Clinical decision support system (CDSS) – effects on care quality

Interventions to promote the implementation of pressure injury prevention measures in nursing homes: A scoping review

AIMS

To identify studies and the content of the interventions that have facilitated the implementation of pressure injury (PI) prevention measures in nursing home settings.

DESIGN AND METHOD

A scoping review methodology was employed. The author has carried out the following steps successively: Identified this scoping review's questions, retrieved potentially relevant studies, selected relevant studies, charted the data, summarised the results, and consulted with stakeholders from nursing homes in China.

DATA SOURCES

Six electronic databases and three resources of grey literature-PubMed, CINAHL, Web of Science Core Collection, Embase, Cochrane Central Register of Controlled Trials, Psych INFO, Open Grey, MedNar, ProQuest Dissertations, and Theses Full Texts were searched from January 2002 through May 2022.

RESULTS

Forty articles were included, among which the primary interventions were quality improvement, training and education, evidence-based practice, device-assisted PI prophylaxis, nursing protocols, and clinical decision support systems. Twenty-three outcome indicators were summarised in 40 articles, which included 10 outcome indicators, seven process indicators, and six structural indicators. Furthermore, only five articles reported barriers in the process of implementing interventions.

CONCLUSION

The common interventions to promote the implementation of PI prevention measures in nursing homes are quality improvement, training, and education. Relatively limited research has been conducted on evidence-based practice, clinical decision support systems, device-assisted PI prophylaxis, and nursing protocols. In addition, there is a paucity of studies examining the impediments to implementing these measures and devising targeted solutions. Therefore, it is recommended that future studies include analysis and reporting of barriers and facilitators as part of the article to improve the sustainability of the intervention.

IMPACT

This article reminds nursing home managers that they should realise the importance of implementation strategies between the best evidence of PI prevention and clinical practice. Also, this review provides the types, contents, and outcome indicators of these strategies for managers of nursing homes to consider what types of interventions to implement in their organisations.

TRIAL AND PROTOCOL REGISTRATION

The protocol of this scoping review was published as an open-access article in June 2022 (Yang et al., 2022).

Clinical decision support systems for diabetic foot ulcers: a scoping review

OBJECTIVE

Map the scientific evidence on the use of clinical decision support systems in diabetic foot care.

METHOD

A scoping review based on the JBI Manual for Evidence Synthesis and registered on the Open Science Framework platform. Searches were carried out in primary and secondary sources on prototypes and computerized tools aimed at assisting patients with diabetic foot or at risk of having it, published in any language or period, in eleven databases and grey literature.

RESULTS

A total of 710 studies were identified and, following the eligibility criteria, 23 were selected, which portrayed the use of decision support systems in diabetic foot screening, predicting the risk of ulcers and amputations, classifying the stage of severity, deciding on the treatment plan, and evaluating the effectiveness of interventions, by processing data relating to clinical and sociodemographic information.

CONCLUSION

Expert systems stand out for their satisfactory results, with high precision and sensitivity when it comes to guiding and qualifying the decision-making process in diabetic foot prevention and care.

Recent advancement in integrating artificial intelligence and information technology with real-world data for clinical decision-making in China: A scoping review

OBJECTIVE

Striking innovations and advancements have been achieved with the use of artificial intelligence and healthcare information technology being integrated into clinical real-world data. The current scoping review aimed to provide an overview of the current status of artificial intelligence-/information technology-based clinical decision support tools in China.

METHODS

PubMed/MEDLINE, Embase, China National Knowledge Internet, and Wanfang data were searched for both English and Chinese literature. The gray literature search was conducted for commercially available tools. Original studies that focused on clinical decision support tools driven by artificial intelligence or information technology in China and were published between 2010 and February 2022 were included. Information extracted from each article was further synthesized by themes based on three types of clinical decision-making.

RESULTS

A total of 37 peer-reviewed publications and 13 commercially available tools were included in the final analysis. Among them, 32.0% were developed for disease diagnosis, 54.0% for risk prediction and classification, and 14.0% for disease management. Chronic diseases were the most popular therapeutic areas of exploration, with particular emphasis on cardiovascular and cerebrovascular diseases. Single-center electronic medical records were the mainstream data sources leveraged to inform clinical decision-making, with internal validation being predominately used for model evaluation.

