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Towards effective clinical decision support systems: A systematic review

Francini hak, tiago guimarães, manuel santos.

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Competing Interests: The authors have declared that no competing interests exist.

* E-mail: [email protected]

Received 2020 Dec 6; Accepted 2022 Jul 27; Collection date 2022.

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Clinical Decision Support Systems (CDSS) are used to assist the decision-making process in the healthcare field. Developing an effective CDSS is an arduous task that can take advantage from prior assessment of the most promising theories, techniques and methods used at the present time.

To identify the features of Clinical Decision Support Systems and provide an analysis of their effectiveness. Thus, two research questions were formulated: RQ1—What are the most common trend characteristics in a CDSS? RQ2—What is the maturity level of the CDSS based on the decision-making theory proposed by Simon?

AIS e-library, Decision Support Systems journal, Nature, PlosOne and PubMed were selected as information sources to conduct this systematic literature review. Studies from 2000 to 2020 were chosen covering search terms in CDSS, selected according to defined eligibility criteria. The data were extracted and managed in a worksheet, based on the defined criteria. PRISMA statements were used to report the systematic review.

The outcomes showed that rule-based module was the most used approach regarding knowledge management and representation. The most common technological feature adopted by the CDSS were the recommendations and suggestions. 19,23% of studies adopt the type of system as a web-based application, and 51,92% are standalone CDSS. Temporal evolution was also possible to visualize. This study contributed to the development of a Maturity Staging Model, where it was possible to verify that most CDSS do not exceed level 2 of maturity.

The trend characteristics addressed in the revised CDSS were identified, compared to the four predefined groups. A maturity stage model was developed based on Simon’s decision-making theory, allowing to assess the level of maturity of the most common features of the CDSS. With the application of the model, it was noticed that the phases of choice and implementation are underrepresented. This constitutes the main gap in the development of an effective CDSS.

Introduction

In the day-to-day routine of healthcare units, the domain professionals interact with hundreds of patients. Naturally, it is of great importance that all this interaction is transformed into records, whether clinical or administrative, which in turn are transformed into data, information, and, hopefully, knowledge. Despite technological developments, these records are still kept in physical format, which creates a delay in the work performed when compared to digital versions [ 1 ].

Friedman [ 2 ] boasted a theorem in which it shows that an individual or group working with the contribution of a technological resource of information, has a better performance compared to a job without such assistance. For this functional theorem to be verified, the information resources must be valid and reliable and the user must know how to handle it properly.

The electronic representation of clinical records are embodied by Electronic Health Records (EHR), which aim, in particular, to eliminate the use of paper in the healthcare field. Notwithstanding, the representation of electronic health data tends to be more than just a substitute for paper [ 1 ]. HIMSS [ 3 ] shows that a healthcare information system must have the ability to generate a complete view of the clinical record at a meeting with a patient, including decision evidence-based support, quality management and results reporting, apart from the support of other activities related to direct or indirect care by means of a shared area. Thereby, it is evident that for a healthcare information system to be effective, it is crucial the existence of an integrated component of support in decision-making processes.

The concept of Decision Support (DS) is equivalent to the activity that assists a given user following a certain purpose. Thus, a Decision Support System (DSS) turns this activity into a system-based format in an efficient and reliable way, composing models and techniques through a knowledge representation infrastructure [ 4 ]. In this sense, a DSS portrays a system designed to support a professional in obtaining knowledge and making decisions in the specific area, therefore, diminishing uncertainties during the decision-making process [ 5 ].

In the healthcare field, the decision-making support activity is designed as Clinical Decision Support (CDS). The representation of this activity in a computerized-based format is translated into a Clinical Decision Support System (CDSS), where it provides all the information and desired knowledge that facilitate the daily tasks of healthcare providers and guarantee an improvement in the quality of services.

As a clinical knowledge-based system, the treatment of information and the process of knowledge extraction are considerable aspects for a Clinical Decision Support System to achieve its objectives. Some features are mandatory in a CDSS, but not all are known and some may be missed and contribute to system failures [ 6 ].

According to Simon, the decision-making process is the heart of any organization and it influences all processes integrated into it [ 7 ]. The decision-making process can be outlined using various models and theorems. Within the scope of this study, the Bounded Rationality theory was adopted as a reference to Herbert Simon’s work [ 8 ]. It is stated that people act in a rational way according to the knowledge and perception that they get. In an initial phase, this model approached three stages: Intelligence, Design and Choice. Later on, a new stage was added by Sprague and Carlson [ 9 ], resulting in the implementation phase. Thus, the four phases resulted by Simon’s work [ 8 ], redound to model decision making process of an Intelligent Decision Support System.

Previous systematic review studies (SRs) have addressed the use of a Clinical Decision Support System to assess its use and effectiveness [ 10 – 12 ], but applied to a specific workflow. Instead, this systematic review aims to analyse a set of articles that address a CDSS in order to identify the trends of the characteristics for its conception. In addition, we also identify other aspects such as the purpose of care and the recipient of the intervention. Furthermore, in order to enrich the outcomes obtained from the review, we trace a general trend of a CDSS, relating it with the four phases proposed by Simon.

This article is structured in four sections. First, an introduction to the topic of study is presented. Secondly, the landscape of the methods used to make the Systematic Review. The third section presents the results of the Systematic Review. The fourth section is dedicated to discussing the results obtained. At last, in section five, conclusions are drawn while leaving open doors for future work.

Decision-making theory

Decision-making is one of the main processes dealt within an organization [ 13 ]. This process can be approached using different models and theories. In the present study, the bounded rationality theory proposed by Simon will be focused.

Herbert Simon [ 8 ] was an economist who carried out research involving several areas, such as psychology, computing and management. One of Simon’s great contributions to scientific research was the theory of bounded rationality, where Simon redefines human rationality arguing that people act rationally according to the knowledge and perception they obtain. To formalize his theory, Simon established four phases that define the decision-making process, as shown in Fig 1 .

Fig 1. Iterative phases of the decision-making process.

In Intelligence phase, problems are identified and the purpose of action involving the decision is determined. In the Design phase, possible solutions are designed and alternatives are created to solve the problem. Consequently, in the Choice phase, the alternative that most satisfies the purpose of action will be chosen. Years later, Turban [ 14 ] was responsible for reformulating Simon’s theory by adding a fourth phase in order to assess the implementation process. Thus, the Implementation phase results from the joining of the three previous phases put into practice. It should be noted that the decision-making is a sequential and iterative process, which requires the completion of all phases to reach the final phase and an ongoing review of the same.

Intelligence—Process of formalization of the problem and definition of decision conditions. The decisions to be made with the help of the proposed system and the benefits it will bring are still unclear and not understood. In this phase, reality is examined in order to model the existing information for decomposing the problem and determining its properties. The intelligence phase ends with a problem statement.

Design—At this stage it is necessary to have the problem defined to search for alternatives or available options, where possible courses of action are analysed and validated. Problem-situation models are built to explore alternative solutions. For this, it is necessary to have well-defined and declared selection criteria as a result for the evaluation of potential solutions that will be identified (includes techniques, technologies, format, integration, functionalities, etc). Several possible outcomes can be considered for each alternative, each with a certain probability of occurrence. In cases where decisions are made with uncertainty, there are multiple outcomes for each alternative.

Choice—In the choice phase, all alternatives are researched, evaluated, and one is defined as the recommended solution. In pursuit of goals, a course of action that is good enough is selected. Computer models or critical success factors can be used as techniques to evaluate the recommended solution. Normative models can use either the analytical model or an exhaustive and complete enumeration model. The solution is tested and once the proposed solution appears viable, a decision can be made.

Implementation—This phase is the validation of all previous phases. If the adopted solution seems good enough, it can be implemented. Successful implementation results in solving the original problem. In case of failure, it must be returned to the previous phases.

The systematic review was conducted based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statements [ 15 ], followed by a checklist and a flow diagram.

