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Published ahead of print on January 28, 2009
Clin J Am Soc Nephrol 4: 273-283, 2009
© 2009 American Society of Nephrology
doi: 10.2215/CJN.02590508
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Chronic Kidney Disease

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

Meenal B. Patwardhan*,{dagger},{ddagger}, Kensaku Kawamoto§,||, David Lobach§, Uptal D. Patel, and David B. Matchar*,{dagger},**,{dagger}{dagger}

* Duke Center for Clinical Health Policy Research, Durham, North Carolina; {ddagger} Abbott Laboratories, Abbott Park, Illinois; {dagger} Division of General Internal Medicine, Department of Medicine, Duke University Medical Center; § Division of Clinical Informatics, Department of Community and Family Medicine, Duke University Medical Center; || Institute for Genome Sciences & Policy, Duke University, Durham, North Carolina; Division of Nephrology, Department of Medicine, Duke University Medical Center; ** Veterans Administration Health Services Research; {dagger}{dagger} Veterans Administration Medical Center, Durham, North Carolina

Correspondence: Dr. Meenal B. Patwardhan, Duke Center for Clinical Health Policy Research, 2200 W. Main Street, Suite 220 Durham NC 27705. Phone: 919-286-3399; Fax: 919-286-5601; E-mail: Meenal.P{at}Duke.edu


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 Appendix A
 Appendix B
 Disclosures
 References
 
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.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 Appendix A
 Appendix B
 Disclosures
 References
 
Chronic kidney disease (CKD) is an increasingly common condition that can progress to end-stage renal disease (ESRD), resulting in large health and resource burdens (1,2). Early and appropriate care consistent with practice guidelines during the stages before ESRD can delay or limit progression, and improves outcomes even if ESRD develops (3).

However, given poor adherence to these current CKD practice guidelines, care for advanced CKD patients is not optimal (4–6). These CKD practice guidelines aim to close gaps in care (7,8); however, simply making providers aware of guidelines has long proven to be an ineffective strategy for improving care. Several guideline implementation tools and implementation strategies have been used to promote adherence to clinical guidelines but have had limited success (9). The problem of guideline implementation has been attributed to several factors, including lack of awareness, inertia of previous practice, and lack of time required for implementing guidelines (10–12).

In seeking to address the problem of implementing guidelines in clinical practice, groups including the Institute of Medicine (13) have endorsed the use of clinical decision support (CDS), defined as the "act of providing clinicians, patients and other health care stakeholders with pertinent knowledge and/or person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care" (14). Examples of CDS include providing reminders of required chronic disease management services to clinicians within an electronic health record system, providing cancer screening recommendations to patients within a web-based personal health record system, and delivering patient-specific recommendations to clinicians within a computerized provider order entry system.

Practical guidance on the design, development, and deployment of CDS systems is available from an implementation workbook published by the Healthcare Information and Management Systems Society (15) This workbook organizes the process of CDS implementation into six concrete steps and provides worksheets and examples for accomplishing them. The steps are (1) identify stakeholders and determine goals and objectives; (2) catalog the available information systems infrastructure; (3) select CDS interventions to achieve the goals and objectives; (4) specify and validate the proposed interventions and implementation plan, then develop the implementations and logistics; (5) test and launch the CDS interventions; and (6) evaluate the intervention impact, then enhancing as needed.

CDS systems have tremendous potential to facilitate improvements in the management of CKD; however, they are neither routinely used nor widely available. In kidney disease, they have been predominantly used for providing guidance regarding medication dosage (16). Individual CDS applications have also been developed for assisting with the management of several common comorbidities associated with CKD, including hypertension (17) and diabetes mellitus (18). However, there are few, if any, CDS systems available that optimally support the comprehensive care of patients with CKD.

Given the need for CDS to improve CKD management and the lack of an existing solution to meet this need, our primary objective was to identify the requirements of an optimal CDS system for CKD management according to existing guidelines. In addition, our secondary goal was to develop a systematic strategy that could be used by other individuals interested in identifying the requirements of a CDS system for the management of other chronic medical conditions.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 Appendix A
 Appendix B
 Disclosures
 References
 
The aims of this study expanded on previous work that used a methodology termed facilitated process improvement (FPI) toward the development and implementation of an evidence-based CKD guideline and a patient management toolkit (Appendix A) (19,20). The FPI approach is based on principles of total quality management but differs from it in one significant way. In FPI, an expert workgroup performs groundwork and develops a set of quality improvement tools that can be selected and customized by practitioners who have insufficient time or training to develop the tools. Tools are developed by the workgroup through an understanding of the settings in which quality improvement tools will be used, processes of care in different settings, process failures, and root causes of process failures. Tools developed through this systematic process include options that can potentially meet the needs of a range of practices with varying resource availability (e.g. in terms of information technology or support staff). FPI proposes that, because national experts perform all of the preliminary work, practitioners simply select and customize tools that meet their unique needs.


