Recommendations for a Clinical Decision Support for the Management of Individuals with Chronic Kidney Disease

Abstract

Background and objectives: Care for advanced CKD patients is suboptimal. CKD practice guidelines aim to close gaps in care, but making providers aware of guidelines is an ineffective implementation strategy. The Institute of Medicine has endorsed the use of clinical decision support (CDS) for implementing guidelines. The authors' objective was to identify the requirements of an optimal CDS system for CKD management.Design, setting, participants, and measurements: The aims of this study expanded on those of previous work that used the facilitated process improvement (FPI) methodology. In FPI, an expert workgroup develops a set of quality improvement tools that can subsequently be utilized by practicing physicians. The authors conducted a discussion with a group of multidisciplinary experts to identify requirements for an optimal CDS system.Results: The panel considered the process of patient identification and management, associated barriers, and elements by which CDS could address these barriers. The panel also discussed specific knowledge needs in the context of a typical scenario in which CDS would be used. Finally, the group developed a set of core requirements that will likely facilitate the implementation of a CDS system aimed at improving the management of any chronic medical condition.Conclusions: Considering the growing burden of CKD and the potential healthcare and resource impact of guideline implementation through CDS, the relevance of this systematic process, consistent with Institute of Medicine recommendations, cannot be understated. The requirements described in this report could serve as a basis for the design of a CKD-specific CDS.

Automated Clinical Reminders for Primary Care Providers in the Care of CKD: A Small Cluster-Randomized Controlled Trial

“…47,55,56 However, despite well-established shortcomings in care, there has been limited use or study of CDSS to enhance non-dialysis CKD care. 53,57 Larger studies employing CDSS to improve CKD care are needed to further delineate the potential utility and ideal settings for deploying these tools.…”

User Requirements for a Chronic Kidney Disease Clinical Decision Support Tool to Promote Timely Referral

“…They developed user requirements including easy access to evidence associated with CKD management, clear identification of patients’ current status, and specific actionable recommendations for intensification of antihypertensives. [20]…”

“…Our findings are similar to qualitative studies about co-management because some participants mentioned experiences with co-management that were beneficial to care while other participants felt that lack of co-management was a barrier to referral. [20, 34, 37, 38]…”

“…[19] One study used the facilitated process improvement methodology to gather requirements for a CDS system to support CKD management. [20] To our knowledge, no one has yet successfully designed a CDS system to promote timely referral.…”

Rule-based decision support system in the biopsy diagnosis of glomerular diseases

“…However, these systems cannot improve themselves, unless the rules are refined further. This is in contrast to neural networks, which require a large number of cases in the training set; they (2) Normal/impaired Serum complement C3 (3) Normal/reduced Microscopic haematuria (6) Absent/present Proteinuria (7) Absent/subnephrotic/nephrotic ANA and dsDNA (4) ANA+/dsDNA+/ANA+dsDNA+ ANCA (5) Negative/positive Histomorphological parameters Global glomerulosclerosis (8) Absent/<50%/>50% Glomerular cellularity (normal/increased) (9) Normal/increased <50% glomeruli/increased >50% glomeruli Type of cells in glomerular hypercellularity (10) Absent/mesangial cells/endocapillary with PMN/endocapillary with PMN and tuft necrosis/endocapillary without PMN Crescents (11) Absent/present <50% glomeruli/>50% glomeruli Glomerular fibrin (12) Absent/present Basement membrane changes (13) Not improve over time, yielding good results once the system is adequately trained. DSS have been explored recently for use in clinical medicine, such as in the cardiac surgical intensive care unit, diagnosis of low back pain, oncology services, and prognostication of patients with colorectal cancer.…”