Information sources

Five online data sources related to research in health information systems were selected. First, a search was performed on Scimago [ 16 ] comprising two areas of study: information systems and multidisciplinary. From this, journals with Q1 quartile in the last 5 years and with an impact factor greater than 150 were analysed. Journals that had the potential to have articles more related to the purpose of this study and that were already known by the authors were selected: Decision Support Systems, Nature Research Journal and Plos One. To complement the study, two public repositories were chosen, such as: the PubMed library (medline) and the Association of Information Systems (AIS) e-library. The Table 1 shows information about the selected data source types.

Table 1. Information of selected data sources.

Source: Scimago Journal and Country Rank via www.scimagojr.com , accessed on May 13, 2022.

Eligibility criteria and search strategy

To select the studies for the development of the systematic review, eligibility criteria were defined: (i) published studies in article format; (ii) open access or free full text; (iii) written in English; (iii) articles addressing a decision support systems in healthcare; (iv) computerized or electronic decision support systems. To meet these criteria, search filters were applied. The selected articles must address practical cases of a specific clinical decision support system, therefore, articles of the literature review type or that did not address a particular CDS system were excluded.

The search strategy was performed considering an ordering from the most recent publications to the oldest ones, restricting to a range from 2000/01/01 to 2020/12/31. To identify studies of the desired scope, the query terms were: ((everything:“Clinical Decision Support System”) OR everything:“Clinical Decision Support”) OR everything:“Decision Support System in Healthcare”. The title, abstract and keywords were the primary strategy for analyzing each scientific publication. When these parameters were not sufficient, a complete reading of each article was made, in order to guarantee the eligibility of inclusion criteria.

Data extraction and management

The screening process was carried out by two authors (FH and TG) independently, by reading the titles, abstracts and keywords of the publications of the search results. When the criteria were met, the full text was read. In moments of disagreement or doubt, the third reviewer (MS) took a stand and contributed to the discussion.

Through the review of eligible articles, data extraction was performed by two reviewers (FH and TG) and focused on four characteristics of a CDSS, defined as: knowledge management and representation technique; technological resources integrated into the system; the type of system; and system integration. These four categories were considered by the authors as the most relevant for the purpose of this study. In addition, the following information was extracted: clinical setting, study design, recipient of intervention, and purpose of care. Data extraction was performed through the textual analysis of the articles and the search for keywords. For better management and analysis, the extracted data were recorded on an appropriate sheet (see S1 File ).

To answer the second research question of this study, it was identified in which phase of the decision-making theory the CDSS of the reviewed articles was found. Thus, based on the literature on decision-making theory [ 17 , 18 ] and the definitions of each phase, the authors considered a set of criteria for the assignment of each phase, as shown in Table 2 . For a system to be classified in a certain phase, it must fulfill at least one of the conditions indicated.

Table 2. Choice of criterias for the classification of Simon’s phases.

Note: The conditions presented are of disjunction, that is, at least one of them must be met for the system to be classified in a certain phase.

Quality assessment and data synthesis

All the reviewed studies portray a Clinical Decision Support System (CDSS) designed for a specific purpose. The key points for conducting this systematic review were to identify the trends of the CDSS developed regarding their type of system, integration, representation and formulation of knowledge, and their technological features.

The reporting quality was made following the PRISMA checklist. Publications considered eligible for the analysis were assessed for the methodological quality, meeting the inclusion criteria, risk of bias, assumptions and simplifications, and clarified evidence-based results. The reviewers did not apply any methodological assessment tool.

After defining the five data sources, the reviewers selected a set of publications within the scope of Clinical Decision Support Systems, through the search strategy and the inclusion criteria previously defined. The outcomes presented through tables and narrative summary characterize the different Clinical Decision Support Systems presented in the selected studies, in order to trace a global trend. The data from the matching results were pooled out and evaluated, based on the total value of studies and the probability of occurrence. The reviewers noted the presence of clinical and statistical heterogeneity among the studies.

Study selection

The study selection was carried out following the PRISMA statement [ 15 ], using a checklist (see S2 File ) and a flow diagram represented in Fig 2 ( S3 File ). 768 records were identified by searching five databases considered most influential for the purpose. After removing duplicate records, 690 records were screened to read the title, abstract and keywords. Lastly, the articles that did not meet the selection and search criteria were excluded, comprising: non-article format; unpublished articles; not in english; out of period; not open access. The selected articles were read in full to assess their eligibility. Finally, 52 articles met the research questions and were elected eligible and appropriate to carry out the systematic review, embracing a specific Clinical Decision Support System and not literature review articles.

Fig 2. Flow diagram of study selection for systematic review.

Study characteristics

The studies that met the inclusion criteria were characterized according to a set of attributes. The period covered between the studies was from 2000 to 2020, with 50% from 2020, giving priority to the most recent ones.

The studies were searched in the five previously selected data sources, with eighteen studies from PubMed (34,62%), eleven studies from Decision Support Systems journal (21,15%), ten studies from PlosOne (19,23%), and five studies from Nature (9,62%). We note a diversity in the countries that developed the studies, covering four different continents. However, the majority stand out to the United States of America, representing nineteen studies (34,62%).

Studies realized in any healthcare setting were the most frequent ones (28,85%), followed by hospital setting (21,15%) and hospital academic centers (21,15%). The most common recipients of the intervention are, directly, general practitioners (32,69%). Studies reveal that CDSS also has an action on patients, but mostly, indirectly.

In general, the study design of the reviews articles, demonstrates the evaluation of the effectiveness of the CDSS regarding its cost, implementation and usability. In addition, the review showed that the CDSS addressed have an associated clinical intervention, with the most common purpose of care being a specific workflow (32,69%), followed by a specific patient disease (15,09%).

The information extracted from the studies that were considered relevant for the desired purpose generated a set of outcomes, based on the characterization of a Clinical Decision Support System (CDSS).

Knowledge management . The representation and formulation of the acquired knowledge is one of the most important steps in the development of a CDSS. In order to identify the most used techniques for knowledge management, we have identified the approaches used in studies of the systematic review, as shown in Table 3 . Clinical Practice Guidelines (38,46%), rule-based module (40,39%) and algorithmic logic (38,46%), were the most used approaches to knowledge management in the designing of a CDSS. These techniques were used individually or in combination with others. The formulation of a knowledge base was also identified in thirteen studies (25%), in combination with other techniques. Inference engines were identified in seven studies (13,46%), which were also considered in some studies, as reasoning engines. Three studies (5,77%) provided if/then statements, and eight studies (15,39%) applied methods based on variables. Terminology standards and clinical classification systems were present in five studies (9,62%), applying, in particular, the ICD-10 system and the HL7 communication protocol. Bayesian and neural networks were used in two different articles (3,85% each). Finally, three studies (5,77%) used data mining techniques to reproduce the desired knowledge. Two studies [ 19 , 20 ] were excluded from pooling, as the method used in the knowledge representation process was not identified.

Table 3. Outcomes of knowledge representation and management.

Technological features . Technological interventions represent the features that contribute to the system achieving its purpose. The most common technological feature approached in the CDSS is recommendation and suggestion feature, identified in twenty four studies (46,15%), as shown in Table 4 . Information management and monitoring are the second most common feature in the systems, covering eighteen studies (46,15%). The third most desired feature is alerts, notifications and reminders, covering fourteen studies (26,92%). Therefore, it follows the purpose of reducing errors as mentioned in eleven studies (21,15%). Eight articles (15,38%) design the CDSS for assessment purposes. The prediction feature is also used for eight studies for different purposes (15,38%). The automation and prioritization of processes is considered to be of great importance and is addressed by seven articles (13,46%). The triggering of events is addressed by four studies (7,69%). The standardization of the clinical process is desired by four studies (7,69%). Three articles (5,77%) highlighted the calculation and scoring methods as features of a CDSS. Finally, the cost and time reduction is seen as a main feature in two studies (3,84%).

Table 4. Outcomes of technological features.

System integration . The results related to the integration of the Clinical Decision Support System (CDSS), showed that 51,92% of the articles (twenty-seven) use a standalone system, as shown in Table 5 . The remaining studies show that their CDSS are integrated with another system, as well as using the information from these systems. Thus, eleven studies (21,15%) reveal that they integrate their CDSS with an Electronic Health Record (EHR) system; five studies (9,62%) integrate their system with a specific health information system; four studies (7,69%) integrate with a Computerized Provider Order Entry (CPOE) system; in this sequence, three studies (5,77%) integrate their CDSS with both EHR and CPOE; two studies differentiate their integration with an Electronic Medical Record (EMR) system.