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  		Appendix A. Tools included in the advanced CKD patient management toolkit

 
During the current project, the FPI concept was expanded by an internal workgroup (authors of this report). Our goals were to build on our previous understanding of the process of identifying and managing CKD, as well as recognizing process failures and root causes related to CDS use in CKD. We developed requirements of a CDS system that could address these root causes through two steps.

First, we identified existing CDS systems for CKD management. For this purpose, we extended an existing (1996 to 2003) literature search on CDS conducted by two researchers on our team (D.L. and K.K.) (21) to include 2003 to 2007, and limited the results to CKD-related applications. This search was supplemented by reviews of reference lists contained in seed articles. We also sought to identify abstracts presented at a 2006 national meeting convened by the American Society of Nephrology and the American Medical Informatics Association. Finally, as another means to identify extant CDS applications, we searched through gray literature.

Second, with an understanding of the existing state of CDS in CKD, we conducted a brainstorming session with a group of multidisciplinary experts. (Appendix B) This panel possessed experience and expertise in a wide range of disciplines relevant for our project.

In preparation for a one-day brainstorming session, the internal workgroup developed a list of general assumptions about the context in which a CDS would be used for identifying and managing CKD (Table 1). This list was presented to the expert advisory panel at the initiation of the workshop.


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  		Table 1. Assumptions related to the use of clinical decision support systems for chronic kidney disease management in the United States

 
The workshop was moderated by D.M. and was divided into time segments for orientation, brainstorming, refining recommendations, and validating recommendations (22,23). All participants were repeatedly invited to offer recommendations and were advised that no ideas would be wrong or unacceptable. Initially, participants were asked to write down their ideas anonymously on individual Postit notes that were then organized on easel sheets. Participants were advised that their ideas would not be judged or criticized during brainstorming. Later they were encouraged to combine ideas or to improve on them with new items. Finally, the participants consolidated all the ideas into thematically related groups.

Participants reviewed the final listing of all of the ideas related to general CDS requirements and those related to the specific-use case that had been discussed. Then, after the workshop, the internal workgroup convened several times to review workshop outcomes, make data analysis decisions, and develop the current report. Conclusions were drawn from the results and debated until a consensus was reached.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 Appendix A
 Appendix B
 Disclosures
 References
 
The search for extant CDS systems for CKD (i.e. the systematic literature search, the search of meeting presentations and abstracts, and the search of the gray literature) led to the identification of a relatively small volume of literature related to CDS applications in CKD (Table 2). In summary, four published articles were found, all of which focused on CDS as a tool for the prevention of medication errors in patients with abnormal renal function (16,24–27). Only one published abstract was found (28). Although it described a CDS system geared toward management of CKD, it focused on supporting the initial management of referred patients by primary care providers in the United Kingdom. Finally, four CDS applications were found, some of which have the potential to serve as CDS systems for CKD care (29–33). However, the information we retrieved from the websites did not provide adequate details to confirm that the applications addressed the entire spectrum of barriers to implementing CDS that we had previously identified.


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  		Table 2. Results of a search for applications of clinical decision support (CDS) for the management of CKD

 
Next, our 1-day workshop included 12 multidisciplinary experts. The entire panel agreed that the prerequisite assumptions (Table 2) possessed reasonable face validity for the goals of this project. Guided by the facilitator, the group then identified several requirements of an optimal CDS system for CKD management that considered the following: (1) the process of CKD patient identification and management, (2) barriers associated with this process, and (3) mechanisms by which a CDS could address these barriers. Then, the advisory panel and the internal workgroup grouped these requirements according to a high-level classification scheme. This was based on a similar scheme that researchers in this group (D.L. and K.K.) have used previously when describing the features of CDS systems. (Table 3) (21) A CDS system that supports most or all of these features would be desirable for facilitating CKD management. It should be noted that most of the requirements identified through the workshop are applicable to the management of many chronic medical conditions other than CKD, especially those that require involvement of more than one physician specialist and ancillary providers, as well as active participation by the patient.


