Clinical Guidelines: Where Next?

JRSM, 2009

Journal of Clinical Epidemiology, 2012

As health professionals and patients are moving toward shared models of decision making, there is a growing need for integrated decision support tools that facilitate uptake of best evidence in routine clinical practice in a patient-centered manner. This article charts the landscape of clinical practice guidelines (CPGs) and patient decision aids.

2000

2022

Background The process of moving from evidence to decisions (EtD) represents a cornerstone within guideline development methods. Little is known about the processes used by organizations in charge of guideline development and what criteria they consider when formulating recommendations. Objective To identify and describe the processes suggested for the formulation of healthcare recommendations in health care guidelines available in guidance documents. Methods We searched in spring 2020 the Guidelines International Network (G-I-N) website, MEDLINE, and The Cochrane Methodology Register to retrieve guidance documents published in the last decade by organizations dedicated to guideline development. Pairs of researchers independently selected and extracted data about the characteristics of the guidance document, including explicit or implicit recommendation-related criteria and processes considered, as well as the use of frameworks. We conducted both descriptive and bivariate analyses. ...

BMC Health Services Research, 2010

Background: Clinical practice guidelines have been developed aiming to improve the quality of care. The implementation of the computerized clinical guidelines (CCG) has been supported by the development of computerized clinical decision support systems. This systematic review assesses the impact of CCG on the process of care compared with non-computerized clinical guidelines. Methods: Specific features of CCG were studied through an extensive search of scientific literature, querying electronic databases: Pubmed/Medline, Embase and Cochrane Controlled Trials Register. A multivariable logistic regression was carried out to evaluate the association of CCG's features with positive effect on the process of care. Results: Forty-five articles were selected. The logistic model showed that Automatic provision of recommendation in electronic version as part of clinician workflow (Odds Ratio [OR]= 17.5; 95% confidence interval [CI]: 1.6-193.7) and Publication Year (OR = 6.7; 95%CI: 1.3-34.3) were statistically significant predictors. Conclusions: From the research that has been carried out, we can conclude that after implementation of CCG significant improvements in process of care are shown. Our findings also suggest clinicians, managers and other health care decision makers which features of CCG might improve the structure of computerized system.

The value of clinical guidelines and decision support tools are yet to be realized, due to a roadblock in its implementation that arises from both lack of understanding user needs, as well as a discrepancy of trust and transparency in its development process. With greater insight to understanding the organizational behaviors that are involved at each step of the process, from development to implementation, adoption to execution, heightened use and benefit from standardized care through guidelines can be evaluated. Beginning to enforce a more rigorous regulation and monitoring of guidelines development will attract more clinical utility. Standardized use of clinical guidelines will enhance care quality, eliminate discrepancies in treatment, and reduce variability across the health care system.

Research Square (Research Square), 2023

Background: The knowledge and help provided by the medical guidelines are essential to make informed clinical decisions. However, there are no systematic methods to assess the e cacy of guidelines, i.e., how much contribution they provide to informed decisions in various health conditions. Methods: A mathematical analysis was developed to assess the e cacy of guidelines. As an example, the "2020 ESC Guidelines on sports cardiology and exercise in patients with cardiovascular disease" (GL-SCE) was analysed/assessed. The analysis was conducted on the Classes of Recommendations (CLASS) and the Levels of Evidence (LEVEL). LEVEL areas under CLASS were calculated to form a Certainty Index (CI:-1 to+1). Results: The frequency of CLASS I ('to do') and CLASS III ('not to do') was relatively high in GL-SCE. Yet, the most frequent LEVEL was C, indicating a low quality of scienti c evidence. The GL-SCE showed a relatively high CI (+0.57), 78.4% Certainty and 21.6% Uncertainty. Conclusions: GL-SCE provides a substantial help to decision-making through the recommendations, but the supporting evidence in most CLASSes has low quality, which is well-re ected in the developed Certainty Index identifying issues that should be clari ed and investigated in future studies. We propose that the developed mathematical analysis should be used as a Guideline to Guidelines to assess their e cacy and support their implementation in clinical practice thus providing a 'quality control'. Contribution to the literature This study provides a mathematical analysis to objectively measure the help/contribution to medical decision-making provided by any medical Guidelines; this analysis allows a better understanding of the classes of recommendations and the levels of scienti c evidence. The higher level of scienti c evidence and/or clinical signi cance of lower evidence may be increased by conducting new experimental and clinical studies on uncertain issues revealed with this analysis on the Guidelines; thus, the e cacy of clinical decision-making can be increased. The outlined mathematical analysis provides a 'quality control', and as such, it can be used as a Guideline for assessing the e cacy of the Guidelines. Background Medical societies publish several guidelines to help medical professionals to make decisions in certain disease conditions. Recently by analysing four ESC GLs for CVDs, we revealed that the contribution provided by them to make a decision is uneven, based on the Certainty/Uncertainty ratio [1]. Indeed, the GLs' contribution to decision-making can vary based on the Levels of Evidence (LEVEL) and Classes of Recommendations (CLASS). It is obvious that in the recommendations of Class I (recommended/indicated) 'to do' and Class III (not recommended) 'not to do', the decision is clear

Clinical practice guidelines abound. The recommendations contained in these guidelines are used not only to make decisions about the care of individual patients but also as practice standards to rate physician " quality. " Physicians' confidence in guidelines is based on the supposition that there is a rigorous, objective process for developing recommendations based on the best available evidence. Though voluntary standards for the development of guidelines exist, the process of guideline development is unregulated and the quality of many guidelines is low. In addition, the few tools available to assess the quality of guidelines are time consuming and designed for researchers, not clinicians. Few guidelines are evaluated, either before or after their dissemination , for their impact on patient outcomes. Just as with pharmaceuticals and other products that can affect patients for better or worse, perhaps it is time to develop more standardized ways to evaluate the development and dissemination of clinical practice guidelines to ensure a similar balance between risk and benefit. (J Am Board Fam Med 2016;29:644 – 648.)

Quality and Safety in Health Care, 1995

Patient education and counseling, 2008

Patient involvement in medical decision making has been suggested to contribute to patients' satisfaction and better patient outcomes. Decision aids are particularly useful for preference-sensitive decisions. Ideally, these should be based on up-to-date evidence-based guidelines. The objective of this project was to develop a generic format for development and maintenance of decision aids based on evidence-based guidelines.