guidelines may enhance patient safety [[23]]. Knowledge-based interventions using novel approaches that leverage other areas, such as cross-fertilization involving more traditional audit and feedback, holds promise [[24]]. Because competing guidelines addressing the same clinical topic can produce different recommendations, work that addresses the synthesis of such CDS output, including a framework for the fusion of treatment recommendations, is important [[25]]. Finally, recognizing that while CDS may improve guideline adherence, it is not a panacea, and work to understand the reasons why clinicians do not accept CDS-generated recommendations, such as those regarding the management of breast cancer patients, is important [[26]].


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Precision Medicine

Analytics involving the use of “big data”, such as genomic and proteomic data as well as ambulatory and related sensor data, in conjunction with CDS in order to try to personalize clinical care and thereby improve its effectiveness, was a key area of recent work in CDS. The use of large collections of clinical and laboratory data for forecasting that can feed into CDS efforts shows promise, such as predicting treatment responses in radiation oncology [[27]]. The establishment of learning health systems, which are structured in part to acquire knowledge from clinical practice and experimentation that can be fed back into CDS efforts to improve care, is a growing phenomenon, and the resulting massive data sets that emerge from such collaboration can be used to improve care in areas such as pediatric surgery [[28]]. The use of sensor data and mobile technology to support CDS interventions, including self-management thereof, may provide value in a world of seemingly ubiquitous mobile devices [[29]]. Integration of bedside instrumentation with a CDS system has been shown to reduce charting time, increase time spent on direct care, reduce errors, and improve clinical outcomes [[30]].

In addition, use of particular techniques to increase the utility of big data has shown interesting results, such as the employment of an artificial neural network and deep-learning techniques to predict inpatient mortality and potential benefit from clinical interventions [[31]]. Identification of high risk for complications from heart failure using large data sets also offers a pathway for CDS that can improve outcomes [[32]]. Using pharmacogenomic data to drive CDS in ways that can personalize care, such as efforts in the eMERGE Network, highlights the important interaction between precision medicine and CDS [[33]]. Leveraging big data to explore interactions among clinical and genetic factors using Bayesian networks to classify manifestations of inflammatory bowel disease illustrates other possibilities of CDS in the realm of precision medicine [[34]]. Leveraging the promise of the Internet of Things for acquiring data and providing information through the design of a reference architecture that can be used to implement CDS illustrates the potential in this area [[35]].


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Quality Improvement

A key promise of CDS is the potential for improving the process of clinical care and its health outcomes. This flows in part from work designed to detect abnormal patient states or events and to communicate the need for action to decision-makers. For example, work to detect and even predict adverse drug reactions can help to improve quality by reducing the negative consequences of clinical care [[36]]. Automated techniques for knowledge discovery that can inform CDS work to improve medication safety also emphasize quality improvement [[37]]. Additional work in this area shows how the use of patient data coupled with CDS that detects early clinical deterioration in, for example, the pediatric inpatient setting, including the use of patient dashboards to inform treatment that intervenes before serious deterioration, may improve the quality of care [[38]].


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Computer-based Provider Order Entry

Computer-based provider order entry (CPOE) is a key technology that offers considerable promise to influence decisions, change practice, and improve clinical care, through the integration of CDS, patient data, and clinical care management. By bringing together the clinician, relevant patient data, and other forms of CDS at the time that decisions are being made about care in a computer-based environment, as substantiated by recent work, CPOE systems can improve care. For example, even if empiric validation may be limited, the use of CPOE technology in a pediatric electronic health record system may be valuable for care quality [[39]]. In an example that flows from recent regulatory changes in the USA to emphasize guideline-appropriate ordering of imaging studies, work has demonstrated that such technology can increase appropriate use of imaging studies by a moderate amount and decrease inappropriate use by a small amount [[40]]. Additional evidence suggests that the use of this technology can exert a moderate effect on improving antibiotic prescribing in the ambulatory environment [[41]].

On the other hand, evidence has arisen suggesting that the commercial market for CPOE systems, such as that in England, is at an immature stage of development that does not meet user needs even as it is further hampered by unrealistic adopter demands [[42]]. These observations were derived from semi-structured interviews with and ethnographic observations of system users, and they substantiated complaints that overly generic configurations are not sufficiently specialized for local user needs. However, these data also revealed that users may not be aware of the characteristics and limitations of packaged systems and that this lack of awareness may lead to unrealistic expectations that in turn could lead to system dissatisfaction [[42]]. Moreover, even systems that are certified under regulatory schemes may lack the capability to implement certain kinds of decision support rules [[43]]. Indeed, there is increasing recognition that CPOE and related technology can malfunction in ways that cause problems, which poses at least a potential threat to patient safety [[44]]. Nevertheless, there is recognition that an emphasis on user interface design issues may hold the key to improving the utility of this technology, such as simulation work with potential users that can help identify potential failure points [[45]].


