Developing Nursing Computer Interpretable Guidelines: a Feasibility Study of Heart Failure Guidelines in Homecare. Maxim Topaz, RN, MA, PhD student1, Erez Shalom, MSc, PhD Student2, Ruth MastersonCreber, RN, MSc, PhD student1 , Kavita Rhadakrishnan, RN, MSEE, PhD3, Karen A. Monsen, PhD, RN, FAAN4, Kathryn H. Bowles RN, PhD, FAAN, FACMI1. 1 University of Pennsylvania School of Nursing, Philadelphia, PA; 2Ben Gurion University of the Negev, Medical Informatics Research Center, Beer Sheva, Israel; 3University of Texas School of Nursing, Austin, TX; 4University of Minnesota School of Nursing, Minneapolis, MN. Abstract Homecare is the fastest growing healthcare sector and evidence based information systems are critically needed. Nurses provide most of the care in homecare setting, yet there is a lack of knowledge on the feasibility of applying existing methodologies to generate computer interpretable nursing guidelines for home care. This study examined the feasibility of encoding homecare nursing heart failure guideline into a computer interpretable format. First, we achieved experts’ consensus on the relevant guideline. Then, after training on the graphical tool for gradual knowledge specification (Gesher), we generated a comprehensive, hierarchical and time-oriented computer interpretable guideline using one of the guideline modeling languages (Asbru). The final guideline included 167 recommendations and experts’ evaluation confirmed the adequacy of guideline knowledge representation. Future work should expand the applicability of our methodology and tools to nursing specialties other than heart failure and develop methods for comprehensive quality evaluation of the resulting guidelines. Introduction During the last decades, providing care based on evidence-based clinical Guidelines (GLs) has become a gold standard for healthcare professionals in many countries [1]. Studies have shown that applying GLs in practice has the potential to improve the quality of care processes and patient outcomes [1,2]. However, there is still a wide gap between development of GLs and their implementation in clinical practice, with some recommendations never being implemented [2,3]. To bridge this gap, GLs might be incorporated into clinical decision support tools available at the point of care for healthcare practitioners [4,5]. In the U.S., legislators have recently recognized the immense potential of health information technologies in general, and clinical decision support in particular, to improve care processes and outcomes. According to the American Recovery and Reinvestment Act of 2009, the majority of healthcare providers are financially incentivized to become meaningful users of certified electronic health records by 2015, otherwise they will face financial penalties [6]. Clinical decision support tools are currently one of the requirements (Meaningful use stage 2) of the certified electronic health records [7]. Several methodologies were developed to assist with the creation of comprehensive Computer Interpretable Guidelines (CIG) that can be incorporated in clinical decision support tools [8]. Although nurses are one of the largest sectors of healthcare providers, only a few informatics projects so far have focused explicitly on evaluating the applicability of the existing methodologies for CIG generation in the domain of nursing knowledge. Even fewer of these projects have addressed nursing CIG creation for community -based settings such as homecare, a vital healthcare venue as more and more care is provided in patients’ homes [9]. The lack of validated ways to generate nursing specific CIG might hinder the advancement of the widespread health information technology efforts and decrease the effect of electronic health records on processes of care and patients outcomes. Background Heart failure continues to be one of the most expensive chronic diseases among the elderly due to the high cost of management and readmission rates [10,11]. Nurses in about 11,000 homecare agencies across the U.S. are key healthcare professionals who provide homecare services for about 2.8 million patients with heart failure annually [12], and therefore are expected to be experts in managing this complex chronic condition [13]. However, this is not the current clinical reality; alarming results of recent studies show that home health nurses often lack knowledge on
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best practices for treating patients with heart failure [13-15]. Wide gaps in nurses’ expertise [14,15], could have implications on patient outcomes [14-16]. To solve the problem, evidence-based recommendations from GL could be incorporated into homecare agencies’ electronic health records for clinician decision support at the point of care. Homecare differs significantly from inpatient settings; it is a unique outpatient setting with its own knowledge base (e.g. focus on chronic health conditions), clinical roles (e.g. nurses provide most of the care) and treatment pace (e.g. relatively infrequent meetings between providers and patients). Unfortunately, there is a lack of published reports on how to integrate homecare specific nursing GL into electronic format. Moreover, there are no reports on the applicability of the current methodologies (such as GL modeling languages) for GL knowledge acquisition for homecare nursing. As healthcare is moving to patients’ homes, there is a critical need for more evidence on clear ways to incorporate the most recent evidence based knowledge to everyday nursing practice. The overall purpose of this novel study was to explore the feasibility of encoding the nursing practice knowledge presented in a GL into a computer interpretable format. We focused on heart failure homecare relevant nursing guidelines extracted from American Heart Association [17] and Heart Failure Society of America GL [18]. Our