The learning health system for pediatric nephrology: building better systems to improve health

Abstract

Learning health systems (LHS) align science, informatics, incentives, and culture for continuous improvement and innovation. In this organizational system, best practices are seamlessly embedded in the delivery process, and new knowledge is captured as an integral byproduct of the care delivery experience aimed to transform clinical practice and improve patient outcomes. The objective of this review is to describe how building better health systems that integrate clinical care, improvement, and research as part of an LHS can improve care within pediatric nephrology. This review will provide real-world examples of how this system can be established in a single center and across multiple centers as learning health networks.

Keywords: Learning health networks; Learning health systems; Patient-centered outcomes; Quality improvement; Systems science.

Fig. 1

Clinical outcome measures with process measures that affect the outcome and the foundational care processes in place that support these processes. In this example, “hypertension” is the clinical outcome of interest. Each clinical outcome measure will have an associated operational definition that details what the goal is. Each outcome will have associated process measures; in this example, for appropriate hypertension management, blood pressure needs to be measured and documented appropriately, classified and assessed, and treated (if necessary). Each of these processes have subprocesses. For example, the clinician attempts to classify and assess the blood pressure (correctly or incorrectly). This figure is simplified to show only the process measures for “hypertension,” but process measures exist for each outcome measure

Fig. 2

Key driver diagram developed to guide improvement work to decrease acute rejection rates for pediatric kidney transplant patients

Fig. 3

Figure describing the continuous cycle of discovering new knowledge and applying this directly to the patients to create change in the system of care

Fig. 4

Composite Ideal Outcome for kidney transplant patients. This figure illustrates one way to map the ideal outcome for a transplant patient. This composite outcome is comprised of relevant health domains (shaded boxes) with the measure(s) associated with each domain and future states (dashed line boxes). Abbreviations: CKD, chronic kidney disease; Ca, calcium; CO₂, bicarbonate; Hgb, hemoglobin; PO₄, phosphorus; UPC, urine protein/creatinine ratio; GFR, glomerular filtration rate; BKV, BK virus; CMV, cytomegalovirus; EBV, Epstein-Barr virus; UTI, urinary tract infection; BMI, body mass index; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol

Fig. 5

Example of population level clinical outcomes for kidney transplant patients. Each graph shows a given outcome measure over time (x-axis) and the percentage of the population that achieved the goal for this outcome measure (y-axis). Abbreviations: BMI, body mass index; CM, cytomegalovirus; UTI, urinary tract infection; GFR, glomerular filtration rate; Chol, cholesterol; LDL, low-density lipoprotein; HDL, high-density lipoprotein; TG, triglycerides; SBP, systolic blood pressure; Ca, calcium; UPC, urine protein/creatinine ratio; HgB, hemoglobin; PO₄, phosphorus; CO₂, bicarbonate

Fig. 6

Ideal outcome for a kidney transplant program. This figure illustrates how these measures can be displayed over time for population management. During this 5-year period our kidney transplant program was actively pursuing improvement projects focused on blood pressure control (brown), reducing acute rejection (red), and appropriately managing chronic kidney disease (purple)