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Expansion of Clinical Settings

In the recent past, workers continued to expand the domains, settings, and targets of CDS in ways that illustrate its value, including different clinical disciplines and the use of CDS in clinical research. For example, as a specialized environment characterized by high volume of cases, a wide range of clinical presentations, significant time-pressure, and high severity, the emergency department particularly could benefit from CDS. A recent review documents that approximately half of 23 selected studies demonstrated a positive effect of CDS on care in this setting [[7]]. CDS has demonstrated benefits for patient care in anesthesiology via point-of-care technology, particularly in ordering antibiotic prophylaxis, and in the use of clinical documentation [[46]]. While the origins of CDS can be traced back to physician-directed support, more recent work has highlighted its impact among other groups of clinicians. Indeed, in a recent review examining the impact of CDS on nurses, all the studies that included process outcomes and usability outcomes, and also had analytic procedures to detect changes in outcomes, demonstrated statistically significant improvements [[47]].

In another work focusing on different domains, recent evidence has shown that, despite sometimes ineffective outcomes that can result in part from low use of CDS functionality, use of CDS to provide diabetes care can be improved by prioritizing care recommendations, improving communication of treatment-relevant information to patients, using such systems for care coordination and case management, and integrating patient-reported information and data from remote devices [[48]]. In other settings, CDS continues to hold promise for improving public health through disease surveillance and helping to intervene against outbreaks, including recent work to model spread of the Zika virus in dynamic human and vector populations [[49]]. Expansion of the use of CDS in psychiatry has produced interesting results that appear to improve on current clinical practice on predicting the development of post-traumatic stress disorder after traumatic events [[50]] and in predicting suicide [[51]]. Applying CDS in the realm of clinical research requires not just appropriate data access but also methods for processing and interpreting research protocols and informed consent documentation. Work in natural language processing identifies ways to structure such knowledge for potential use in CDS [[52]]. Work in matching patients to potential clinical trials remains challenging; recent efforts involving a semantic web approach yielded high recall but relatively low precision, mainly due to missing data in the patient record [[53]]. Nevertheless, despite all this potential in these expanded domains of CDS application, the effect of CDS on patient-reported outcomes may be only marginally positive [[54]].


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Patient-Directed Clinical Decision Support

While traditionally intended to help improve decision-making by clinicians caring for patients, CDS has been increasingly developed for patients themselves as the individuals with the most at stake in any given health care decision and as the final common pathway for effecting many treatments, such as medication administration and lifestyle modifications. For example, workers are identifying uses of mobile technology to help train adolescents, who comprise a disproportionate share of those at risk for anaphylaxis, about the appropriate use of epinephrine auto-injection [[55]].

Initiatives that target patients for decision support use methods taking into account factors such as suitability for diverse patient populations, including work to develop a patient-directed decision aid for those with heart failure who may need a left ventricular assist device, which effort required 19 iterations to produce suitable written and video decision support material for patients [[56]]. Indeed, in additional evidence pertaining to educating patients about oncologic disease, researchers who developed computer-based decision support for patients with thyroid cancer discovered only a moderately positive correlation of knowledge acquisition with CDS use [[57]], suggesting the need for additional work in this important thematic area. Future work to fold shared-decision making between clinicians and patients into CDS, such as implementation of the direct decision support model being promoted by the US Centers for Medicare and Medicaid Services (CMS), likely will help address these challenges [[58]].


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Negative Impact of Clinical Decision Support

In addition to the many benefits of HIT, there is growing recognition that use of HIT in general and of CDS in particular could result in negative impacts on patients, including direct harm. For example, in a recent review that examined the causes of medication prescribing errors in the use of CPOE systems [[59]], workers identified CDS as one of the causes of such errors. In addition, the literature in this thematic area has described the challenge of alert fatigue with CPOE and other systems that use synchronous alerts to implement CDS. One aspect of this are the high override rates for some alerts in some systems, such as an override rate as high as 89% for opioid drug-allergy alerts [[60]]. This has progressed to the point where workers more recently have developed efforts to address this problem. For example, researchers at Dalhousie University in Nova Scotia have created the INITIATE framework to enable intelligent adaption to the output of alert-generating systems in a way that could help alleviate alert fatigue [[61]]. Additional work to reduce alert fatigue related to drug-drug interaction alerts, including targeted alert reduction as measured by “think time”—a metric for evaluating the resolution time for an alert—managed to reduce the alert burden overall as well as improve prescriber efficiency [[62]]. Accordingly, while evidence demonstrates that HIT and CDS can have a negative impact, recent work also has shown pathways for addressing such an impact.