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  		Table 3. Requirements of an optimal clinical decision support (CDS) system for CKD

 
The brainstorming and open discussions related to three central themes: (1) requirements of an optimal CDS System for CKD, (2) specific clinical knowledge needs related to CKD management that should be supported by a CDS system in a typical use case scenario (the comanagement of a patient with CKD by a general internist and a consulting nephrologists), and (3) key requirements for the successful implementation of a CDS system in practices caring for CKD patients (in contrast to the requirements of a CDS system itself).

In addition to the requirements of a CDS system for CKD, the specific knowledge needs for CKD management were discussed during the workshop in the context of a representative use scenario (the comanagement of a patient with CKD by a general internist and a consulting nephrologists). These discussions allowed the group to identify the specific knowledge needs related to CKD management that should be supported by a CDS system for CKD. (Table 4) Of note, these information needs overlap significantly with the information needs identified through the one-on-one interviews and focus group conducted with clinicians during our prior work (i.e. during the development of the Advanced CKD Patient Management Toolkit).


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  		Table 4. Knowledge needs for CKD management that should be supported by a CDS system (comanagement of a patient with CKD by a general internist and a consulting nephrologist)

 
Finally, the group also developed a set of core requirements that would be critical to the successful implementation of a CDS system. These included (1) establishing a business case for implementation and ongoing support, (2) securing institutional support, (3) securing support of local opinion leaders, and (4) introducing a CDS system as a component of an overall revamping of clinical workflows.

These key requirements are likely to facilitate the implementation of any CDS system aimed at improving the management of any chronic medical condition, not just practices managing CKD.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
Appendix A
 Appendix B
 Disclosures
 References
 
The Roadmap for National Action on Clinical Decision Support states, "The goal of CDS is to provide the right information, to the right person, in the right format, through the right channel, at the right point in workflow to improve health and health care decisions and outcomes. CDS should involve input from and be well accepted by end-users, and should support rather than detract from their workflow." It also states that "CDS should involve input from and be well accepted by end-users," and that it should "make business sense" (14).

These recommendations are based on evidence that suggests that certain CDS features lead to a significant increase in the proportion of patients receiving appropriate, guideline-based care (34). A systematic review of features of CDS systems critical for improving clinical practice also demonstrates that automatic provision of decision support as part of clinician workflow, provision of recommendations rather than just assessments, provision of decision support at the time and location of decision making, and use of a computer to generate decision support lead to improved patient care (21).

We believe that, through a systematic process, we have developed a pragmatic set of recommendations that are aligned with those suggested by the developers of the Roadmap. The recommendations that we have derived address the three pillars of the Roadmap that are expected to help fully realize the promise of CDS: best knowledge is available when needed (Pillar 1), tools produce significant value while making financial and operational sense to end-users and purchasers (Pillar 2), and interventions and clinical knowledge undergo continuous improvement (Pillar 3).

We have accomplished this through a process similar to FPI, which is in turn based on standard quality improvement principles. Our methods include our prior work related to the development of a set of tools, a literature search to explore existing CDS applications in the field, our discussions as an internal workgroup, and the workshop with our multidisciplinary advisory panel. Through this spectrum of activities, we have identified several requirements of a CDS for use in CKD management: general system requirements, clinician–system interaction requirements, requirements related to clinician communication, patient decision support requirements, and auxiliary requirements for a CDS in the context of CKD management. In addition, we have stipulated the knowledge needs for the most typical use case scenario in CKD management (i.e. the comanagement of a patient with CKD by a general internist and a consulting nephrologists).

Other researches have emphasized that ideal information systems should support knowledge-based decisions, possess evaluation/ reporting capability, evolve with the health care system, be sustainable, and be nonintrusive. They also claim that the major barriers to optimal use of decision support are that it does not fit into workflow; is slow and inflexible; and is associated with problems of information overload, lack of data integration (35). A decision-support system aimed at coordinating care of several chronic diseases should provide a consistent interface across various chronic diseases, and should be modifiable and expandable (36,37). A recent review of CKD in the United Kingdom (34) defines salient issues related to "information strategy" for CKD. These include integrating clinical records and calculating GFR, using existing patient data to automate CKD identification, providing performance feedback to providers, and using a decision support system. We note that the recommendations set forth by our advisory panel meet the information needs stated by Keble at al.