Table 5. Outcomes of system integration.

Type of system . The typology of a Clinical Decision Support System (CDSS) differs in several technological aspects, considering both the presentation of the user interface and the technique it is based on. The type of systems identified in the review of studies is represented in Table 6 . Most studies (19.23%) use a web-based application to present the system interface to the user. Nine studies (17,31%) describe their CDSS as a computerized tool, not specifying the type of software or interface used. Eight studies (15,38%) develop their decision support system in a software application. Regarding the logic used, five studies (9,62%) are based on machine learning techniques, and four studies (7,69%) are artificial intelligence-based. Three studies (5,77%) develop their CDSS in a mobile application and, still, four studies (7,69%) cover a mobile and web application. Two studies (3,85%) describe their CDSS as knowledge-based, and other two studies (3,85%) present the CDSS as data analytics. Other types of systems were considered in unique studies, such as cloud computing (1,92%), data-layer infraestructure (1,92%), image retrieval expert system (1,92%), user interface (1,92%), and web application integrated with data analytic (1,92%).

Table 6. Outcomes of type of system.

The outcomes for each group were varied regarding the type of the CDSS, knowledge management, integration of systems and the technological features. Despite this diversity, we were able to notice a trend in results based on their frequent presence in the reviewed studies. As shown in Fig 3 , the most common knowledge representation and management techniques were the rule-based module, clinical practice guidelines and algorithmic logic. Regarding to the technological intervention or feature, the three top trends were recommendation and suggestion, information management and monitoring, and alerts, notifications and reminders. Standalone CDSS were the most common one, following integration with Electronic Healthcare Records (EHR) systems and specific healthcare information systems. The most frequent type of system were, respectively, web-based application, specific computerized tool, and software application. Despite the individual results, we also noticed a trend towards a combination of results. The most frequent sequence related to knowledge management was the mix of algorithmic logic with clinical practice guidelines. For technological features, the most common combination was the joining of recommendation and suggestion with alerts, notifications and reminders.

Fig 3. Characterization of general trends obtained through the review.

Temporal evolution

Fig 4 demonstrates the temporal evolution of knowledge management and representation techniques used in studies. It is possible to verify that rule-based module and the algorithmic logic have been present since 2000 until today, mostly. As for clinical practice guidelines, on the other hand, proved to be a current issue due to their presence from 2013 to 2020. In general, the characteristics remain in existence over time, with a greater occurrence in 2020 due to the number of articles analysed that year. In contrast, the characteristics of the Bayesian network, if / then statements and neural networks were considered more recent, emerging from 2014.

Fig 4. Temporal evolution of knowledge management.

According to Fig 5 , we can see that the integration of standalone CDSS has been increasing in recent years. The integration with EHR systems, oscillated between 2014 to 2020, standing out also in the last year. Specific health information systems have been integrated into the CDSS from 2000 to 2020, on a regular basis.

Fig 5. Temporal evolution of system integration.

According to Fig 6 , the specific computerized tools have been present since the beginning of the time interval and were more present in the year 2020. The software application, machine learning-based, was already present. The mobile application remained constant. Web-based application was more prevalent in 2013.

Fig 6. Temporal evolution of type of system.

As showed in Fig 7 the alerts, notifications and reminders and the information management and monitoring feature were more present in 2020, but also present in some previous year. The recommendations and suggestions feature was present from 2003 to 2020. Standardization was present in 2000 and only returned in 2020. The calculation and scoring, cost and time reduction and prediction features were considered the most recent ones.

Fig 7. Temporal evolution of CDSS features.

Maturity staging model

For a CDSS to be considered effective, it has to reach the prestigious level of maturity. Thus, it is important to recognize the degree of maturity associated with the characterization of the CDSSs. In order to complement the study, a cross-checking was made with the trends characteristics facing the four phases of Simon’s decision-making theory [ 8 ]. Thus, four stages were classified aiming to represent the level of maturity of a system, based on Simon’s phases:

Stage 4: Implementation + Choice + Design + Intelligence

Stage 3: Choice + Design + Intelligence

Stage 2: Design + Intelligence

Stage 1: Intelligence

Table 7 represents the crossing of the Simon’s phases with the three most common characteristics of each group, in order to assess their stage of maturity. The columns referring to the maturity stages, correspond to the number of articles of the respective characteristic, given their presence in each Simon phase. It should be noted that the values are cumulative, that is, for a stage to be reached, it must contain the previous stage. All studies corresponding to each characteristic are present in the initial phase of Intelligence. Maturity is calculated using the weighted average (WAVG), which should vary between 1 and 4, corresponding to each stage.

Table 7. Maturity Staging Model applied on the reviewed studies.

Main findings.

This article aimed to develop a systematic review within the scope of Clinical Decision Support Systems (CDSS). The first research question (RQ1) was to identify the tendency of adopted approaches in CDSS development addressed in the reviewed studies, as shown in Fig 3 . To classify the revised studies, information about four predefined groups were extracted: knowledge management, technological intervention, system integration and type of system. Although there are other characteristics associated with a CDSS, the authors chose to extract specific information that go beyond the main objective of a CDSS, which is to assist the decision maker in the decision-making process. The results showed that the most used techniques for the knowledge representation in the systems are the creation of modules based on rules, clinical practice guidelines and logic algorithms. The technological features most present in CDSS are recommendation and suggestion, monitoring and information management, and alerts and reminders. Usually, CDSS are used standalone, following the integration with Electronic Health Systems or a specific information system. It was also identified that the most common types of systems are web applications, specific computer tools, and software applications.

In order to answer the second research question (RQ2), a preliminary classification during the review was carried out identifying which phase of the decision-making process model the CDSS was in. According to Simon’s bounded rationality theory [ 8 ], it was considered: Intelligence phase as responsible for analysis, exploration and description of the problem to be faced; the Design phase as the development and analysis of possible solutions to the problem at hand; in the Choice phase, the most appropriate solution is chosen to solve the problem; the Implementation seeks to apply the solution to the problem in question.

Regarding the maturity staging model proposed in Table 7 , when the stage 4 is achieved, the system has reached its prestigious level of maturity. However, this is not verified in the analysis. The weighted average (WAVG) of the characteristics showed very similar values, meaning that the CDSS of the reviewed studies predominates in Simon’s Intelligence and Design phases, equivalent to stage 2 of maturity. The recommendation and suggestion (1.55) and software application (1.53) characteristics have the lowest values nearly reaching the previous phase of Intelligence. The algorithmic logic characteristic stands out the most and shows that it is closer to achieving the upper stage of maturity (stage 3), meeting the Choice phase.

There are other systematic reviews that study the role of CDSS. However, to the best of our knowledge, existing studies analyse CDSS applied to a specific clinical context and do not evaluate its effectiveness as a whole (as [ 11 , 12 , 71 ]). The present systematic review selected a set of studies that approach the development of a CDSS characterizing them in order to trace a global trend, as well as assess their level of maturity. In this sense, it was possible to analyse the CDSS in its completeness, covering different approaches, techniques and purposes of use. The main contribution of this work was the proposed maturity staging model, that allowed to identify a gap between the state-of-art and the desirable stage of maturity in order to provide an effective CDSS. The results showed that the revised CDSS do not go beyond stage 2, meaning that CDSS are not succeeding in the healthcare arena due to the lack of maturity, i.e., as CDSS are not capable of supporting the choice of actions in clinical settings and are also not involved in the implementation of these actions.

This study allowed to raise a concern in the development of CDSS and to raise awareness that limited systems are being created and that may be far from being optimized. The projection of this study allowed us to portray a reality of many decision support systems in the health area, demonstrating the opposite of what it should be. When a system reaches the model implementation phase, it should be closer to reaching its optimization and not going back to the previous phases. This immaturity may be due to a lack of understanding of the real problem, difficulty in choosing the ideal solution, and failures in usability tests. An in-depth analysis must be done to discover the main constraints that prevent the inclusion of the decision-making phases in the CDSS.