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Structuring Data

Computer-based CDS essentially relies on the application of knowledge to data in digital format in order to improve decision-making. While sometimes not directly related to CDS, attempts to improve the availability of structured data, including representation using data models and controlled terminologies, can be vital in the accuracy and ultimate success of CDS. For example, workers at Partners Healthcare in Boston have shown how their strong efforts over a decade to derive structured data from clinical documentation can serve as the building blocks for leveraging data to improve care, including via CDS [[63]]. Other work has shown how training techniques can be used to improve the performance of natural language parsers with regard to semantic role labeling, which can help improve the structure of narrative data for use in activities such as CDS [[64]]. Structuring complex data such as microbiology reports can help improve their utility for secondary uses, including CDS [[65]]. The use of a reference terminology in this context, such as SNOMED CT, can help evaluate terminology coverage in a CDS system and possibly improve its recognition of data in a patient’s electronic health record [[66]]. In large health care organizations, a systematic approach, such as the ten-step process employed at Partners Healthcare, can assist greatly in the provision of structured data for CDS and other purposes [[67]].


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Diagnostic Clinical Decision Support

As an important case of secondary use for structured clinical data, diagnostic decision support dates to the earliest days of the academic endeavor that later became biomedical informatics. Recent work continues to emphasize the importance of assisting clinicians and patients in the early and accurate diagnosis of disease so that curative interventions can be undertaken. For example, at least observational evidence suggests that expedited diagnosis of symptomatic cancer may provide benefits, and initiatives to accomplish this in the primary care setting similarly may be beneficial [[68]]. Beyond structured clinical data, approaches for improving diagnosis using image data, such as photographs of the ocular fundus, may result in better care [[69]]. In another effort to prevent harm by improved diagnostic detection, the use of a multi-algorithm approach to detecting patient-ventilator asynchronies that can result in lung injury can improve detection in ventilator wave form data from 66% to as high as 95% [[70]]. A variety of score calculators to identify risks for and probability of presence of various diseases already exist, but a key challenge remains to integrate them with electronic health record systems. Recent work has demonstrated that, even with advanced techniques for variable extraction, only 29% of 171 such scores are completely programmable, while 63% are at least 75% programmable, suggesting that such integration holds both promises and challenges [[71]].


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Health Information Technology Standards for Clinical Decision Support

Research has demonstrated that HIT standards have a positive role to play to get the structured data needed for CDS, such as facilitating data exchange in clinical research using the CDISC Operational Data Model (ODM) to support exchange of research protocols and case reports [[72]]. In addition, HIT standards play a role in the structuring of and access to knowledge that can form the basis of CDS interventions. Indeed, the presence of numerous standards in this space may interfere with the semantic interoperability needed to share computable knowledge among CDS systems [[73]]. In addressing these challenges, some workers have focused on improving standardization of data representation within CDS knowledge formalisms, such as the potential use of the HL7 standard Fast Healthcare Interoperability Resources (FHIR) as a possible standard data model in the Arden Syntax [[74]].

Sidestepping the challenge of agreeing a standard formalism that allows computable knowledge to be shared, some workers have emphasized standards that allow the integration of CDS applications with electronic health record systems, such as the increasingly popular CDS Hooks protocol [[75]]. Other workers have incorporated support for sophisticated reasoning techniques in CDS knowledge formalisms, such as the incorporation of fuzzy logic in the Arden Syntax in order to mimic clinical reasoning regarding infection surveillance [[76]]. Additional work in standards has demonstrated the significant prevalence of fuzzy logic in clinical reasoning and shown how a standard such as the Arden Syntax that incorporates constructs for fuzzy logic can be used to represent the knowledge in a large corpus of quality measures [[77]]. Widespread agreement on a standard CDS knowledge formalism such as the Arden Syntax remains elusive even if this approach has demonstrated utility in implementing CDS [[78]]. Standards development organizations such as HL7 seek to address the utility of such standards not only for knowledge sharing but also for knowledge access, such as improvements in the Infobutton standard, a popular method for accessing knowledge, e.g. genomic resources, within electronic health record systems [[79]].


