Introduction
Electronic health records (EHRs) that enable efficient and secure exchange of health care data among providers, patients, health care administrators, and public health officials have the potential to improve clinical care for a variety of chronic conditions. Well designed EHR systems can facilitate improved care for patients with chronic diseases across all elements of the Chronic Care Model, including clinical information systems (e.g., identifying patients and improving continuity of care), decision support for providers, delivery system design (e.g., multidisciplinary teams and provider collaboration), and patient self-management support (Figure 1) (1). Standardized and accessible EHR systems can also improve our understanding of chronic diseases by providing rich data for observational studies, identifying potential patients for research, and enabling national surveillance systems.
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Figure 1: How health information technology (HIT) can improve CKD care. Potential benefits to appropriate incorporation of CKD-related data in electronic health records within the context of the Chronic Care Model (
1). CQM, continuous quality management.
Because of this potential for improved care, the Health Information Technology for Economic and Clinical Health Act established the Medicare and Medicaid EHR Incentive Programs to encourage the widespread implementation and use of EHRs (2). Eligible providers that show that they have met the criteria for meaningful use of certified EHR technology may qualify for incentive payments under Medicare and Medicaid. This is one of many factors that has resulted in a 3-fold increase in the number of clinics and hospitals using EHRs between 2001 and 2011 (3). As EHR use becomes more widespread, it is important to recognize and capitalize on the potential of EHRs to improve the care of patients with chronic conditions. More integrated EHR data may not only help provide direct clinical benefits but also, greater data integration could simultaneously support secondary public health and research objectives (i.e., disease registries and pragmatic clinical trials) that could increase disease knowledge and ultimately, improve patient care as well (4). It is also important to acknowledge some of the unintended consequences of EHRs—such as increased work tasks associated with computerized order entry, fragmentation of data, loss of communication, and clinical decision support that may be too rigid, include outdated content, and lead to alert fatigue—that need to be minimized by thoughtful design and implementation (5,6).
CKD: A Unique Condition to Show the Potential of EHRs in Chronic Disease Care
CKD represents a unique condition that may show the potential of EHRs to improve chronic disease care for several reasons.
CKD Is a Common and Growing Clinical Problem in the United States, Providing an Opportunity to Improve Care for Many Americans
More than 20 million Americans ages 20 years old and older may have CKD (7). In 2009, the US Renal Data System (USRDS) estimated that the prevalence of CKD had increased by 20%–25% over the preceding decade (8). The current diabetes and obesity epidemics are expected to fuel additional growth. Because of the significant burden of CKD, improved performance and interoperability of data related to kidney health within and across EHR systems have the potential to improve the health of millions of Americans.
Current CKD Care Is Suboptimal and Could Benefit from Effective Use of EHRs
The care of patients with CKD is often inconsistent with published guidelines (9–11). CKD and its related complications often go unrecognized and untreated, in part because patients with CKD are asymptomatic until late stages, high-risk individuals are not always appropriately screened, and results may be misinterpreted (12). Few patients with CKD are appropriately monitored for metabolic complications (13), and over one half of patients with CKD have BP above current treatment targets (10,14). In addition, >70% of patients who progress to ESRD start dialysis with catheters, which are associated with increased morbidity and mortality compared with fistulas and grafts (15). EHRs have the potential to improve the care of such patients with CKD by facilitating earlier identification and appropriate management through tracking of processes, provider reminders, and decision support (16). After being developed for CKD, such tools could be adapted for other chronic conditions. For example, components of clinical decision support systems focused on medication monitoring and dose adjustment among patients with CKD may be adapted for use among patients with liver disease and hematologic conditions.
CKD and Its Associated Conditions Are Defined Primarily by Objective Data That Could Be Incorporated or Better Structured within EHRs with Relative Ease
CKD is primarily defined by laboratory abnormalities and may be the prototypical computable EHR phenotype (that is, a disease identified using EHR data) (17). Much of the objective data related to CKD and its complications and comorbidities is already incorporated into many EHRs. However, more complete labeling of test names and units (18) with universal codes, such as Logical Observation Identifiers Names and Codes (LOINC) (19) for laboratory results, is needed to optimize their use. Because CKD comorbidities and complications (e.g., BP, A1C, cholesterol, and urine albumin-to-creatinine ratio) are relevant to numerous chronic conditions (e.g., heart disease and diabetes), such optimization of data would be broadly beneficial to chronic disease care. Thus far, subjective elements critical to optimal CKD care, such as renal replacement modality choice, have been more challenging to capture uniformly and have not yet been incorporated into most EHRs.