An existing literature review describes several determinants of successful dissemination and implementation of innovations. These include features of the innovation (e.g., compatibility, low complexity, adaptability), characteristics of the system in which the innovation is expected to be implemented (e.g., system antecedents, leadership, and vision), and the implementation process itself (e.g., dedicated resources, attention to human resource issues, feedback) (39). The results of our brainstorming session include several of these factors, and we believe that they are critical for successful adoption of a CDS in CKD.

We believe that a CDS developed on the basis of the requirements stipulated by our advisory panel will satisfy recommendations of other researchers in the field, and meet the needs of a wide spectrum of users: health care system administrators, nephrologists, non-nephrologists, ancillary providers and CKD patients. Local information technology groups often build customized CDS applications that may be used within their existing electronic medical record systems or as independent CDS systems. As some of them decide to design CKD-specific CDS, they may seek advice from clinicians who could propose many of the requirements described in this report.

The systematic process that we have described in this report also responds to the "gap between the current state of CDS and the full promise of CDS" (40). As such, our approach toward developing credible recommendations for a CDS should serve as guidance to other individuals interested in identifying the requirements of a CDS system for the management of any other chronic medical condition through relatively minor modifications to the CKD-specific CDS.

Despite their broad range of expertise and extensive experience, it is possible that our advisory panel of 12 members may not have uncovered the entire spectrum of CDS requirements for CKD management. These gaps may be discovered only when the specifications developed here are moved to the next phase: an actual CDS system implementation on the basis of recommendations in this report. Nevertheless, we believe that during this exercise we have developed a pragmatic process that should be used as the preliminary step for the development of any CDS application.

Conclusion
In conclusion, we consider our approach toward developing recommendations for a CDS for management of individuals with CKD to be pragmatic and realistic. The results of our brainstorming session results are aligned with the suggestions of the Roadmap for National Action on Clinical Decision Support. Finally, considering the growing burden of CKD and the potential healthcare and resource impact of guideline implementation through a CDS, the relevance of this first systematic step in its development cannot be understated.


   Appendix A
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Appendix A
Appendix B
 Disclosures
 References
 


   Appendix B
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Appendix A
 Appendix B
Disclosures
 References
 
Participants in Decision Support Systems for Chronic Kidney Disease Advisory Meeting

Meenal Patwardhan –Principal Investigator, Methodologist

David Matchar –Investigator, Internist

Uptal Patel –Investigator, Nephrologist

David Lobach –Investigator, Endocrinologist

Kensaku Kawamoto –Investigator, Community & Family Medicine - Informatics

Asif Ahmad –Advisor, VP & Chief Information Officer, DUHS

David Edelman –Advisor, Internist

Robert Gutman –Advisor, Nephrologist

Paul Lee –Advisor, Ophthalmologist

Franklin Maddux –Advisor, Nephrologist

Maureen Velazquez –Advisor, Strategic Services Assoc., Hosp Cost Acct/Finance Mgt

Kimberly Yarnall –Advisor, Internist

Dev Kalyan –Advisor, Lawyer

Robert Annechiarico –Advisor, Director of Cancer Center Biostatistics and Information

David Butterly –Advisor, Nephrologist

William Haley –Advisor, Nephrologist

John Middleton –Advisor, Nephrologist

Michael Russell –Advisor, Assoc. Chief Information Officer, DUHS


   Disclosures
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Appendix A
 Appendix B
 Disclosures
References
 
The Principle Investigator, Meenal Patwardhan, maintained a full-time position in Duke University Medical Center during the project period 01/01/2007-04/30/07. This study was supported by Abbott Laboratories.


   Acknowledgments
 
None.


   Footnotes
 
Published online ahead of print. Publication date available at www.cjasn.org.