Limitations and future research

The maturity staging model, combined with the phases of the decision-making process, serves to assess the effectiveness of the decision. When an implemented decision does not produce the desired results, there are likely to be several causes, such as incorrect problem definition, poor evaluation of alternatives, or inadequate implementation [ 18 ]. The proposed alternative may not be successful, which will lead to a new analysis of the problem, evaluation of alternatives and selection of a new alternative. Thus, evaluation is a key factor in the process because decision-making is a continuous and never-ending process.

Some limitations should be stressed. First, we were unable to perform a meta-analysis due to the variation in the type of studies analysed, as well as to use a quality assessment methodology to assess the quality of studies. The sample number of eligible studies was also limited. Another limitation was regarding the characterization of the CDSS, due to some studies not directly explaining the respective approach used. There may also be some inconsistency in the review carried out due to the personal opinions of each reviewer.

It is known that in the clinical area regulations and ethical issues (e.g. computer based control of infusion pumps) limit the application of the most advanced phases of the decision-making process (e.g. choice and implementation). This means that the highest value for the maturity stage might be lower than 4. Thus, some difficulties encountered in the adoption of CDSS may be related to the medical context, such as: the development of methods for supporting choice phase of decision making; interoperability among medical devices and health care information systems; technology acceptance; ethical and regulatory restrictions; poor involvement of professionals and organizations. A lot of work should be done in the future to mitigate those limitations, starting with: i) consider a larger sample of studies; ii) determine the accepted range of values for the maturity stage in particular clinical context of application; and iii) depict what it should be the focus of research in order to fulfil the actual maturity gap and develop effective CDSS.

Clinical Decision Support Systems (CDSS) represent the decision support activity and can be translated into a machine-readable computerized format. Most healthcare information systems are encouraged to or already include clinical decision support practices to organize clinical knowledge and improve the decision-making process [ 3 ]. In order to answer the research questions formulated in this work, a systematic review was developed to identify the techniques and approaches used in CDSS from 52 studies. The outcomes were varied and did not show a main pattern, leading to limited evidence. Nevertheless, it was identified the top three trends of the four defined groups: knowledge management, technological features, type of system, and system integration. In addition, this study allowed a more complete analysis to understand the state of maturity of the CDSS. A crossover of the identified trends was made to identify the maturity regarding the four phases proposed by Simon. The results demonstrated the lack of maturity of the CDSS presented by the reviewed studies.

Decision making is a process of making a choice between several alternatives to achieve a desired outcome. Among these possible causes, the most common and serious error is an inadequate definition of the problem. When the problem is not defined correctly, the alternative selected and implemented will not produce the desired result. That said, we believe that the biggest difficulties on the CDSS adoption are in the operational clinical environment, with the involvement of characteristics, processes, and stakeholders. Furthermore, based on the identified gap in this study, an agenda should be created for what an effective CDSS should be. There is an incentive for the scientific community to contribute to mitigate the limitations in order to achieve more effective Clinical Decision Support Systems.

Supporting information

Data availability.

All relevant data are within the manuscript and its Supporting information files.

Funding Statement

This study was supported by FCT – Fundação para a Ciência e Tecnologia, within the scope of the project DSAIPA/DS/0084/2018, FH work was supported by the grant 2021.06230.BD. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Gabriele oliva.

22 Mar 2022

PONE-D-20-38355Towards Effective Clinical Decision Support Systems: A Systematic ReviewPLOS ONE

Dear Dr. Hak,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Additional Editor Comments (if provided):

Two reviews were obtained, both suggesting minor edits to the paper. I agree with the reviewers' evaluation and I am recommending a minor revision of the paper.

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Reviewers' comments:

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Reviewer #1: Partly

Reviewer #2: Partly

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Reviewer #1: I Don't Know

Reviewer #2: I Don't Know

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Reviewer #2: Yes

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Reviewer #1: The topic is quite relevant and important. Overall, the paper achieved its overall aim i.e. " to systematically review the extant empirical evidence on this topic, establish the status quo of the research, and propose an agenda for further studies" However, there are a number of issues with the methods and the reporting as documented below.

This paper provides an extensive literature review related to the key features that influences the development of effective CDSS.

• The authors argue that focused only on papers published in five online data sources. There are other resources such as valuable conferences that might be even more relevant to the study's purpose than those in grey literature.

• The search strategy is not comprehensive enough for a systematic review. The search strategy is apparently missing.

• In the information resources, it is stated that five online data sources that have a major impact on health information systems research were selected. On what basis are these five main sources (references)? Which reference (s)?

Discussion:

• The authors argue that none of the reviews have provided a global trend of a CDSS through a systematic review, but the reader might find this claim ambitious (Given that you have not searched the database anymore).

• In the discussion, in systematic review articles, the results of the article should be compared with other articles related to this paper.

The structuring of the discussion and conclusion presentation is not well-argued and not clear to me.

Please rewrite the conclusion. Start with the main findings which should contain an overview of the literature you studied.

Reviewer #2: The article carries out a systematic review of clinical decision support systems. In particular, they want to identify the articles dealing with this issue by highlighting the characteristics that distinguish them, also providing information on the purpose and the recipient.

Five open source online resources are considered. In addition, defined a methodology based on specific criteria: freely accessible articles, written in English, must provide medical support, media must be computer or technological. Two reviewers will analyze the title, abstract and keyword of the articles according to the parameters and will extract the most significant ones. The extracted elements will be read entirely and analyzed. In particular, four main features will be analyzed: the management and technical representations, technological characteristics of the system, the type of system and the integration of the system. The data were classified without providing an analysis of the analyzed characteristics and differences between the various solutions proposed. It also shows the trend of the distribution and the use of different methodologies not analyzing the data or providing a comment of the same.

Authors should express more clearly the choice of criteria and the analysis made. In addition, the differences and aspects highlighted by the collection should be highlighted and clarify.

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Reviewer #1:  Yes:  Maryam Zahmatkeshan

Reviewer #2: No

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Author response to Decision Letter 0

Collection date 2022.

20 Jul 2022

Reviewer #1:

First of all, we would like to thank for the comments and constructive suggestions that will certainly contribute to the enrichment of this study. In this revised version, we reinforce all the points suggested by the reviewer, especially in the methods and in the discussion part. The changes are highlighted in yellow in the revised manuscript with track changes version.

In the 'Information Sources' chapter, we reinforce the criteria that were applied to choose data sources, based on the Scimago ranking. The search strategy was also completed, reinforcing the filters and terms used for the search. We also combine the search strategy with the eligibility criteria. In the discussion part, we refer to other systematic review studies that address the Clinical Decision Support System (DSS), but that do not relate to the objective of this study. They tend to analyse the application/performance of DSS into specific clinical problems, while we intend to analyse and characterize DSS by their capacities supporting the phases decision making process.

In the Discussion and Conclusion chapters, we rewrote as suggested, presenting a clearer, literature-based structure. We hope that the changes made are as expected.

Reviewer #2:

First of all, we would like to thank for the comments and constructive suggestions that will certainly contribute to the enrichment of this study. In this revised version, we reinforce all the points suggested by the reviewer, especially in the methods part. The changes are highlighted in green in the revised manuscript with track changes version. In the Data Extraction and Management chapter, we describe all the information that was extracted from the reviewed articles. In addition, we highlighted what was expected to be found at each stage of the decision-making process. We included the list of conditions considered for classifying DSS according to these phases (Table 2). We hope that the changes made are as expected.