Optimal CKD Care Requires Collaboration by a Broad and Diverse Team of Health Care Professionals across Numerous Settings, Which Could Be Facilitated by More Functional EHR Interoperability
Most care for patients with CKD is provided in the primary care setting. However, optimal CKD care, especially in more advanced stages, requires active collaboration with a broad range of providers, including nephrologists, pharmacists, nurses, dietitians, and allied health professionals. These providers often work in diverse settings, including inpatient and outpatient facilities, emergency departments, extended and long-term care facilities, pharmacies, and dialysis facilities. Members of the entire CKD care team need to have accurate and timely information to appropriately assess care needs, prescribe or adjust medications, optimize patient education, and ensure a coordinated transition to RRT if necessary (20). In addition to supporting interprovider and intersite collaboration for CKD, improved interoperability and collaboration tools within and across EHR systems have the potential to enhance team-based care for a variety of conditions.
CKD Is Clinically Significant, and Incorporation of CKD Data into EHRs Will Facilitate Identification of a High-Risk Patient Population
CKD is often a sentinel disease, heralding increased risk for hospitalizations, cardiovascular events, and all-cause mortality (21). Effective implementation of CKD data into EHRs will enable providers to more easily identify this high-risk patient population for targeted care management programs that may reduce the increased risk for adverse outcomes associated with CKD (22).
Improving EHRs to Facilitate Quality Care for Patients with Chronic Conditions
Given the potential benefit of greater EHR use to improve care for patients with CKD, the National Kidney Disease Education Program has established a Health Information Technology Working Group (23). The goal of the working group is to enable and support the widespread interoperability of data related to kidney health among health care software applications to optimize CKD detection and management. A subset of the working group, consisting of experts in bioinformatics, nephrology, population health, and clinical research, met over the course of the last 2 years to identify general features needed within EHR systems to improve care for patients with CKD; the working group then identified ways in which CKD data, after appropriately incorporated within EHRs, could be used to improve the care of patients with CKD. This paper outlines general recommendations for features needed to facilitate:A number of organizations have implemented various EHR-based interventions and registries (Table 1). Unfortunately, the majority of these early efforts have been isolated to single institutions, required significant resources to develop, and have not been adopted by other organizations. Navaneethan et al. (24) outline potential applications of EHRs for CKD identification and management within an individual health care system. This paper aims to build on that discussion by presenting general goals and a framework for more widespread implementation and use of these evolving tools and resources.
- Optimal care of individual patients through improved and intuitive provider- and patient-facing interfaces as well as secure access for both patients and providers to all clinical information, regardless of where the data were collected.
- Clinical quality improvement through quality measurement and implementation of population management programs.
- National CKD surveillance to improve public health through broader availability of population-level CKD data.
- Research to improve CKD management practices, such as observational studies, comparative effectiveness research, and enhanced design and implementation of clinical trials through efficiencies in study recruitment and data collection.
Table 1: Sample of health information technology efforts in CKD
EHRs Should Incorporate CKD-Related Data as Structured Data Using Standard Code Systems and Units to Enable Identification and Effective Management across the Entire Disease Course
For the purposes of incorporation into EHRs, CKD-related data may be most effectively categorized into three types (Table 2).For optimal management of CKD, all three data categories should be incorporated within EHRs using standard code systems and units (18). Challenges to implementation include legacy local coding systems and implementation of EHRs that is highly variable and customized for each provider organization (33).
- Laboratory data required to diagnose CKD, evaluate its severity, monitor progression, and identify appropriate treatment. Required data include measures of kidney function (i.e., serum creatinine, corresponding eGFR, and method of estimation) and kidney damage (i.e., quantitative assessment of urine albumin excretion, such as urine albumin-to-creatinine ratio) (25). Meaningful use stage 2 required that EHRs incorporate at least 55% of their numeric and qualitative tests as structured data as of November of 2014 (26); meaningful use stage 3 will likely strengthen this requirement.