Received May 29, 2008. Accepted October 7, 2008.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Appendix A
 Appendix B
 Disclosures
 References
 

  1. Coresh J, Astor BC, Greene T, Eknoyan G, Levey AS: Prevalence of chronic kidney disease and decreased kidney function in the adult US population: Third National Health and Nutrition Examination Survey. Am J Kidney Dis41 :1 –12,2003[Medline]
  2. United States Renal Data System: Annual Data Report: Atlas of End-Stage Renal Disease in the United States. http://www.usrds.org/adr.htm. Accessed February 4,2007
  3. Bradbury BD, Fissell RB, Albert JM, Anthony MS, Critchlow CW, Pisoni RL, Port FK, Gillespie BW: Predictors of early mortality among incident US hemodialysis patients in the Dialysis Outcomes and Practice Patterns study (DOPPS). Clin J Am Soc Nephrol2 :89 –99,2007[Abstract/Free Full Text]
  4. Kausz AT, Khan SS, Abichandani R, Kazmi WH, Obrador GT, Ruthazer R, Pereira BJ: Management of patients with chronic renal insufficiency in the Northeastern United States. J Am Soc Nephrol12 :1501 –1507,2001[Abstract/Free Full Text]
  5. Nissenson AR, Collins AJ, Hurley J, Petersen H, Pereira BJ, Steinberg EP: Opportunities for improving the care of patients with chronic renal insufficiency: Current practice patterns. J Am Soc Nephrol12 :1713 –1720,2001[Abstract/Free Full Text]
  6. Patwardhan M, Samsa G, Matchar D, Haley W: Advanced chronic kidney disease practice patterns among nephrologists and non-nephrologists: A database analysis. Clin J Am Soc Nephrol:02600706 ,2007
  7. National Kidney Foundation: K/DOQI clinical practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. Am J Kidney Dis39 :s1 –s246,2002[Medline]
  8. Renal Physicians Association: Renal Physicians Association's Appropriate Preparation of the Patient for Renal Replacement Therapy: Clinical Practice Guideline Number 3, Rockville, MD, Renal Physicians Association,2002
  9. Grimshaw J, Eccles M, Thomas R, MacLennan G, Ramsay C, Fraser C, Vale L: Toward evidence-based quality improvement: Evidence (and its limitations) of the effectiveness of guideline dissemination and implementation strategies 1966–1998. J Gen Intern Med21 :S14 –S20,2006
  10. Locatelli F, Andrulli S, Del Vecchio L: Difficulties of implementing clinical guidelines in medical practice. Nephrol Dial Transplant15 :1284 –1287,2000[Free Full Text]
  11. Weingarten S: Using practice guideline compendiums to provide better preventive care. Ann Intern Med130 :454 –458,1999[Free Full Text]
  12. Cabana MD, Rand CS, Powe NR, Wu AW, Wilson MH, Abboud PA, Rubin HR: Why don't physicians follow clinical practice guidelines? A framework for improvement. JAMA282 :1458 –1465,1999[Abstract/Free Full Text]
  13. Institute of Medicine, Committee on Data Standards for Patient Safety (CoDSfPS): Patient Safety: Achieving a New Standard for Care, Washington DC, National Academies Press,2004
  14. Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE: A roadmap for national action on clinical decision support. J Am Med Inform Assoc14 :141 –145,2007[CrossRef][Medline]
  15. Osheroff JA, Pifer EA, Teich JM, Sittig DF and Jenders RA: Improving Outcomes with Clinical Decision Support: An Implementer's Guide, Chicago, IL, Health Information and Management Systems Society,2005
  16. Chertow GM, Lee J, Kuperman GJ, Burdick E, Horsky J, Seger DL, Lee R, Mekala A, Song J, Komaroff AL, Bates DW: Guided medication dosing for inpatients with renal insufficiency. JAMA286 :2839 –2844,2001[Abstract/Free Full Text]
  17. Bosworth HB, Olsen MK, Oddone EZ: Improving blood pressure control by tailored feedback to patients and clinicians. Am Heart J149 :795 –803,2005[CrossRef][Medline]
  18. Balas EA, Krishna S, Kretschmer RA, Cheek TR, Lobach DF, Boren SA: Computerized knowledge management in diabetes care. Med Care42 :610 –621,2004[CrossRef][Medline]
  19. Matchar DB, Patwardhan MB, Samsa GP, Haley WE: Facilitated process improvement: An approach to the seamless linkage between evidence and practice in CKD. Am J Kidney Dis47 :528 –538,2006[CrossRef][Medline]