CONCLUSIONS

To effectively promote the extensive use of real-world data and drive a paradigm shift in clinical decision-making in China, multidisciplinary collaboration of key stakeholders is urgently needed.

A novel, evidence-based, comprehensive clinical decision support system improves outcomes for patients with traumatic rib fractures

BACKGROUND

Traumatic rib fractures are associated with high morbidity and mortality. Clinical decision support systems (CDSS) have been shown to improve adherence to evidence-based (EB) practice and improve clinical outcomes. The objective of this study was to investigate if a rib fracture CDSS reduced hospital length of stay (LOS), 90-day and 1-year mortality, unplanned ICU transfer, and the need for mechanical ventilation. The independent association of two process measures, an admission EB order set and a pain-inspiratory-cough score early warning system, with LOS were investigated.

METHODS

The CDSS was scaled across nine US trauma centers. Following multiple imputation, multivariable regression models were fit to evaluate the association of the CDSS on primary and secondary outcomes. As a sensitivity analysis, propensity score matching was also performed to confirm regression findings.

RESULTS

Overall, 3,279 patients met inclusion criteria. Rates of EB practices increased following implementation. On risk-adjusted analysis, in-hospital LOS preintervention versus postintervention was unchanged (incidence rate ratio [IRR], 1.06; 95% confidence interval [CI], 0.97-1.15, p = 0.2) but unplanned transfer to the ICU was reduced (odds ratio, 0.28; 95% CI, 0.09-0.84, p = 0.024), as was 1-year mortality (hazard ratio, 0.6; 95% CI, 0.4-0.89, p = 0.01). Provider utilization of the admission order bundle was 45.3%. Utilization was associated with significantly reduced LOS (IRR, 0.87; 95% CI, 0.77-0.98; p = 0.019). The early warning system triggered on 34.4% of patients; however, was not associated with a significant reduction in hospital LOS (IRR, 0.76; 95% CI, 0.55-1.06; p = 0.1).

CONCLUSION

A novel, user-centered, comprehensive CDSS improves adherence to EB practice and is associated with a significant reduction in unplanned ICU admissions and possibly mortality, but not hospital LOS.

LEVEL OF EVIDENCE

Therapeutic/Care Management; Level III.

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

Among the technology-based solutions, clinical decision support systems (CDSSs) have the ability to keep up with clinicians with the latest evidence in a smart way. Hence, the main objective of our study was to investigate the applicability and characteristics of CDSSs regarding chronic disease. The Web of Science, Scopus, OVID, and PubMed databases were searched using keywords from January 2000 to February 2023. The review was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Then, an analysis was done to determine the characteristics and applicability of CDSSs. The quality of the appraisal was assessed using the Mixed Methods Appraisal Tool checklist (MMAT). A systematic database search yielded 206 citations. Eventually, 38 articles from sixteen countries met the inclusion criteria and were accepted for final analysis. The main approaches of all studies can be classified into adherence to evidence-based medicine (84.2%), early and accurate diagnosis (81.6%), identifying high-risk patients (50%), preventing medical errors (47.4%), providing up-to-date information to healthcare providers (36.8%), providing patient care remotely (21.1%), and standardizing care (71.1%). The most common features among the knowledge-based CDSSs included providing guidance and advice for physicians (92.11%), generating patient-specific recommendations (84.21%), integrating into electronic medical records (60.53%), and using alerts or reminders (60.53%). Among thirteen different methods to translate the knowledge of evidence into machine-interpretable knowledge, 34.21% of studies utilized the rule-based logic technique while 26.32% of studies used rule-based decision tree modeling. For CDSS development and translating knowledge, diverse methods and techniques were applied. Therefore, the development of a standard framework for the development of knowledge-based decision support systems should be considered by informaticians.

A roadmap for the development and evaluation of the eHealthResp online course

Background

Inappropriate antibiotic use constitutes one of the most concerning public health issues, being one of the main causes of antibiotic resistance. Hence, to tackle this issue, it is important to encourage the development of educational interventions for health practitioners, namely by using digital health tools. This study focuses on the description of the development and validation process of the eHealthResp online course, a web platform directed to physicians and pharmacists, with the overall goal of improving antibiotic use for respiratory tract infections, along with the assessment of its usability.