- Data related to CKD risk factors, complications, comorbidities, and treatments, which are critical to determine the etiology of CKD, optimize treatment to prevent or delay progression (27), dose medications requiring adjustment for renal function, and reduce the risk of avoidable AKI (28,29). These data are also available in most EHRs in structured and unstructured formats and include the following items.
- Diagnoses, such as diabetes mellitus, hypertension, dyslipidemia, anemia, and cardiovascular, cerebrovascular, and peripheral vascular disease (stage 1 meaningful use core objective).
- Clinical and laboratory results, such as BP, glucose, lipids, hemoglobin, urinalysis, potassium, bicarbonate, transferrin saturation index, ferritin, calcium, phosphorus, and parathyroid hormone.
- Prescription and over-the-counter medications are almost universal and use standard coding systems, such as RxNorm (stage 1 meaningful use core objective).
- Documentation of nephrology referral, which is essential both to ensure appropriate preparation for RRT and because early referral may reduce mortality in those who progress to ESRD (30–32).
- Patient education and preferences and planning for ESRD. Unlike the first two categories of CKD-related data, this final category is not available in most EHRs and often needs to be obtained from patients; collection of these data needs to be carefully planned, so that it can be converted to structured data to support applications, such as decision support. This category includes data related to the following items.
- Patient education, such as attending nutrition or treatment options classes.
- Preferences regarding dialysis modality, vascular access, and transplantation.
- Patient-reported outcomes, such as quality of life or functional status.
- ESRD planning data with regard to vascular access (i.e., vein mapping, surgery referral, access placement date, and previous complications and interventions), evaluation for peritoneal dialysis, and/or transplant evaluation and listing.
Table 2: CKD-related data and their clinical use
CKD-Related Data in EHRs Should Be Readily Available and Easily Accessible for Patients and Providers
Although data in the first two categories are included in most EHRs, they are frequently stored in formats that do not allow easy access by patients and providers. CKD-related data should be stored in structured, standard formats incentivized by the EHR Incentive Program’s meaningful use criteria. Although such formats have been incorporated into many EHRs, there is room for improvement. For example, some EHRs continue to store BP data as a text field as systolic value/diastolic value. Although the EHR Incentive Program meaningful use encourages the use of standard LOINC codes (19) for identifying laboratory results, these are only beginning to appear within EHRs, and EHRs do not consistently use the standard units of measure (Unified Code for Units of Measure) (18) that are recommended by Health Level 7.
Even when data are appropriately stored, it can be time-consuming to manually search multiple sections of the EHR to obtain necessary CKD-related data during a patient visit (34). EHRs could offer provider-facing flow sheets to display all important CKD-related data in one location. These flow sheets could be embedded with decision support tools, clinical reminders, and links to supporting references. Furthermore, providers should have access to customizable displays of longitudinal data to allow critical monitoring of trends and disease progression. Patients should also have access to these flow sheets with consolidated CKD-related data to facilitate self-management support.
EHRs Should Support Exchange of CKD-Related Information across Health Care Settings
Information exchange across health care settings and between providers is critical to providing optimal care for patients with CKD, but records are currently fragmented. For example, clinical data from dialysis facilities are not typically integrated with the health records of other providers. With appropriate safeguards to protect patient privacy, information exchange could be expanded, so that CKD data could be shared between providers and across care settings to improve continuity of care, and, potentially, reduce costs (35). For example, readily available dialysis prescription and recent laboratory data could lead to more appropriate and efficient care of hospitalized patients. In addition, eGFR could be included as part of electronic prescriptions to aid pharmacist verification of medication dosing and avoidance of nephrotoxic agents. Many technical and regulatory challenges, such as incompatibility across EHR systems and compliance with Health Insurance Portability and Accountability Act guidelines, remain before we can fully realize such potential.