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Discussion

Overall, the current work is a thematic, selective survey of recent activity in CDS covering a recent time period that aligns with the timeline and requirements of the IMIA Yearbook. It is a characterization of the kinds of recent activities in CDS, particularly research, in order to provide an overview of this field. Differing from more elaborate recent studies [[80]], it is not a top-down, comprehensive systematic review addressing all possible facets of CDS. Instead, this survey elaborates key themes in research and practice involving CDS during this time period derived from the narrative text of relevant publications. Moreover, while illustrative examples of these themes are described, space does not allow inclusion of every single study or effort relevant to the identified themes. The overall objective was to convey the breadth of activity in the important domain of CDS. As manifested by the illustrative examples in the ten identified thematic areas, our knowledge of appropriate structure, implementation, utility, and effective CDS continues to grow. Indeed, the illustrated effects, as well as actual and potential utility of CDS, offer potentially pervasive effects on clinical practice and health care delivery, making a review of recent work all the more timely.

Acting on this imperative, other workers have conducted reviews of research and practice in CDS but elaborated somewhat different classifications. For example, in their comprehensive quarter-century review of CDS published in 2016, Middleton and colleagues identified a framework through iterative discussion among themselves that framed their informal review of CDS around six axes: data, knowledge, inference, architecture and technology, implementation and integration, and users [[80]]. A decade earlier, Peleg and Tu, focusing in part on knowledge representation and management in the context of CDS, framed their analysis of CDS using a life-cycle approach [[81]]. Their thematic areas included the goals of CDS, CDS systems as part of a knowledge management enterprise, modeling of knowledge to enable knowledge representation, design features that foster success of CDS systems, standardization efforts, and evaluation of CDS.

The present analysis, which covers a more limited time frame than the above-mentioned studies, differs in methodology and offers both contrasts with and similarities to these prior classifications. All three analyses identify an emphasis on knowledge structure and representation in CDS in research and practice in this area. Further, while aggregating the themes somewhat differently, all three cover the importance of standards and evaluation, although the present analysis does this in part through the theme of the negative impact of CDS. By contrast, because it is somewhat more granular than the earlier studies, the present elaboration of CDS themes suggests the emphasis on specific uses of CDS, such as quality improvement and the use of CDS to help practice precision medicine.

One point brought forth by comparing different classifications of CDS activity is that overlap may occur between classification schemes and within any particular example used to illustrate them. For example, a single study may use a standard knowledge formalism to provide the computable knowledge that then, implemented in a CDS system, is used to improve clinical outcomes. Such a study could be classified in at least three themes identified in the present classification: knowledge representation, quality improvement, and HIT standards. Nevertheless, the key purpose in the present analysis was to identify key kinds of CDS activity even if every single published work could not be discretely classified into a single theme.

Despite the progress illustrated by the examples of each of the CDS themes, barriers and limitations continue to affect the practice of CDS. Financial incentives may not be fully aligned to promote quality over volume in a way that could increase uptake of CDS; despite considerable efforts to contrary, lack of widespread use of knowledge representation and terminology standards makes knowledge sharing and data use in CDS difficult; and re-engineering is not infrequently needed to implement knowledge bases at different sites [[80]]. Moreover, despite recommendations to the contrary, adequate governance, knowledge maintenance, and feedback from evaluation may not be in place to make ideal use of CDS [[1]].

Regardless of the varying ways CDS is being implemented and the barriers to or limitations on its use, the application of knowledge remains central, and the recent work highlighted here addresses both parts of this milieu. Knowledge is addressed through the development of HIT standards for representing, sharing, and accessing it; by leveraging clinical practice guidelines to incorporate evidence-based practice recommendations in CDS; by improvements in support for diagnostic activity; and by the expansion into different clinical domains and applications, such as clinical research and nursing. The role of data in CDS is addressed by increasing big data to facilitate precision medicine and by structuring data, including the use of standard data models and terminologies. The overall utility of CDS has been demonstrated by work in quality improvement and by research in patient-directed decision support. While much of this work shows promises for achieving the benefits attributed to CDS, including improvements in the process and, to a more limited extent, clinical outcomes of health care, recent work also demonstrates that CDS is not a panacea and that care must be taken to minimize its adverse consequences as well.


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Conclusions

Recent work in CDS has demonstrated both potential and pitfalls in improving the process and outcomes of clinical care. While several models have been identified for classifying CDS and CDS systems, recent work in CDS can be aggregated in ten thematic areas. The breadth and depth of research and practice in CDS in recent years substantiate its importance and offer promise for still more advances in the future.


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Acknowledgements

This work was supported in part by NIMHD grants U54MD007598 and S21MD000103 and NCATS grant UL1TR001881 from the National Institutes of Health (USA).


Correspondence to:

Robert A. Jenders, MD, MS, FACP, FACMI
Center for Biomedical Informatics
Charles Drew University
University of California
Los Angeles
1731 E 120th Street
Los Angeles
CA 90059 USA