Methods

The eHealthResp platform and the courses, developed with a user-centered design and based on Wordpress and MySQL, were based on a previously developed online course. A questionnaire to assess the usability was distributed among physicians (n = 6) and pharmacists (n = 6). Based on the obtained results, statistical analyses were conducted to calculate the usability score and appraise the design of the online course, as well as to compare the overall scores attributed by both groups. Further qualitative comments provided by the participants have also been analyzed.

Results

The eHealthResp contains two online courses directed to physicians and pharmacists aiming to aid in the management of respiratory tract infections. The average usability score of the eHealthResp online courses for physicians and pharmacists was of 78.33 (±11.57, 95%CI), and 83.75 (±15.90, 95%CI), respectively. Qualitative feedback emphasized the usefulness of the course, including overall positive reviews regarding user-friendliness and consistency.

Conclusions

This study led us to conclude that the eHealthResp online course is not recognized as a complex web platform, as both qualitative and quantitative feedback obtained were globally positive.

Validation of the eHealthResp online course for pharmacists and physicians: A Delphi method approach

FRAMEWORK

The inappropriate use of antibiotics for respiratory tract infections is dispersed worldwide, thus being a strong contributor to antibiotic resistances. As the use of educational interventions among health practitioners is shown to have an impact on judicious antibiotic use, an online course (eHealthResp) has been developed, especially targeted to pharmacists and physicians. Thus, the main goal of this study is to validate the contents of the online course eHealthResp.

METHODS

This two-round Delphi study involved the recruitment of a multidisciplinary panel (n = 19), to which the questionnaires of the first round were sent. After the first round, a report summing up the results has been forwarded to the panel, along with a new, reformulated version of the questionnaire.

RESULTS

After the two rounds of the Delphi process, consensus was evaluated. Six clinical cases and fifty-one treatments obtained minor consensus [60-75%] or full consensus (≥75%). The question on antibiotic practice has obtained a consensus >90% on both rounds.

CONCLUSIONS

The validation of the contents based on experts' consensus has been an essential approach to improve eHealthResp's online course, as valuable feedback has been provided by the panel on both rounds.

E-Health Tools to Improve Antibiotic Use and Resistances: A Systematic Review

(1) Background: e-Health tools, especially in the form of clinical decision support systems (CDSSs), have been emerging more quickly than ever before. The main objective of this systematic review is to assess the influence of these tools on antibiotic use for respiratory tract infections. (2) Methods: The scientific databases, MEDLINE-PubMed and EMBASE, were searched. The search was conducted by two independent researchers. The search strategy was mainly designed to identify relevant studies on the effectiveness of CDSSs in improving antibiotic use, as a primary outcome, and on the acceptability and usability of CDSSs, as a secondary outcome. (3) Results: After the selection, 22 articles were included. The outcomes were grouped either into antibiotics prescription practices or adherence to guidelines concerning antibiotics prescription. Overall, 15 out of the 22 studies had statistically significant outcomes related to the interventions. (4) Conclusions: Overall, the results show a positive impact on the prescription and conscientious use of antibiotics for respiratory tract infections, both with respect to patients and prescribing healthcare professionals. CDSSs have been shown to have great potential as powerful tools for improving both clinical care and patient outcomes.

A unique framework for the Persian clinical guidelines: addressing an evidence-based CDSS development need

BACKGROUND AND AIM

One of the prerequisites to develop Computerised Decision Support Systems is Clinical Practice Guidelines (CPGs) which provide a systematic aid to make complex medical decisions. In order to provide an automated CPG, it is needed to have a unique structure for the CPGs. This study aims to propose a unique framework for the Persian guidelines.

MATERIALS AND METHODS

20 Persian CPGs were selected and divided into the creation and validation sets (n=10 for each). The first group was studied independently and their headings were listed; wherever possible, the headings were merged into a new heading that was applicable to all the guidelines. The developed framework was validated by the second group of the guidelines.

RESULTS

Studied guidelines had a very heterogeneous structure. The number of original headings was 249; they were reduced to 14 main headings with 16 subheadings in a unique developed framework. The framework is able to represent and cover 100% of the guidelines.

CONCLUSION

The heterogeneity of guidelines was high as they were not developed based on the unique framework. The proposed framework provides a layout for designing the CPGs with a homogeneous structure. Guideline developers can use this framework to develop structured CPGs. This will facilitate the integration of the guidelines into electronic medical records as well as clinical decision support systems.