EHRs Could Enable Use of CKD-Related Data for Measuring and Improving Quality of Care
Health care providers and hospital administrators who strive to improve performance (e.g., access to care, quality of care, and efficiency) need to be able to identify and track patients with CKD within their population who are not receiving recommended care. EHRs could be configured to allow individual providers and health care delivery organizations to search for patients using CKD-related data and develop CKD registries. Quality dashboards could aggregate and display all CKD-related population data to allow providers to actively manage panels of patients, track achievement of continuous quality management goals, and better coordinate care with other specialties. Although CKD registries and other EHR tools alone are likely not sufficient on their own to improve the quality of CKD care (36,37), CKD registries could be used by quality improvement teams to help identify patients for targeted interventions, such as patients with significantly elevated BP or those with severe CKD not yet referred to nephrology. Obstacles to developing functional CKD registries include the underlying data structure of many EHRs, difficulty in identification of important comorbidities and medications, inability to capture important processes, such as referral for CKD education, and lack of documentation of patient preferences for treatment (i.e., RRT modality).
After established, CKD registries could also facilitate reporting on existing quality measures for patients with CKD that are endorsed by Healthy People 2020 and the National Quality Forum (NQF) (38). These include treatment with angiotensin-converting enzyme inhibitors or angiotensin receptor blockers for patients with nondiabetic nephropathy (NQF 0621), treatment with angiotensin-converting enzyme inhibitors/angiotensin receptor blockers for patients with diabetes and hypertension (NQF 0546), and control of BP to <140/90 mmHg (NQF 0018). These measures must be specified as eMeasures for consistent use across different EHR technologies.
EHRs Could Enable Use of CKD-Related Data to Facilitate CKD Surveillance and Improve Public Health and Health Care Planning
Public health efforts are necessary to understand the full burden of CKD across different communities and track the progress of efforts to reduce this burden through enhanced prevention, detection, and management. Improvements in CKD care could be facilitated through exploration of local, regional, socioeconomic, cultural, medical, and treatment disparities, which has been done in ESRD through the USRDS. A better understanding of these disparities is also important given the disproportionate burden of CKD among minorities and underserved populations (39).
The efforts of the Centers for Disease Control and Prevention (CDC) to establish a National CKD Surveillance System have been limited by the lack of national data (40). Early work by the CDC has incorporated data from national surveys (National Health and Nutrition Examination Survey), cohort studies (Chronic Renal Insufficiency Cohort Study and CKD in Children Prospective Cohort Study), and EHRs, such as those used within the Veterans Affairs Health System. To further expand this effort, large health care systems could periodically share deidentified or aggregate CKD data with national surveillance programs. These health care systems could be certified to automate submission of these data and be publicly recognized for their efforts. The main challenge encountered in establishing the CDC’s CKD Surveillance System has been the difficulty in obtaining data from health care organizations.
Emerging Standards for Accessing EHR Data Should Be Encouraged to Facilitate CKD Research
Most CKD management guideline recommendations are on the basis of expert opinion because of a paucity of high-quality clinical evidence resulting from several factors: (1) few clinical trials in kidney disease, (2) rarity of many kidney diseases (e.g., GN), (3) entrenched expert opinion, resulting in a perceived lack of equipoise and resistance to additional study, and (4) exclusion of patients with CKD from trials in other fields, such as cardiology and oncology. Modern, flexible technologies could use CKD-related EHR data to improve clinical trial design and implementation, including identification and recruitment of study participants with CKD (41,42).
CKD-related EHR data could also facilitate observational, comparative effectiveness, and safety studies of diagnostic and therapeutic approaches to kidney diseases (43–46). Large aggregated datasets could be used to evaluate differences in risk-adjusted clinical outcomes and costs between providers and health systems as well as potentially identify processes that may account for these differences. These clinical datasets could be further enhanced by linking to important clinical outcomes, such as ESRD through the USRDS, renal transplantation through the Scientific Registry of Transplant Recipients, and all-cause mortality through state death files and the National Death Index. Such an approach has been successfully used by the Cardiovascular Research Network, the Food and Drug Administration’s Sentinel Initiative, and the Observational Health Data Sciences and Informatics Program (47–50). Realizing the potential of these complex, large, disparate datasets will require standardization of EHR elements; use of standard codes to identify comorbidities, medications, and other variables; and multidisciplinary expertise in informatics, study design, data management, and statistics. Such efforts could benefit from ongoing collaborations, such as the National Patient-Centered Clinical Research Network and the National Institutes of Health Collaboratory Distributed Research Network (51,52).