Submitted filename: Response to Reviewers.docx

Decision Letter 1

28 Jul 2022

Towards Effective Clinical Decision Support Systems: A Systematic Review

PONE-D-20-38355R1

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• Published: 06 February 2020

An overview of clinical decision support systems: benefits, risks, and strategies for success

• Reed T. Sutton   ORCID: orcid.org/0000-0002-3009-1914 1 ,
• David Pincock 2 ,
• Daniel C. Baumgart 1 ,
• Daniel C. Sadowski 1 ,
• Richard N. Fedorak 1 &
• Karen I. Kroeker 1  

npj Digital Medicine volume  3 , Article number:  17 ( 2020 ) Cite this article

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• Drug regulation
• Health services
• Medical imaging

Computerized clinical decision support systems, or CDSS, represent a paradigm shift in healthcare today. CDSS are used to augment clinicians in their complex decision-making processes. Since their first use in the 1980s, CDSS have seen a rapid evolution. They are now commonly administered through electronic medical records and other computerized clinical workflows, which has been facilitated by increasing global adoption of electronic medical records with advanced capabilities. Despite these advances, there remain unknowns regarding the effect CDSS have on the providers who use them, patient outcomes, and costs. There have been numerous published examples in the past decade(s) of CDSS success stories, but notable setbacks have also shown us that CDSS are not without risks. In this paper, we provide a state-of-the-art overview on the use of clinical decision support systems in medicine, including the different types, current use cases with proven efficacy, common pitfalls, and potential harms. We conclude with evidence-based recommendations for minimizing risk in CDSS design, implementation, evaluation, and maintenance.

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Introduction: what is a clinical decision support system.

A clinical decision support system (CDSS) is intended to improve healthcare delivery by enhancing medical decisions with targeted clinical knowledge, patient information, and other health information. 1 A traditional CDSS is comprised of software designed to be a direct aid to clinical-decision making, in which the characteristics of an individual patient are matched to a computerized clinical knowledge base and patient-specific assessments or recommendations are then presented to the clinician for a decision. 2 CDSSs today are primarily used at the point-of-care, for the clinician to combine their knowledge with information or suggestions provided by the CDSS. Increasingly however, there are CDSS being developed with the capability to leverage data and observations otherwise unobtainable or uninterpretable by humans.

Computer-based CDSSs can be traced to the 1970s. At the time, they had poor system integration, were time intensive and often limited to academic pursuits. 3 , 4 There were also ethical and legal issues raised around the use of computers in medicine, physician autonomy, and who would be at fault when using the recommendation of a system with imperfect ‘explainability’. 5 Presently, CDSS often make use of web-applications or integration with electronic health records (EHR) and computerized provider order entry (CPOE) systems. They can be administered through desktop, tablet, smartphone, but also other devices such as biometric monitoring and wearable health technology. These devices may or may not produce outputs directly on the device or be linked into EHR databases. 6

CDSSs have been classified and subdivided into various categories and types, including intervention timing, and whether they have active or passive delivery. 7 , 8 CDSS are frequently classified as knowledge-based or non-knowledge based. In knowledge-based systems, rules (IF-THEN statements) are created, with the system retrieving data to evaluate the rule, and producing an action or output 7 ; Rules can be made using literature-based, practice-based, or patient-directed evidence. 2 CDSS that are non-knowledge based still require a data source, but the decision leverages artificial intelligence (AI), machine learning (ML), or statistical pattern recognition, rather than being programmed to follow expert medical knowledge. 7 Non-knowledge based CDSS, although a rapidly growing use case for AI in medicine, are rife with challenges including problems understanding the logic that AI uses to produce recommendations (black boxes), and problems with data availability. 9 They have yet to reach widespread implementation. Both types of CDSS have common components with subtle differences, illustrated in Fig. 1 .

They are composed of (1) base: the rules that are programmed into the system (knowledge-based), the algorithm used to model the decision (non-knowledge based), as well as the data available, (2) inference engine: takes the programmed or AI-determined rules, and data structures, and applies them to the patient’s clinical data to generate an output or action, which is presented to the end user (eg. physician) through the (3) communication mechanism: the website, application, or EHR frontend interface, with which the end user interacts with the system 9 .

CDSS have been endorsed by the US Government’s Health and Medicare acts, financially incentivizing CDS implementation into EHRs. 10 In 2013, an estimated 41% of U.S. hospitals with an EHR, also had a CDSS, and in 2017, 40.2% of US hospitals had advanced CDS capability (HIMSS Stage 6). 11 Elsewhere, adoption rates of EMRs have been promising, with approximately 62% of practitioners in Canada in 2013. 12 Canada has had significant endorsement from the government level, as well as Infoway, a not-for-profit corporation. 13 England has also been a world leader in healthcare IT investment, with up to 20 billion euros invested back in 2010. 13 Several countries have also managed to implement national health records, at least for patient-facing data, including Denmark, Estonia, Australia, and others. 14

The scope of functions provided by CDSS is vast, including diagnostics, alarm systems, disease management, prescription (Rx), drug control, and much more. 15 They can manifest as computerized alerts and reminders, computerized guidelines, order sets, patient data reports, documentation templates, and clinical workflow tools. 16 Each CDSS function will be discussed in detail throughout this review, with the potential and realized benefits of these functions, as well as unintended negative consequences, and strategies to avoid harm from CDSS. Methodology used to inform the review is shown in Box 1 .

Box 1. Methods and sources used for this overview

MEDLINE search 1980-January 2018. Key words: CDSS, diagnostic decision support system/DDSS, personal health record/PHR decision support, EHR decision support

Hand searches of the references of retrieved literature

University libraries searching for texts on clinical decision support systems and other keywords mentioned above

Personal and local experience working with healthcare technology and decision support systems

Functions and advantages of CDSS

Patient safety.

Strategies to reduce medication errors commonly make use of CDSS (Table 1 ). Errors involving drug-drug interactions (DDI) are cited as common and preventable, with up to 65% of inpatients being exposed to one or more potentially harmful combinations. 17 CPOE systems are now designed with drug safety software that has safeguards for dosing, duplication of therapies, and DDI checking. 18 The types of alerts generated by these systems are among the most disseminated kind of decision support. 19 However, studies have found a high level of variability between how alerts for DDIs are displayed (e.g., passive or active/disruptive), which are prioritized, 20 , 21 and in the algorithms used to identify DDIs. 18 , 22 Systems often have varying degrees of irrelevant alerts presented, and there is no standard for how best to implement which alerts to providers. The US Office of the National Coordinator for Health Information Technology has developed a list of ‘high-priority’ list of DDIs for CDS, which has reached various levels of endorsement and deployment in CDSS’ of other countries including the U.K., Belgium, and Korea. 20 , 21 , 23

Other systems targeting patient safety include electronic drug dispensing systems (EDDS), and bar-code point-of-care (BPOC) medication administration systems. 24 These are often implemented together to create a ‘closed loop’, where each step of the process (prescribing, transcribing, dispensing, administering) is computerized and occurs within a connected system. At administration, the medication is automatically identified through radio-frequency identification (RFID) or barcodes and crosschecked with patient information and prescriptions. This presents another target for CDSS and the potential benefit is the prevention of medication administration errors occurring at the ‘bedside’ (opposed to further upstream). Adoption is relatively low, partly due to high technology requirements and costs. 25 However; studies show good efficacy for these systems in reducing errors. 26 Mohoney et al. showed that many of these systems can be combined with CPOE and CDSS simultaneously, with reduced prescribing error rates for drug allergy detection, excessive dosing, and incomplete or unclear ordering. 24 As with most CDSS, errors can still be made if providers omit or deliberately work around the technology. 27

CDSS also improve patient safety through reminder systems for other medical events, and not just those that are medication related. Among numerous examples, a CDSS for blood glucose measurement i n the ICU was able to decrease the number of hypoglycemia events. 28 This CDSS automatically prompted nurses to take a glucose measurement according to a local glucose monitoring protocol, which specified how often measurements should be done according to specific patient demographics and previous glucose levels/trends. 28

Overall, CDSS targeting patient safety through CPOE and other systems have generally been successful in reducing prescribing and dosing errors, contraindications through automated warnings, drug-event monitoring and more. 29 Patient safety can be considered a secondary objective (or requirement) of almost all types of CDSS, no matter the primary purpose for their implementation.