Self-directed learning using clinical decision support: costs and outcomes

It is an axiom of research into cost and value in education that the way to deliver low cost education is to save on faculty cost, as this makes up the majority of the costs in most educational programmes. Thus, any savings that can be made to faculty costs will make a significant contribution to reducing overall cost. But this may not be the case with all programmes, or with all methods of learning. In particular, it may not be the case with self-directed learning. The cost of self-directed learning can be divided up into cost categories just like any other form of learning. As such, it will be made up of faculty cost, learner costs, infrastructure and facility costs, content costs and technology costs. However, in postgraduate education, these costs are largely the same as those that account for clinical care, as postgraduate learners learn as they work. To get maximum value from self-directed learning that will support clinical decisions, learners will need to have access to excellent clinical decision support resources that will facilitate self-directed learning.

Impact of a hybrid decision support system to improve the adherence to implantable cardioverter defibrillator therapy guidelines for primary prevention of sudden cardiac death

OBJECTIVES

Despite the well-documented benefit of implantable cardioverter defibrillator (ICD) in patients with severe left ventricular dysfunction, there is a large number of patients who had not been offered this therapy. The aim of this study is to evaluate the utility of a hybrid decision support system (hCDSS) to improve the adherence to indicate ICD therapy in our institution.

METHODS

We conducted a retrospective, observational and single-center study. An hCDSS focused on patients with severe deterioration of the left ventricular function was implemented, creating a mandatory field containing the value of left ejection fraction and three options to choose: >35%, ≤ 35% or unknown. When the option ≤ 35% is checked, an email is automatically sent to the electrophysiology section where the staff can contact the treating physician to discuss the indication of ICD therapy. We measured the number of ICDs implanted before the alert (month 1-21), immediate post and late post alert (month 22-27 and 28-48 months respectively) RESULTS: The rate of ICD implantation increased from 1.76% per month in the pre-intervention period to 4.48% after the intervention (p < 0.001). This increase in the rate of ICD implantation remained stable between the immediate and late post-intervention period (4.6 vs. 4.4; p = .8) CONCLUSION: The implementation of a hybrid decision support system was associated with improved adherence to clinical guidelines for prevention of sudden cardiac death, as evidenced by a rapid and sustained increase in the number of ICD implants in patients with severe left ventricular dysfunction.

Sustained return to work and work participation after a new legislation obligating employers to notify prolonged sickness absence

AIMS

Return to work (RTW) after prolonged sickness absence benefits both the individual and society. However, the effectiveness of legislation aiming to improve RTW remains uncertain. We examined whether sustained RTW and work participation were different before and after a legislative change enacted in 2012 (i.e. an intervention) that obligated employers to give notice of prolonged sickness absence to occupational health services.

METHODS

Two random samples (2010 and 2013) of the Finnish working aged population (70%, ~2.6 million each) were drawn. Using survival analysis, we assessed sustained RTW (≥28 consecutive working days) during a two-month follow-up after a sickness absence minimum of 30 calendar days in the pre- and post-intervention period. We also identified pathways for RTW with cluster analysis and calculated relative gain in work participation in the total sample and by several population subgroups.

RESULTS

In the total sample, sustained RTW was 4% higher and the mean time to sustained RTW was 0.42 days shorter in the post- than in the pre-intervention period. The estimates were larger among women than men and among those with mental disorders compared with other diagnoses. Changes in the pathways for sustained RTW indicated a 4.9% relative gain in work participation in the total sample. The gain was larger among those who lived in areas of low unemployment rate (20.6%) or worked in the public sector (11.9%).

CONCLUSIONS

From 2010 to 2013, RTW and work participation increased among the employees with prolonged sickness absence, suggesting that the legislative change enhanced RTW. The change in work participation varied by population subgroup.

Computer-generated reminders delivered on paper to healthcare professionals: effects on professional practice and healthcare outcomes

BACKGROUND

Clinical practice does not always reflect best practice and evidence, partly because of unconscious acts of omission, information overload, or inaccessible information. Reminders may help clinicians overcome these problems by prompting them to recall information that they already know or would be expected to know and by providing information or guidance in a more accessible and relevant format, at a particularly appropriate time. This is an update of a previously published review.