Conclusions
CKD is common and associated with significant morbidity and mortality. The care of patients with CKD is complex and data-intense. The implementation of EHRs by hospitals, large provider organizations, and practice groups presents an opportunity to improve the care of patients with CKD through appropriate incorporation of CKD-related data. However, to optimize care of patients with CKD, it is critical that EHRs be designed to make this information readily accessible. At the individual patient level, CKD care could be improved by using patient- and provider-facing flow sheets; at the population level, aggregated data could facilitate population quality improvement efforts. Such functionality will enable providers and organizations to better manage individual patients and identify groups of patients with CKD for targeted interventions. Methods and standards for extracting, analyzing, linking with external resources, and aggregating EHR data should be developed to enable quality measurement and reporting, CKD surveillance, and research, which are vital to improving outcomes for patients with CKD. Detailed solutions for each of the broad goals outlined in this paper will require collaborative engagement from the community, including primary care providers, nephrologists, and experts in public health, outcomes research, and bioinformatics. To that end, the National Institute of Diabetes and Digestive and Kidney Diseases will convene stakeholders in CKD health information technology, population health management, and research in the fall of 2015 to begin to identify specific solutions for the recommendations included in this manuscript.
Disclosures
P.A. serves as the Food and Drug Administration (FDA) cochair of the Kidney Health Initiative, a public-private partnership cofounded by the FDA and the American Society of Nephrology to advance scientific understanding of the kidney health and patient safety implications of new and existing medical products and foster development of therapies for diseases that affect the kidney.
Full disclosure for U.D.P. is available at https://www.dcri.org/about-us/conflict-of-interest.
Acknowledgments
U.D.P. was supported by National Institutes of Health (NIH) Grants R01-DK093938 and R34-DK102166.
The authors take full responsibility for the content of this article, and this work is solely their own. The views expressed do not represent those of the NIH, the Centers for Disease Control and Prevention, or the federal government. This manuscript represents the authors’ original work and is not under consideration for publication elsewhere. All authors meet criteria for authorship and have signed a statement attesting authorship.
The National Kidney Disease Education Program (NKDEP) Health Information Technology (HIT) Working Group includes P.E.D., P.A., N.R.P., K.A.S., D.E.W., U.D.P., A.N., Theresa Cullen, Brenda Hemmelgarn, Ken Kawamoto, Celeste Lee, and Thomas Sequist.
Thanks for support from the NKDEP HIT Intern, Jessica Pereira.
Published online ahead of print. Publication date available at www.cjasn.org.
References
1. Wagner EH, Austin BT, Davis C, Hindmarsh M, Schaefer J, Bonomi A: Improving chronic illness care: Translating evidence into action. Health Aff (Millwood) 20: 64–78, 2001
2. Blumenthal D, Tavenner M: The “meaningful use” regulation for electronic health records. N Engl J Med 363: 501–504, 2010
3. Hsiao C, Hing E, Socey T, Cai B: Electronic Health Record Systems and Intent to Apply for Meaningful Use Incentives among Office-Based Physician Practices: United States, 2001–2011. NCHS Data Brief, No. 79, Hyattsville, MD, National Center for Health Statistics, 2011