  20. Renal Physicians Association: The RPA Advanced CKD Patient Management Toolkit, Rockville, Maryland,2006
  21. Kawamoto K, Houlihan CA, Balas EA, Lobach DF: Improving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to success. BMJ330 :765 ,2005[Abstract/Free Full Text]
  22. Osborn A: Applied Imagination: Principles and Procedures of Creative Problem-Solving, New York, Scribner's,1957
  23. Crandall R (ed.): Break-Out Creativity: Bringing Creativity to the Workplace, Corte Madera, CA, Select Press for Association for Innovation Management,1998
  24. Galanter WL, Didomenico RJ, Polikaitis A: A trial of automated decision support alerts for contraindicated medications using computerized physician order entry. J Am Med Inform Assoc12 :269 –274,2005[CrossRef][Medline]
  25. Nash IS, Rojas M, Hebert P, Marrone SR, Colgan C, Fisher LA, Caliendo G, Chassin MR: Reducing excessive medication administration in hospitalized adults with renal dysfunction. Am J Med Qual20 :64 –69,2005[Abstract/Free Full Text]
  26. Rind DM, Safran C, Phillips RS, Wang Q, Calkins DR, Delbanco TL, Bleich HL, Slack WV: Effect of computer-based alerts on the treatment and outcomes of hospitalized patients. Arch Intern Med154 :1511 –1517,1994[Abstract/Free Full Text]
  27. Oppenheim MI, Vidal C, Velasco FT, Boyer AG, Cooper MR, Hayes JG, Frayer WW: Impact of a computerized alert during physician order entry on medication dosing in patients with renal impairment. Proc AMIA Symp:577 –581,2002
  28. Klebe B, Hobbs H, Irving J, Cooley R, Stevens P, O'Donoghue D, Middleton R, Farmer C: A computer-based decision support tool to manage CKD in primary care. Presented at the American Society of Nephrology Annual Meeting, San Diego, CA, November 14-19,2006
  29. InferMed Ltd.: Arezzo(R) Technical White Paper. 2007. Available at www.InferMed.com. Accessed on February 2,2007
  30. Raghavan SR, Ladik V, Meyer KB: Developing decision support for dialysis treatment of chronic kidney failure. IEEE Trans Inf Technol Biomed9 :229 –238,2005[CrossRef][Medline]
  31. Gamewood Inc.: Available at: http://www.gamewood.com/build.php?page_id=1&section_id=1. Accessed on February 2,2007
  32. Allscripts: Available at: http://www.allscripts.com/. Accessed on February 2,2007
  33. Kawamoto K, Lobach DF: Proposal for fulfilling strategic objectives of the U.S. Roadmap for national action on clinical decision support through a service-oriented architecture leveraging HL7 services. J Am Med Inform Assoc14 :146 –155,2007[CrossRef][Medline]
  34. Shiffman RN, Liaw Y, Brandt CA, Corb GJ: Computer-based guideline implementation systems: A systematic review of functionality and effectiveness. J Am Med Inform Assoc6 :104 –114,1999[Abstract/Free Full Text]
  35. Hynes DM: VIReC CyberSeminar Series. Clinical Informatics: Attributes of an Ideal Informatics System with Highlights from a State of the Art Conference. Available at http://www.hsrd.research.va.gov/publications/citations/abstract.cfm?Identifier=22628. Accessed February 4,2007
  36. Warren JR, Beliakov GV, Noone JT, Frankel HK: Chronic disease coordinated care planning: Flexible, task-centered decision support. Top Health Inf Manage20 :52 –68,1999[Medline]
  37. Adams R, Ruffin R, Smith B, Campbell D, Dippy S: Problems and some solutions in adapting clinical practice guidelines for asthma patient management into a computerised management system: The Western Region Asthma Pilot Project (WRAPP). Inform Healthc Aust7 :16 –21,1998
  38. Klebe B, Farmer C, Cooley R, de Lusignan S, Middleton R, O'Donoghue D, New J, Stevens P: Kidney disease management in UK primary care: Guidelines, incentives, and information technology. Family Practice24 :330 –335,2007[Abstract/Free Full Text]
  39. Greenhalgh T, Robert G, Macfarlane F, Bate P, Kyriakidou O: Diffusion of innovations in service organizations: Systematic review and recommendations. Milbank Q82 :581 –629,2004[CrossRef][Medline]
  40. Sim I, Gorman P, Greenes RA, Haynes RB, Kaplan B, Lehmann H, Tang PC: Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc8 :527 –534,2001[Abstract/Free Full Text]




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