OBJECTIVES

To evaluate the effects of reminders automatically generated through a computerized system (computer-generated) and delivered on paper to healthcare professionals on quality of care (outcomes related to healthcare professionals' practice) and patient outcomes (outcomes related to patients' health condition).

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, six other databases and two trials registers up to 21 September 2016 together with reference checking, citation searching and contact with study authors to identify additional studies.

SELECTION CRITERIA

We included individual- or cluster-randomized and non-randomized trials that evaluated the impact of computer-generated reminders delivered on paper to healthcare professionals, alone (single-component intervention) or in addition to one or more co-interventions (multi-component intervention), compared with usual care or the co-intervention(s) without the reminder component.

DATA COLLECTION AND ANALYSIS

Review authors working in pairs independently screened studies for eligibility and abstracted data. For each study, we extracted the primary outcome when it was defined or calculated the median effect size across all reported outcomes. We then calculated the median improvement and interquartile range (IQR) across included studies using the primary outcome or median outcome as representative outcome. We assessed the certainty of the evidence according to the GRADE approach.

MAIN RESULTS

We identified 35 studies (30 randomized trials and five non-randomized trials) and analyzed 34 studies (40 comparisons). Twenty-nine studies took place in the USA and six studies took place in Canada, France, Israel, and Kenya. All studies except two took place in outpatient care. Reminders were aimed at enhancing compliance with preventive guidelines (e.g. cancer screening tests, vaccination) in half the studies and at enhancing compliance with disease management guidelines for acute or chronic conditions (e.g. annual follow-ups, laboratory tests, medication adjustment, counseling) in the other half.Computer-generated reminders delivered on paper to healthcare professionals, alone or in addition to co-intervention(s), probably improves quality of care slightly compared with usual care or the co-intervention(s) without the reminder component (median improvement 6.8% (IQR: 3.8% to 17.5%); 34 studies (40 comparisons); moderate-certainty evidence).Computer-generated reminders delivered on paper to healthcare professionals alone (single-component intervention) probably improves quality of care compared with usual care (median improvement 11.0% (IQR 5.4% to 20.0%); 27 studies (27 comparisons); moderate-certainty evidence). Adding computer-generated reminders delivered on paper to healthcare professionals to one or more co-interventions (multi-component intervention) probably improves quality of care slightly compared with the co-intervention(s) without the reminder component (median improvement 4.0% (IQR 3.0% to 6.0%); 11 studies (13 comparisons); moderate-certainty evidence).We are uncertain whether reminders, alone or in addition to co-intervention(s), improve patient outcomes as the certainty of the evidence is very low (n = 6 studies (seven comparisons)). None of the included studies reported outcomes related to harms or adverse effects of the intervention.

AUTHORS' CONCLUSIONS

There is moderate-certainty evidence that computer-generated reminders delivered on paper to healthcare professionals probably slightly improves quality of care, in terms of compliance with preventive guidelines and compliance with disease management guidelines. It is uncertain whether reminders improve patient outcomes because the certainty of the evidence is very low. The heterogeneity of the reminder interventions included in this review also suggests that reminders can probably improve quality of care in various settings under various conditions.

Generating Individual Patient Preferences for the Treatment of Osteoarthritis Using Adaptive Choice-Based Conjoint (ACBC) Analysis

INTRODUCTION

To explore how adaptive choice-based conjoint (ACBC) analysis could contribute to shared decision-making in the treatment of individual patients with osteoarthritis (OA).

METHODS

In-depth case study of three individuals randomly selected from patients with OA participating in an ACBC analysis exercise. Eleven members of a research users' group participated in developing an ACBC task on medication preferences for OA. Individual medication priorities are illustrated by the detailed analysis of ACBC output from three randomly selected patients from the main sample.

RESULTS

The case study analysis illustrates individual preferences. Participant 1's priority was avoidance of the four high-risk side effects of medication, which accounted for 90% of the importance of all attributes, while the remaining attributes (expected benefit; way of taking medication; frequency; availability) accounted only for 10% of the total influence. Participant 3 was similar to participant 1 but would accept a high risk of one of the side effects if the medication were available by prescription. In contrast, participant 2's priority was the avoidance of Internet purchase of medication; this attribute (availability) accounted for 52% of the importance of all attributes.