Clinical management

Studies have shown CDSS can increase adherence to clinical guidelines. 30 This is significant because traditional clinical guidelines and care pathways have been shown to be difficult to implement in practice with low clinician adherance. 31 , 32 The assumption that practitioners will read, internalize, and implement new guidelines has not held true. 33 However, the rules implicitly encoded in guidelines can be literally encoded into CDSS. Such CDSS can take a variety of forms, from standardized order sets for a targeted case, alerts to a specific protocol for the patients it pertains to, reminders for testing, etc. Furthermore, CDSS can assist with managing patients on research/treatment protocols, 34 tracking and placing orders, follow-up for referrals, as well as ensuring preventative care. 35

CDSS can also alert clinicians to reach out to patients who have not followed management plans, or are due for follow-up, and help identify patients eligible for research based on specific criteria. 36 A CDSS designed and implemented at Cleveland Clinic provides a point-of-care alert to physicians when a patient’s record matches clinical trial criteria. 37 The alert prompts the user to complete a form which establishes eligibility and consent-to-contact, forwards the patient’s chart to the study coordinator, and prints a clinical trial patient information sheet.

Cost containment

CDSS can be cost-effective for health systems, through clinical interventions, 38 decreasing inpatient length-of-stay, CPOE-integrated systems suggesting cheaper medication alternatives, 39 or reducing test duplication. A CPOE-rule was implemented in a pediatric cardiovascular intensive care unit (ICU) that limited the scheduling of blood count, chemistry and coagulation panels to a 24-h interval. 40 This reduced laboratory resource utilization with a projected cost savings of $717,538 per year, without increasing length of stay (LOS), or mortality.

CDSS can notify the user of cheaper alternatives to drugs, or conditions that insurance companies will cover. In Germany, many inpatients are switched to drugs on hospital drug formularies. After finding that 1 in 5 substitutions were incorrect, Heidelberg hospital developed a drug-switch algorithm and integrated it into their existing CPOE system. 41 The CDSS could switch 91.6% of 202 medication consultations automatically, with no errors, increasing safety, reducing workload and reducing cost for providers.

Administrative functions

CDSS provide support for clinical and diagnostic coding, ordering of procedures and tests, and patient triage. Designed algorithms can suggest a refined list of diagnostics codes to aid physicians in selecting the most suitable one(s). A CDSS was conceived to address inaccuracy of ICD-9 emergency department(ED) admission coding (ICD is International Statistical Classification of Diseases, standardized codes used to represent diseases and diagnoses). 42 The tool used an anatomographical interface (visual, interactive representation of the human body) linked to ICD codes to help ED physicians accurately find diagnostic admission codes faster.

CDSS can directly improve quality of clinical documentation. An obstetric CDSS featured an enhanced prompting system, significantly improving documentation of indications for labor induction and estimated fetal weight, compared to control hospital. 43 Documentation accuracy is important because it can directly aid clinical protocols. For example, a CDSS was implemented to ensure patients were properly vaccinated following splenectomy, to combat the increased risk of infections (including pneumococcal, Haemophilus influenzae , meningococcal, etc.) that comes with spleen removal. However, the authors found that 71% of patients with the term ‘splenectomy’ in their EHR did not have it documented on their problem list (which was what triggers the CDSS alert). 44 A supplemental CDSS was then developed to enhance problem list documentation of splenectomy, 45 and improve the utility of the original vaccination CDSS.

Diagnostics support

CDSS for clinical diagnosis are known as diagnostic decision support systems (DDSS). These systems have traditionally provided a computerized ‘consultation’ or filtering step, whereby they might be provided data/user selections, and then output a list of possible or probable diagnoses. 46 Unfortunately, DDSS have not had as much influence as other types of CDSS (yet) for reasons including negative physician perceptions and biases, poor accuracy (often due to gaps in data availability), and poor system integration requiring manual data entry. 47 , 48 The latter is improving with better EHR-integration and standardized vocabulary like Snomed Clinical Terms.

A good example of an effective DDSS is one which was created by Kunhimangalam et al. 49 for diagnosis of peripheral neuropathy using fuzzy logic. Through 24 input fields which include symptoms and diagnostic test outputs, they achieved 93% accuracy compared to experts at identifying motor, sensory, mixed neuropathies, or normal cases. While this has great utility, especially in countries with less access to established clinical experts, there is also a desire for systems that can supplement specialist diagnostics. DXplain is an electronic reference based DDSS that provides probable diagnosis based on clinical manifestations. 50 In a randomized control trial involving 87 family medicine residents, those randomized to use the system showed significantly higher accuracy (84% vs. 74%) on a validated diagnosis test involving 30 clinical cases. 50

Given the known incidence of diagnostic errors, particularly in primary care, 51 there is a lot of hope for CDSS and IT solutions to bring improvements to diagnosis. 52 We are now seeing diagnostic systems being developed with non-knowledge-based techniques like machine learning, which may pave the way for more accurate diagnosis. The Babylon AI powered Triage and Diagnostic System in the U.K. is a good example of the potential, but also of the work that still has to be done before these systems are ready for primetime. 53 , 54

Diagnostics support: imaging

Knowledge-based imaging CDSS are typically used for image ordering, where CDSS can aid radiologists in selecting the most appropriate test to run, providing reminders of best practice guidelines, or alerting contraindications to contrast, for example. 55 An interventional CDS for image ordering at Virginia Mason Medical Center was shown to substantially decrease the utilization rate of lumbar MRI for low back pain, head MRI for headache, and sinus CT for sinusitis. 56 The CDS required a series of questions to be answered by providers prior to image ordering (POC), to verify appropriateness. Importantly, if an image was denied, an alternative was suggested by the system. Another commercialized example is RadWise®, which guides clinicians to the most relevant imaging order by analyzing patient symptoms and matching them with a large database of diagnoses, while also providing appropriate use recommendations at the point of care. 57

There is great interest in non-knowledge based CDS for enhanced imaging and precision radiology (‘radiomics’). 58 , 59 With images accounting for increasing amounts of medical data, but requiring extensive manual interpretation, providers need technologies to aid them in extracting, visualizing, and interpreting. 60 AI technologies are proving capable of providing insights into data beyond what humans can. 61 To do so, these technologies make use of advanced pixel recognition and image classification algorithms, most prominently: deep learning (DL). 62 IBM Watson Health, DeepMind, Google, and other companies are at the forefront, developing products for use in tumor detection, 63 medical imaging interpretation, 64 diabetic retinopathy diagnosis, 65 Alzheimer’s diagnosis through multimodal feature learning, 62 and countless more. IBM Watson’s ‘Eyes of Watson’, has been able to combine image recognition of a brain scan with text recognition of case descriptions to provide comprehensive decision support (or what IBM describes as a ‘cognitive assistant’). 60

Several projects have been able to demonstrate performance that is disputably ‘on par’ with human experts. 65 , 66 , 67 , 68 For example, Google’s team trained a deep convolutional neural network (CNN) to detect diabetic retinopathy (blood vessel damage in the eye) from a dataset of 130,000 retinal images with a very high sensitivity and specificity. 65 The algorithms performance was on par with US board certified ophthalmologists. Another study just recently published by the Stanford group demonstrated a CNN for detecting arrhythmias on electrocardiogram that exceeded the accuracy (F1 and sensitivity with matched specificity) of the average cardiologist on all rhythm classes. 68 With the current rate of progress, some experts controversially speculate that in 15–20 years, the majority of diagnostic imaging interpretation will be done (or at least pre-processed) by computers. 69 For the time being however, we should think of these early systems as an addition or augmentation to a clinician’s available toolset.