4. Johnson KE, Tachibana C, Coronado GD, Dember LM, Glasgow RE, Huang SS, Martin PJ, Richards J, Rosenthal G, Septimus E, Simon GE, Solberg L, Suls J, Thompson E, Larson EB: A guide to research partnerships for pragmatic clinical trials. BMJ 349: g6826, 2014
5. Ash JS, Berg M, Coiera E: Some unintended consequences of information technology in health care: The nature of patient care information system-related errors. J Am Med Inform Assoc 11: 104–112, 2004
6. Ash JS, Sittig DF, Campbell EM, Guappone KP, Dykstra RH: Some unintended consequences of clinical decision support systems. AMIA Annu Symp Proc 2007: 26–30, 2007
7. Levey AS, de Jong PE, Coresh J, El Nahas M, Astor BC, Matsushita K, Gansevoort RT, Kasiske BL, Eckardt KU: The definition, classification, and prognosis of chronic kidney disease: A KDIGO Controversies Conference report. Kidney Int 80: 17–28, 2011
8. US Renal Data System: USRDS 2009 Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States. NIH Publication No. 09-3176, Bethesda, MD, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 2009
9. Patwardhan MB, Samsa GP, Matchar DB, Haley WE: Advanced chronic kidney disease practice patterns among nephrologists and non-nephrologists: A database analysis. Clin J Am Soc Nephrol 2: 277–283, 2007
10. Muntner P, Anderson A, Charleston J, Chen Z, Ford V, Makos G, O’Connor A, Perumal K, Rahman M, Steigerwalt S, Teal V, Townsend R, Weir M, Wright JT Jr.Chronic Renal Insufficiency Cohort (CRIC) Study Investigators: Hypertension awareness, treatment, and control in adults with CKD: Results from the Chronic Renal Insufficiency Cohort (CRIC) Study. Am J Kidney Dis 55: 441–451, 2010
11. Wasse H, Hopson SD, McClellan W: Racial and gender differences in arteriovenous fistula use among incident hemodialysis patients. Am J Nephrol 32: 234–241, 2010
12. Kern EF, Maney M, Miller DR, Tseng CL, Tiwari A, Rajan M, Aron D, Pogach L: Failure of ICD-9-CM codes to identify patients with comorbid chronic kidney disease in diabetes. Health Serv Res 41: 564–580, 2006
13. Hoy T, Fisher M, Barber B, Borker R, Stolshek B, Goodman W: Adherence to K/DOQI practice guidelines for bone metabolism and disease. Am J Manag Care 13: 620–625, 2007
14. Coresh J, Wei GL, McQuillan G, Brancati FL, Levey AS, Jones C, Klag MJ: Prevalence of high blood pressure and elevated serum creatinine level in the United States: Findings from the third National Health and Nutrition Examination Survey (1988-1994). Arch Intern Med 161: 1207–1216, 2001
15. Astor BC, Eustace JA, Powe NR, Klag MJ, Fink NE, Coresh JCHOICE Study: Type of vascular access and survival among incident hemodialysis patients: The Choices for Healthy Outcomes in Caring for ESRD (CHOICE) Study. J Am Soc Nephrol 16: 1449–1455, 2005
16. Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, Morton SC, Shekelle PG: Systematic review: Impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 144: 742–752, 2006
17. Hripcsak G, Albers DJ: Next-generation phenotyping of electronic health records. J Am Med Inform Assoc 20: 117–121, 2013
18. LOINC: Common UCUM Units. Available at:
https://loinc.org/usage/units/. Accessed March 20, 2015
19. LOINC: Logical Observation Identifiers Names and Codes. Available at: loinc.org. Accessed January 16, 2015
20. Lee BJ, Forbes K: The role of specialists in managing the health of populations with chronic illness: The example of chronic kidney disease. BMJ 339: b2395, 2009
21. Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY: Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med 351: 1296–1305, 2004
22. Mendu ML, Schneider LI, Aizer AA, Singh K, Leaf DE, Lee TH, Waikar SS: Implementation of a CKD checklist for primary care providers. Clin J Am Soc Nephrol 9: 1526–1535, 2014
23. National Kidney Disease Education Program: National Kidney Disease Education Program Health Information Technology Working Group. Available at:
http://nkdep.nih.gov/about-nkdep/working-groups/health-information-technology-working-group.shtml. Accessed March 2, 2015
24. Navaneethan SD, Jolly SE, Sharp J, Jain A, Schold JD, Schreiber MJ Jr., Nally JV Jr.: Electronic health records: A new tool to combat chronic kidney disease? Clin Nephrol 79: 175–183, 2013
25. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group: KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 3: 1–150, 2013
26. Federal Register: Vol. 77, No. 171. Available at:
http://www.gpo.gov/fdsys/pkg/FR-2012-09-04/pdf/2012-21050.pdf. Accessed March 30, 2015
27. James PA, Oparil S, Carter BL, Cushman WC, Dennison-Himmelfarb C, Handler J, Lackland DT, LeFevre ML, MacKenzie TD, Ogedegbe O, Smith SC Jr., Svetkey LP, Taler SJ, Townsend RR, Wright JT Jr., Narva AS, Ortiz E: 2014 evidence-based guideline for the management of high blood pressure in adults: Report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA 311: 507–520, 2014
28. Plantinga L, Grubbs V, Sarkar U, Hsu CY, Hedgeman E, Robinson B, Saran R, Geiss L, Burrows NR, Eberhardt M, Powe NCDC CKD Surveillance Team: Nonsteroidal anti-inflammatory drug use among persons with chronic kidney disease in the United States. Ann Fam Med 9: 423–430, 2011
29. Grubbs V, Plantinga LC, Tuot DS, Hedgeman E, Saran R, Saydah S, Rolka D, Powe NRCenters for Disease Control and Prevention CKD Surveillance Team: Americans’ use of dietary supplements that are potentially harmful in CKD. Am J Kidney Dis 61: 739–747, 2013
30. Winkelmayer WC, Owen WF Jr., Levin R, Avorn J: A propensity analysis of late versus early nephrologist referral and mortality on dialysis. J Am Soc Nephrol 14: 486–492, 2003
31. Hommel K, Madsen M, Kamper AL: The importance of early referral for the treatment of chronic kidney disease: A Danish nationwide cohort study. BMC Nephrol 13: 108, 2012
32. Kinchen KS, Sadler J, Fink N, Brookmeyer R, Klag MJ, Levey AS, Powe NR: The timing of specialist evaluation in chronic kidney disease and mortality. Ann Intern Med 137: 479–486, 2002
33. Harris MR, Langford LH, Miller H, Hook M, Dykes PC, Matney SA: Harmonizing and extending standards from a domain-specific and bottom-up approach: An example from development through use in clinical applications [published online ahead of print February 10, 2015]. J Am Med Inform Assoc
34. McDonald CJ, Callaghan FM, Weissman A, Goodwin RM, Mundkur M, Kuhn T: Use of internist’s free time by ambulatory care Electronic Medical Record systems. JAMA Intern Med 174: 1860–1863, 2014
35. Rudin RS, Motala A, Goldzweig CL, Shekelle PG: Usage and effect of health information exchange: A systematic review. Ann Intern Med 161: 803–811, 2014
36. Drawz PE, Miller RT, Singh S, Watts B, Kern E: Impact of a chronic kidney disease registry and provider education on guideline adherence—a cluster randomized controlled trial. BMC Med Inform Decis Mak 12: 62, 2012
37. Demakis JG, Beauchamp C, Cull WL, Denwood R, Eisen SA, Lofgren R, Nichol K, Woolliscroft J, Henderson WG: Improving residents’ compliance with standards of ambulatory care: Results from the VA Cooperative Study on Computerized Reminders. JAMA 284: 1411–1416, 2000
38. National Quality Forum: Home. Available at:
http://www.qualityforum.org/Home.aspx. Accessed January 3, 2014
39. Crews DC, Pfaff T, Powe NR: Socioeconomic factors and racial disparities in kidney disease outcomes. Semin Nephrol 33: 468–475, 2013
40. Centers for Disease Control and Prevention: The Chronic Kidney Disease Surveillance System. Available at:
http://nccd.cdc.gov/CKD/default.aspx. Accessed June 6, 2014
41. Mandl KD, Kohane IS: Escaping the EHR trap—the future of health IT. N Engl J Med 366: 2240–2242, 2012
42. Mandl KD, Mandel JC, Murphy SN, Bernstam EV, Ramoni RL, Kreda DA, McCoy JM, Adida B, Kohane IS: The SMART Platform: Early experience enabling substitutable applications for electronic health records. J Am Med Inform Assoc 19: 597–603, 2012