CONCLUSIONS

Individual patients have preferences that likely lead to different medication choices. ACBC has the potential as a tool for physicians to identify individual patient preferences as a practical basis for concordant prescribing for OA in clinical practice. Future research needs to establish whether accurate knowledge of individual patient preferences for treatment attributes and levels translates into concordant behavior in clinical practice.

Design and Development of a Sharable Clinical Decision Support System Based on a Semantic Web Service Framework

Clinical decision support (CDS) systems provide clinicians and other health care stakeholders with patient-specific assessments or recommendations to aid in the clinical decision-making process. Despite their demonstrated potential for improving health care quality, the widespread availability of CDS systems has been limited mainly by the difficulty and cost of sharing CDS knowledge among heterogeneous healthcare information systems. The purpose of this study was to design and develop a sharable clinical decision support (S-CDS) system that meets this challenge. The fundamental knowledge base consists of independent and reusable knowledge modules (KMs) to meet core CDS needs, wherein each KM is semantically well defined based on the standard information model, terminologies, and representation formalisms. A semantic web service framework was developed to identify, access, and leverage these KMs across diverse CDS applications and care settings. The S-CDS system has been validated in two distinct client CDS applications. Model-level evaluation results confirmed coherent knowledge representation. Application-level evaluation results reached an overall accuracy of 98.66 % and a completeness of 96.98 %. The evaluation results demonstrated the technical feasibility and application prospect of our approach. Compared with other CDS engineering efforts, our approach facilitates system development and implementation and improves system maintainability, scalability and efficiency, which contribute to the widespread adoption of effective CDS within the healthcare domain.

eHealth: past and future perspectives

eHealth is an umbrella term incorporating any area that combines healthcare and technology to improve efficiencies and reduce costs. The ultimate goal of eHealth is to rationalize treatment selection to improve patient safety and outcomes. Telemedicine, first used in the 1920s, is the oldest form of eHealth. The introduction of broadband Internet, followed by wireless technologies, has allowed an explosion of mHealth applications within this field. Wearable technologies, such as smartwatches, are now being used for diagnostics and patient monitoring. Challenges remain to develop reusable Clinical Decision Support systems that will streamline the flow of data from clinical laboratories to point of care. This review explores the history of eHealth, and describes some of the remaining integration and implementation challenges.

A Bridging Opportunities Work-frame to develop mobile applications for clinical decision making

Background:

Mobile applications (apps) providing clinical decision support (CDS) may show the greatest promise when created by and for frontline clinicians. Our aim was to create a generic model enabling healthcare providers to direct the development of CDS apps.

Methods:

We combined Change Management with a three-tier information technology architecture to stimulate CDS app development.

Results:

A Bridging Opportunities Work-frame model was developed. A test case was used to successfully develop an app.

Conclusion:

Healthcare providers can re-use this globally applicable model to actively create and manage regional decision support applications to translate evidence-based medicine in the use of emerging medication or novel treatment regimens.

Medical information needs to be structured in a way that it can be used in a time-constrained environment. This project looked at the process of creating mobile applications (apps), to help medical professionals rapidly apply new knowledge to better treat patients. We developed a novel system that allowed medical professionals to have a leading role in development. With the input from a pharmacist we created an app to deal with pharmacy and patient-related decisions surrounding newly available anticancer pills. Other medical professionals could also use this method to make apps to provide information with relevance to their medical decisions.

Understanding information synthesis in oral surgery for the design of systems for clinical information technology

An understanding of the processes of clinical decision-making is essential for the development of health information technology. In this study we have analysed the acquisition of information during decision-making in oral surgery, and analysed cognitive tasks using a "think-aloud" protocol. We studied the techniques of processing information that were used by novices and experts as they completed 4 oral surgical cases modelled from data obtained from electronic hospital records. We studied 2 phases of an oral surgeon's preoperative practice including the "diagnosis and planning of treatment" and "preparing for a procedure". A framework analysis approach was used to analyse the qualitative data, and a descriptive statistical analysis was made of the quantitative data. The results showed that novice surgeons used hypotheticodeductive reasoning, whereas experts recognised patterns to diagnose and manage patients. Novices provided less detail when they prepared for a procedure. Concepts regarding "signs", "importance", "decisions", and "process" occurred most often during acquisition of information by both novices and experts. Based on these results, we formulated recommendations for the design of clinical information technology that would help to improve the acquisition of clinical information required by oral surgeons at all levels of expertise in their clinical decision-making.