Diagnostics support: laboratory and pathology

Another subset of diagnostics where CDSS can be useful is laboratory testing and interpretation. Alerts and reminders for abnormal lab results are simple and ubiquitous in EHR systems. CDSS can also extend the utility of lab-based tests for the purpose of avoiding riskier or more invasive diagnostics. In Hepatitis B and C testing, liver biopsies are considered the gold standard for diagnosis, while non-invasive lab tests are not accurate enough to be accepted. However; AI models are being developed that combine multiple tests (serum markers, imaging, and gene tests) to produce much greater accuracy. 70 There is also application for CDSS as an interpretation tool where a test’s reference ranges are highly personalized, for example age, sex, or disease subtypes. 71

Pathology reports are crucial as decision points for many other medical specialties. Some CDSS can be used for automated tumor grading. This was done for urinary bladder tumor grading and estimating recurrence, with up to 93% accuracy. 72 The same has been done for brain tumor classification and grading. 73 There are many other examples including computerized ECG analysis, automated arterial blood gas interpretation, protein electrophoresis reports, and CDSS for blood cell counting. 46

Patient-facing decision support

With the advent of the ‘Personal Health Record’ (PHR), we are seeing CDS functionality integrated, similar to EHRs, with the patient as the end user or ‘manager’ of the data. This is a great step towards patient-focused care, and CDS-supported PHRs are the ideal tool to implement shared decision-making between patient and provider, specifically because CDSS can remove a ‘lack of information’ as a barrier to a patient’s participation in their own care. 74 PHRs are frequently designed as an extension of commercial EHR software, or as standalone web-based or mobile-based applications. 75 When connected to EHRs, PHRs can have a two way relationship, whereby information entered directly by the patient can be available to their providers, and also information in the EHR can be transmitted to the PHR for patients to view. 76

One of the earliest PHRs, the “Patient Gateway”, was simply a dashboard for patients to view medications and labs, and communicate with their physicians. 77 This has expanded and some systems now allow patients to modify their own record of care, effecting the EHR data as well. 78 Another example is Vanderbilt University’s MyHealthAtVanderbilt, a PHR fully integrated into the institutional EHR. In addition to disease-targeted delivery of patient educational materials, they incorporated a Flu Tool for patients with flu-like symptoms to decide the level of care they need and then help them seek treatment. 79 Symptom tracking is a useful and common feature of PHRs, but the variety of collected data is virtually limitless, from allergies to insurance coverage to prescription and medication information. 80 Furthermore, PHRs and other patient monitoring applications can be designed to collect information from health devices and other wearables, to create actionable insights for providers. An excellent example exists in diabetes care. Many systems are already in use, 81 but one in particular pioneered by the Stanford School of Medicine uses a wearable glucose monitor which transmits data to an Apple device (HealthKit). 82 Apple has made HealthKit interoperable with the Epic EHR and Epic PHR, “MyChart”. This successfully allows providers to monitor glucose trends in their patients in between visits, and contact them through MyChart for follow up or urgent recommendations. The pilot study demonstrated improved provider workflow, communication with patients, and ultimately quality of care. 82 Various other medical fields are deploying similar systems for monitoring that combines PHR/EHR, wearable technologies, and CDSS, including but not limited to heart failure (cardiology), hypertension, sleep apnea, palliative/elder care, and more.

It is worth noting that as PHRs have become more advanced with CDSS capabilities, there has also been increasing emphasis on the design of these systems to serve shared decision making between patient and provider, and to be interactive tools to make patients more knowledgeable/involved in their own care. PHRs that only serve as a repository for health information are now seen as missing the mark, particularly by patients themselves. 75

Pitfalls of CDSS

Fragmented workflows.

CDSS can disrupt clinician workflow, especially in the case of stand-alone systems. Many early CDSS were designed as systems that required the provider to document or source information outside their typical workspace. CDSS also disrupt workflow if designed without human information processing and behaviors in mind. In response, CDSS have been designed using the ‘think-aloud’ method to model practitioners’ workflow and create a system with better usability. 83

Disrupted workflow can lead to increased cognitive effort, more time required to complete tasks, and less time face-to-face with patients. Even when CDSS are well integrated within existing information systems, there can be disconnect between face-to-face interactions and interaction with a computer workstation. Studies have found that practitioners with more experiential knowledge are less likely to use, and more likely to override CDSS. 84

Alert fatigue and inappropriate alerts

Studies have found up to 95% of CDSS alerts are inconsequential, and often times physicians disagree with or distrust alerts. 85 Other times they just do not read them. If physicians are presented with excessive/unimportant alerts, they can suffer from alert fatigue. 86

Disruptive alerts should be limited to more life-threatening or consequential contraindications, such as serious allergies. However; even allergy alerts can be incorrect, and clinicians will often verify themselves, especially if the source is another site/hospital/practitioner. 85 , 87 Medication alerts can also be specialty specific, but irrelevant when taken out of context. For example, an alert against using broad-spectrum antibiotics such as vancomycin may be inappropriate in ICU. 85 An alert against duplicate medications may be inappropriate in inflammatory bowel disease clinics, where the same class of drug can be applied through different administration routes for increased effect.

Impact on user skill

Prior to CPOE and CDSS, healthcare providers, pharmacists, and nurses were relied upon exclusively to double-check orders. CDSS can create the impression that verifying the accuracy of an order is unnecessary or automatic. 85 This is an important myth to dispel.

It is also important to consider the potential long-term effect of a CDSS on users. Over time a CDSS can exert a training effect, so that the CDSS itself may no longer be required. Coined the “carry-over effect”, it is most likely with CDSS that are educational in nature. 88 Conversely, providers may develop too much reliance or trust on a CDSS for a specific task. 89 This could be compared to using a calculator for mathematical operations over a long period of time, and then having poorer mental math skills. It is potentially problematic as the user has less independence and will be less equipped for that task should they switch to an environment without the CDSS.

CDSS may be dependent on computer literacy

Lack of technological proficiency can be hindering when engaging with a CDSS. 90 , 91 This can vary by the design details of the CDSS, but some have been found to be overly complex, relying too much on user skill. 90 , 92 Systems should aim to stay as close to the core functionality of the pre-existing system as possible. Regardless, all new systems have a learning period, and so baseline evaluations of users’ technological competence may be appropriate. Further training can then be provided to facilitate full use of CDSS capabilities, 93 or more explicit guidance incorporated into the CDSS’ recommendations themselves. 94 This information could be implemented as info buttons to be non-disruptive. 95

System and content maintenance

Maintenance of CDSS is an important but often neglected part of the CDSS life-cycle. This includes technical maintenance of systems, applications and databases that power the CDSS. Another challenge is the maintenance of knowledge-base and its rules, which must keep apace with the fast-changing nature of medical practice and clinical guidelines. Even the most advanced healthcare institutions report difficulty keeping their systems up to date as knowledge inevitably changes. 85 Order sets and the algorithmic rules behind the CDSS have been identified as particularly difficult. 85

Operational impact of poor data quality and incorrect content

EHRs and CDSSs rely on data from external, dynamic systems and this can create novel deficiencies. As an example, some CDSS modules might encourage ordering even when the hospital lacks adequate supplies. In a study by Ash et al. 85 , a number of experts indicated that at their hospital, Hemoccult tests or pneumococcal vaccine inventories run out quickly, but this is not communicated to the CDSS.

Medication and problem lists can be problematic, if not updated or used appropriately. At one site, the medication list might be a list of dispensations, which means patients may or may not be taking them(and thus must still be asked in person). 85 Other medication lists are generated from CPOE orders only, thus still requiring manual confirmation that patients are taking the medication. Systems that make it easy to distinguish these are ideal. It is also a major area where PHRs could create a solution, by collecting medication adherence data directly from patients.

In poorly designed systems, users may develop workarounds that compromise data, such as entering generic or incorrect data. 85 The knowledge base of CDSS is dependent on a centralized, large clinical data repository. Quality of data can affect quality of decision support. If data collection or input into the system is unstandardized, the data is effectively corrupted. You may design a system for use at the point-of-care, but when applied to real world environments and data, will not be utilized properly. The importance of using informational standards such as ICD, SNOMED, and others, cannot be understated.

Lack of transportability and interoperability

Despite ongoing development for the better part of three decades, CDSS (and even EHRs in general) suffer from interoperability issues. Many CDSS exist as cumbersome stand-alone systems, or exist in a system that cannot communicate effectively with other systems.