43. Richesson RL, Hammond WE, Nahm M, Wixted D, Simon GE, Robinson JG, Bauck AE, Cifelli D, Smerek MM, Dickerson J, Laws RL, Madigan RA, Rusincovitch SA, Kluchar C, Califf RM: Electronic health records based phenotyping in next-generation clinical trials: A perspective from the NIH Health Care Systems Collaboratory. J Am Med Inform Assoc 20: e226–e231, 2013
44. Kovesdy CP, Lu JL, Molnar MZ, Ma JZ, Canada RB, Streja E, Kalantar-Zadeh K, Bleyer AJ: Observational modeling of strict vs conventional blood pressure control in patients with chronic kidney disease. JAMA Intern Med 174: 1442–1449, 2014
45. Navaneethan SD, Schold JD, Arrigain S, Jolly SE, Wehbe E, Raina R, Simon JF, Srinivas TR, Jain A, Schreiber MJ Jr., Nally JV Jr.: Serum bicarbonate and mortality in stage 3 and stage 4 chronic kidney disease. Clin J Am Soc Nephrol 6: 2395–2402, 2011
46. Sheta MA, Hostetter T, Drawz P: Physiological approach to assessment of acid-base disturbances. N Engl J Med 372: 194–195, 2015
47. Behrman RE, Benner JS, Brown JS, McClellan M, Woodcock J, Platt R: Developing the Sentinel System—a national resource for evidence development. N Engl J Med 364: 498–499, 2011
48. Go AS, Magid DJ, Wells B, Sung SH, Cassidy-Bushrow AE, Greenlee RT, Langer RD, Lieu TA, Margolis KL, Masoudi FA, McNeal CJ, Murata GH, Newton KM, Novotny R, Reynolds K, Roblin DW, Smith DH, Vupputuri S, White RE, Olson J, Rumsfeld JS, Gurwitz JH: The Cardiovascular Research Network: A new paradigm for cardiovascular quality and outcomes research. Circ Cardiovasc Qual Outcomes 1: 138–147, 2008
50. Observational Health Data Sciences and Informatics (OHDSI): Observational Health Data Sciences and Informatics (OHDSI) Program. Available at:
http://www.ohdsi.org/. Accessed March 20, 2015
51. PCORnet: The National Patient-Centered Clinical Research Network. Available at:
http://www.pcori.org/funding-opportunities/pcornet-national-patient-centered-clinical-research-network/. Accessed February 14, 2014
52. NIH: NIH Distributed Research Network: Available at:
https://www.nihcollaboratory.org/Pages/distributed-research-network.aspx. Accessed February 14, 2014
53. Navaneethan SD, Jolly SE, Schold JD, Arrigain S, Saupe W, Sharp J, Lyons J, Simon JF, Schreiber MJ Jr., Jain A, Nally JV Jr.: Development and validation of an electronic health record-based chronic kidney disease registry. Clin J Am Soc Nephrol 6: 40–49, 2011
54. Rutkowski M, Mann W, Derose S, Selevan D, Pascual N, Diesto J, Crooks P: Implementing KDOQI CKD definition and staging guidelines in Southern California Kaiser Permanente. Am J Kidney Dis 53[Suppl 3]: S86–S99, 2009
55. Wilson FP, Shashaty M, Testani J, Aqeel I, Borovskiy Y, Ellenberg SS, Feldman HI, Fernandez H, Gitelman Y, Lin J, Negoianu D, Parikh CR, Reese PP, Urbani R, Fuchs B: Automated, electronic alerts for acute kidney injury: A single-blind, parallel-group, randomised controlled trial [published online ahead of print February 25, 2015]. Lancet
56. Field TS, Rochon P, Lee M, Gavendo L, Baril JL, Gurwitz JH: Computerized clinical decision support during medication ordering for long-term care residents with renal insufficiency. J Am Med Inform Assoc 16: 480–485, 2009
57. Barnes KD, Tayal NH, Lehman AM, Beatty SJ: Pharmacist-driven renal medication dosing intervention in a primary care patient-centered medical home. Pharmacotherapy 34: 1330–1335, 2014
58. Narva AS: Decision support and CKD: Not there yet. Clin J Am Soc Nephrol 7: 525–526, 2012
59. Cooney D, Moon H, Liu Y, Miller RT, Perzynski A, Watts B, Drawz PE: A pharmacist based intervention to improve the care of patients with CKD: A pragmatic, randomized, controlled trial. BMC Nephrol, 16: 56, 2015
