Contributions from the 2017 Literature on Clinical Decision Support

Effect of a computerized decision support system on the treatment approach of stage III or IV pressure injury in patients with spinal cord injury: a feasibility study

BACKGROUND

Stage III and IV pressure injuries (PIs) in patients with spinal cord injury (SCI) require complex interdisciplinary and interprofessional treatment approaches that are difficult to implement. Practical aspects, such as information exchange and coordination, remain challenging. We investigated whether a computerized decision support system (CDSS) could increase treatment adherence and improve clinical outcomes and interprofessional collaboration.

METHOD

In this feasibility study, a core team developed the initial treatment process and adapted it based on several discussions with clinical experts and information technologists. The CDSS followed the Basel Decubitus Approach and was used in a clinic specializing in SCI. Thirty patients with SCI admitted for stage III/IV PI between July 2016 and May 2017 were randomly allocated to standard or CDSS-supported care. Between-group differences in treatment adherence, complication rates, length of stay, and costs were analyzed using descriptive statistics. The use of the CDSS and potential barriers and facilitators were evaluated through interprofessional focus groups, transcribed verbatim, and thematically analyzed (30 participants).

RESULTS

No differences in SCI characteristics, comorbidities, or PI characteristics (localization: ischium [number (n) = 19 PI, 63%], sacrum [n = 10 PI, 33%], recurrent PI [n = 21, 70%]) were found between the two groups. Furthermore, no statistically significant differences were observed in treatment adherence, frequency of major (20% vs. 13% between CDSS and control group) and minor (33% vs 27%) complications, and length of stay (98 [±28] vs 81 [±23] days). Healthcare professionals found the CDSS to be helpful for visualizing the treatment process. However, the high workload and difficulties in the information technology processes, such as missing reminders, slow computer performance and data processing, and poor accessibility, hindered the effective implementation of the CDSS.

CONCLUSION

The implementation of the CDSS to support the treatment of stage III/IV PI in patients with SCI was feasible and included definitions of milestones, interventions, and outcomes. However, to assess the impact of the CDSS, a longer observation period is required. Further, the technical difficulties must be addressed, and solid integration of the CDSS into the clinical information system is necessary.

TRIAL REGISTRATION

This quality improvement project received a declaration of no objection from the Ethics Committee of Northwest and Central Switzerland (EKNZ UBE-16/003), and ethical approval was received for the focus groups (EKNZ Req-2017-00860).

Quadruple Decision Making for Parkinson's Disease Patients: Combining Expert Opinion, Patient Preferences, Scientific Evidence, and Big Data Approaches to Reach Precision Medicine

Clinical decision making for Parkinson’s disease patients is supported by a combination of three distinct information resources: best available scientific evidence, professional expertise, and the personal needs and preferences of patients. All three sources have clear value but also share several important limitations, mainly regarding subjectivity, generalizability and variability. For example, current scientific evidence, especially from controlled clinical trials, is often based on selected study populations, making it difficult to translate the outcome to the care for individual patients in everyday clinical practice. Big data, including data from real-life unselected Parkinson populations, can help to bridge this information gap. Fine-grained patient profiles created from big data have the potential to aid in identifying therapeutic approaches that will be most effective given each patient’s individual characteristics, which is particularly important for a disorder characterized by such tremendous interindividual variability as Parkinson’s disease. In this viewpoint, we argue that big data approaches should be acknowledged and harnessed, not to replace existing information resources, but rather as a fourth and complimentary source of information in clinical decision making, helping to represent the full complexity of individual patients. We introduce the ‘quadruple decision making’ model and illustrate its mode of action by showing how this can be used to pursue precision medicine for persons living with Parkinson’s disease.

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

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.

Contributions on Clinical Decision Support from the 2018 Literature

Objectives : To summarize recent research and select the best papers published in 2018 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook.

Methods : A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and the section editors' evaluation.

Results : Among 1,148 retrieved articles, 15 best paper candidates were selected, the review of which resulted in the selection of four best papers. The first paper introduces a deep learning model for estimating short-term life expectancy (>3 months) of metastatic cancer patients by analyzing free-text clinical notes in electronic medical records, while maintaining the temporal visit sequence. The second paper takes note that CDSSs become routinely integrated in health information systems and compares statistical anomaly detection models to identify CDSS malfunctions which, if remain unnoticed, may have a negative impact on care delivery. The third paper fairly reports on lessons learnt from the development of an oncology CDSS using artificial intelligence techniques and from its assessment in a large US cancer center. The fourth paper implements a preference learning methodology for detecting inconsistencies in clinical practice guidelines and illustrates the applicability of the proposed methodology to antibiotherapy.

Conclusions : Three of the four best papers rely on data-driven methods, and one builds on a knowledge-based approach. While there is currently a trend for data-driven decision support, the promising results of such approaches still need to be confirmed by the adoption of these systems and their routine use.