What makes transportability so difficult to achieve? Beyond programming complexities that can make integration difficult, the diversity of clinical data sources is a challenge. 96 There is a reluctance or perceived risk associated with transporting sensitive patient information. Positively, interoperability standards are continuously being developed and improved, such as Health Level 7 (HL7) and Fast Healthcare Interoperability Resources (FHIR). These are already being utilized in commercial EHR vendors. 97 Several government agencies, medical organizations and informatics bodies are actively supporting and some even mandating the use of these interoperability standards in health systems. 98 , 99 , 100

The cloud also offers a potential solution to interoperability (and other EHR ailments such as data sync, software updating, etc. 101 ). Cloud EHRs have open architecture, newer standards, and more flexible connectivity to other systems. 102 It is also a common misconception that data stored on a cloud is more vulnerable. This is not necessarily true. Web-based EHRs are required to store data in high-level storage centers with advanced encryption and other safeguards. They must comply with national data security standards including the Health Insurance Portability and Accountability Act (HIPAA) in the USA, Personal Information Protection and Electronic Documents Act (PIPEDA) in Canada, or the Data Protection Directive and General Data Protection Regulation (GDPR) in Europe, to name a few. 103 They can be just as safe (or just as vulnerable) as traditional, server-based architecture. 103 In fact, there are often fewer people who have access to unencrypted data in cloud storage centers vs. server-based records. 103

Financial challenges

Up to 74% of those with a CDSS said that financial viability remains a struggle. 104 Outset costs to set up and integrate new systems can be substantial. Ongoing costs can continue to be an issue indefinitely as new staff need to be trained to use the system, and system updates are required to keep pace with current knowledge.

Results from cost analyses of CDSS implementations are mixed, controversial, and sparse. 105 , 106 , 107 , 108 Whether an intervention is cost-effective depends on a wide range of factors, including those specific to the environment, both political and technological. 105 Cost benefit assessment in itself can be limited, with challenges such as a lack of standardized metrics. 107 This is an emerging research area and much work needs to be done to advance our understanding of the financial effects of CDSS.

CDSS have been shown to augment healthcare providers in a variety of decisions and patient care tasks, and today they actively and ubiquitously support delivery of quality care. Some applications of CDSS have more evidence behind them, especially those based on CPOE. Support for CDSS continues to mount in the age of the electronic medical record, and there are still more advances to be made including interoperability, speed and ease of deployment, and affordability. At the same time, we must stay vigilant for potential downfalls of CDSS, which range from simply not working and wasting resources, to fatiguing providers and compromising quality of patient care. Extra precautions and conscientious design must be taken when building, implementing, and maintaining CDSS. A portion of these considerations were covered in this review, but further review will be required in practice, especially as CDSS continue to evolve in complexity through advances in AI, interoperability, and new sources of data.

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Sutton, R.T., Pincock, D., Baumgart, D.C. et al. An overview of clinical decision support systems: benefits, risks, and strategies for success. npj Digit. Med. 3 , 17 (2020). https://doi.org/10.1038/s41746-020-0221-y

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The Role and Importance of Decision Support Systems Essay (Critical Writing)

Short review of the company, using the system to evaluate the proposed level production plan, using the system to assist in annual planning and budgeting, factors for success.

Over the recent years, decision support systems (DSS) have appeared as a new tool for supporting and improving administrative decision-making. A DSS is an interactive computer-based system that offers easy access to choice models and assignments to sustain semi-structured and uncontrolled decision-making tasks. The gradually growing interest in DSS is advanced by supervisors familiar with the accessibility and potentials of this technology.

A DSS creator is a “package” of associated hard and soft that can be used to expand a definite DSS. It naturally holds capabilities such as information and data administration, graphic demonstration, economic and statistical study routines, and optimization analysis-all capabilities that have been obtainable independently for some time, but only lately as an incorporated easy-to-use set of tools. While large company have reclined more upon DSS for successful decision taking, small businesses have practically disregarded this new technology.

The case study offers the investigation of DSS use in some small business company, engaged in the production of plastic toys.

The toy manufacturing is a highly competitive commerce. The sales volumes of the corporation consisted $3 million in 1982, and were planned to be increased up to $3.6 million in 1983. Over 80 percent of annual financial turnover was raised during the August-November time. The company’s manufacturing plan had been seasonal.

In 1982, the company’s production manager became extremely bothered by the issues that arose not only from manufacture planning but also from ultimately falling incomes. Seasonal extension and reduction of the staff resulted in employment troubles as well as high training and quality control expenditure. Equipment, inactive for several months, was unexpectedly subjected to intense use. Speeded up manufacturing plans resulted in regular system modifications on technical equipment and perplexity in planning runs, which reasoned inefficiencies in assemblage and wrapping.

For these grounds, the production manager had recommended the president to assume a strategy of level monthly manufacturing in 1983, indicating that estimations of trade volumes had been steadfast in the earlier periods, and that acquisition terms would not be influenced by the rescheduling of purchases.

The president became aware that an obvious development of manufacture efficiency could result from production levels, but he was hesitant on the issue what the impact on other stages of the industry might be. Predominantly, he was anxious about the effect that productivity level might have on the firm’s prerequisites for finances in 1983. To concentrate on this issue, the production manager offered that ABC needs to develop a computer based DSS to study the financial suggestions of the level production plan.

To correspond acknowledged needs it was determined to elaborate a computer-based DSS that could be implemented into the assistance in yearly planning, to estimate the financial insinuations of the proposed manufacturing level plan, and to assist in cash financial planning.

As it was estimated, net profit ($174,000) gained under the manufacturing level plan is considerably higher than that acquired implementing of the seasonal production plan ($108,000). The monthly documented balance, consequential from the existing manufacturing plan shows that the only noticeable regular financing obligation throughout the year will be a swelling in receivables for the duration of the collection wrap after the peak sales months. The largest expected bank borrowing of $657,000 happens in November, well within the $850,000 credit limit.

The firm’s economic position in July becomes even more dangerous since its existing benefits are contained predominately of inventories, while by September, the increase of accounts receivable is extensive. The risk that takes into account incomes will be uncollectible is much less than the risk that supplies may not be salable, mainly in the toy industry where costs and trends vary sharply in the off-season. The DSS system allows administration to weigh the augmented profit available under a manufacturing level plan against the risk of augmented inventory investment and the complexity of attaining sufficient financing.

The assessment of this kind of trade-off in creation of a production plan resolution is significant when one believes the fact that for a small-scale industry, liquidity and survival are synonymous. Because of predictable complexity in finding sufficient financing, the president ultimately settled on against the level production plan. Nevertheless, the two manufacturing plans appraised above characterization only two extremes of a range of probable substitute manufacturing plans. The administration of ABC Co. realized that the system can be used to decide the most attractive manufacturing plan under the restriction of predictable economic circumstances.

Monthly sales estimates and manufacturing plans are the two main contributions to the process of raising a chain of standard statements. In their annual development procedure, ABC’s administration can use the DSS to test the economic impact of different manufacturing strategies grounded upon an agreed sales estimate. These systems may be created by different groupings of level and seasonal manufacture.

For each manufacturing policy, the model is run by using the “what-if’ characteristic to create a set of monthly pro forma reports. Administration can inspect and analyze the economic influence of these strategies, mainly with value of the necessity for exterior financing. This procedure is duplicated until management is pleased with the projected values on the reports.

Aspects that seem to have donated to the achievement of this scheme are:

• the necessity for a system to sustain top managing decision-making was well recognized;
• top management must support the system from its foundation;
• the production manager needs to serve as the catalyst;
• most important consumers (the vice president of economics and the production manager) vigorously contributed in the actual achievement of the system;
• use of a DSS creator such as IFPS to develop the system.

It should be underlined that a DSS needs to be developed over time in order to better correspond to the requirements of the decision makers and the enterprise. As such, the system has to be updated or modified as the requirements of the users become better recognized or the executive surroundings changes significantly.

Alter, S. L. (1980). Decision support systems: current practice and continuing challenges. Reading, Mass., Addison-Wesley Pub.

Druzdzel, M. J. and R. R. Flynn (1999). Decision Support Systems. Encyclopedia of Library and Information Science. A. Kent, Marcel Dekker, Inc.

Finlay, P. N. (1994). Introducing decision support systems. Oxford, UK Cambridge, Mass., NCC Blackwell; Blackwell Publishers.

Kuang-Chian, C. (1989). Developing Decision Support Systems for Small Business Management: A Case Study. Journal of Small Business Management, 27 (3), 11

Pollock, C., & Kanachowski, A. (1993). Application of Theories of Decision Making to Group Decision Support Systems (GDSS). International Journal of Human-Computer Interaction, 5 (1), 71-94.

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