A Clinical Decision Support System (KNOWBED) to Integrate Scientific Knowledge at the Bedside: Development and Evaluation Study

Abstract

Background The evidence-based medicine (EBM) paradigm requires the development of health care professionals’ skills in the efficient search of evidence in the literature, and in the application of formal rules to evaluate this evidence. Incorporating this methodology into the decision-making routine of clinical practice will improve the patients’ health care, increase patient safety, and optimize resources use. Objective The aim of this study is to develop and evaluate a new tool (KNOWBED system) as a clinical decision support system to support scientific knowledge, enabling health care professionals to quickly carry out decision-making processes based on EBM during their routine clinical practice. Methods Two components integrate the KNOWBED system: a web-based knowledge station and a mobile app. A use case (bronchiolitis pathology) was selected to validate the KNOWBED system in the context of the Paediatrics Unit of the Virgen Macarena University Hospital (Seville, Spain). The validation was covered in a 3-month pilot using 2 indicators: usability and efficacy. Results The KNOWBED system has been designed, developed, and validated to support clinical decision making in mobility based on standards that have been incorporated into the routine clinical practice of health care professionals. Using this tool, health care professionals can consult existing scientific knowledge at the bedside, and access recommendations of clinical protocols established based on EBM. During the pilot project, 15 health care professionals participated and accessed the system for a total of 59 times. Conclusions The KNOWBED system is a useful and innovative tool for health care professionals. The usability surveys filled in by the system users highlight that it is easy to access the knowledge base. This paper also sets out some improvements to be made in the future.

The Application of Knowledge-Based Clinical Decision Support Systems to Enhance Adherence to Evidence-Based Medicine in Chronic Disease

“…These various methods and their frequencies were categorized into 13 categories which are represented in Table 2 . As it is apparent, 34.21% of studies utilized ruled-based logic techniques [ 21 , 22 , 25 – 28 , 30 , 31 , 34 , 37 , 40 , 41 , 56 ] while 26.32% of studies used rule-based decision tree modeling [ 36 , 42 – 44 , 46 , 47 , 57 ] to convert the clinical knowledge into CDSSs in the form of computerized systems.…”

“…Knowledge-based decision support systems are generally developed to improve the quality of patient care, prevent unwanted medical errors, and provide timely decision-making. The users of developed systems comprise only physicians in 31 (81.15%) studies [ 14 , 15 , 19 , 20 , 22 – 24 , 26 – 29 , 32 – 34 , 36 , 39 – 44 , 46 , 47 , 56 ], only nurses or team care members in two studies (5.26%) [ 21 , 35 ], and both healthcare providers and patients in four studies (10.53%) [ 25 , 30 , 31 , 37 , 38 , 45 ].…”

Experiences and perceptions of final‐year nursing students of using a chatbot in a simulated emergency situation: A qualitative study

“…This could be explained by the fact that the most current software available for implementing conversational agents is fee-based and thus not cost-effective to maintain in clinical practice (Barthelmäs et al, 2021). Surely, some participants mentioned the usefulness and beneficial effects that such advances may have in promoting evidence-based clinical decisionmaking at the bedside, regardless this technology is not currently present in their actual clinical placements (Martinez-Garcia et al, 2021).…”

“…This could be explained by the fact that the most current software available for implementing conversational agents is fee‐based and thus not cost‐effective to maintain in clinical practice (Barthelmäs et al, 2021 ). Surely, some participants mentioned the usefulness and beneficial effects that such advances may have in promoting evidence‐based clinical decision‐making at the bedside, regardless this technology is not currently present in their actual clinical placements (Martinez‐Garcia et al, 2021 ). Emerging information and communication technologies, such as AI‐based conversational agents, may not only contribute in better patient safety judgements and decisions but also introduce new avenues for higher organizational safety culture values (Akbar et al, 2021 ).…”

Toward Enhanced Clinical Decision Support for Patients Undergoing a Hip or Knee Replacement: Focus Group and Interview Study With Surgeons

“…The study departs from previous literature on the use of data in clinical decision support, which is largely focused on data from clinical environments such as intensive care [ 26 , 27 ]. To date, studies that have presented data from community settings to surgeons have focused principally on manually clinician-reported data [ 28 ] and laboratory outcomes [ 29 ], such as those commonly stored in electronic health records, or patient self-reported data [ 30 ] such as PROMs [ 31 ]. Where sensor data are sampled, this is often at a relatively low sample rate (eg, a daily measurement or 12 measurements per day) or over a relatively short period, from a few minutes [ 32 ] to a week or month [ 33 ].…”