team was one of the first to apply the process of knowledge acquisition to create homecare nursing specific heart failure CIG. The aim of this paper is to describe the challenges and solutions experienced during the process of CIG creation and evaluate the applicability of the process for nursing GL implementation. Methods and procedure: Guideline knowledge acquisition process To present GL in a computer interpretable format, there is a need in knowledge acquisition process defined as “transformation of knowledge from the forms in which it is available in the world into forms that can be used by a knowledge system” [19]. During this process, a distinction is made between tacit clinical knowledge and conscious, explicit knowledge. The tacit knowledge is the kind we would most likely want to incorporate into our knowledge database repository, also called the knowledge base. In textual GL much of the knowledge is implicit, and should be explicitly specified and defined through the knowledge acquisition process. This process is usually performed by knowledge engineers (familiar with the knowledge base domain); clinical experts (familiar with the clinical domain); and clinical editors (clinically trained editors with knowledge in informatics) [20]. The final goal is to make the implicit GL knowledge explicit [19]. For the GL framework we used the Digital Electronic Guideline Library (DeGeL) [21] and its’ tools that support the design and runtime tasks involved in guideline-based care. Within DeGeL, Shalom and colleagues [20] have previously developed and validated a three-phase knowledge acquisition methodology for specification of GLs. We implemented a modified version of this methodology that included the following steps: 1) initiating an expert consensus, and 2) training the clinical editor and gradually formalizing the GL into a computer interpretable format. Step 1: Experts’ consensus The first stage towards GL specification is identifying the best available source GL. In case no specific GL for the clinical condition exists, it is common to create consensus GL using experts’ opinions [20]. In this study, we aimed to generate heart failure CIG for nurses in homecare settings. Unfortunately, we did not identify any nursing specific homecare heart failure GL in the literature. To construct the GL, we examined the best available heart failure GL sources (American Heart Association GL [17] and Heart Failure Society of America GL [18]) and extracted homecare relevant nursing GL. To evaluate the relevance of each of the GL recommendation for homecare nursing, we invited four experts in heart failure homecare. Two experts were PhD prepared nurses from academic settings and had more than 20 years of experience in heart failure research. The two additional experts were practice experts with more than five years clinical and managerial experience in homecare settings and had advanced education in heart failure care (one nurse was PhD prepared while the other was Heart Failure Nurse Clinical Specialist). The experts revised the GL through series of Delphi rounds until 100% agreement was achieved on the final set of recommendations. Step 2: Generating computer interpretable nursing GL To generate nursing CIG, we used DeGeL's software providing a graphical framework for specification of clinical knowledge, called Gesher [22]. The clinical editor (MT) and the clinical team were trained by the knowledge engineer (ES) during three online meetings. Gesher supports a number of GL modeling languages; for this project we used Asbru [23], which is a skeletal plan-representation language for the modeling of time-oriented, hierarchical treatment GL. We chose Asbru because it provides a powerful mechanism to express extended hierarchical time-
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oriented actions and plans caused by extended time-oriented states of an observed clinical event or symptom. For example, our GL included sections on initial and serial assessments needed for heart failure patients (such as blood pressure, pulse etc.), therefore a formal element that represents the assessment timing (initial versus performed at each following visit) was necessary. In addition, choosing Asbru was particularly applicable for our GL since we needed the ability to represent many actions and plans that need to be executed in parallel at a specific time point. For example, our GL included 19 parallel recommendations for the initial nursing assessment of heart failure patients admitted to homecare (e.g. “Assess patient’s ability to perform activities of daily living during the initial visit” and “Assess current and past history of alcohol use” [17]). Finally, the quality of GL knowledge coding from text to the formalized format was evaluated by one of the clinical experts. Results Step 1: Experts’ consensus We extracted the most relevant GL recommendations from both of the sources (American Heart Association GL [17] and Heart Failure Society of America GL [18]). After excluding duplicate recommendations, we had 162 recommendations relevant to homecare nursing, which constituted about 45% of the total guideline recommendations. After the first revision, the experts agreed that 85% of the recommendations were relevant to homecare nursing and their opinions were split on the remaining 15% of recommendations. The 15% of recommendations that the experts disagreed on included guidelines that could be considered outside the scope of practice of homecare nurses (such as complex heart failure pharmacological interventions or planning of complex medical procedures). During the second revision (Delphi round), experts shared their arguments for including or excluding each of the disputed GL recommendations. After this round, 100% agreement on the recommendations was achieved and 9 recommendations were excluded by a majority decision (three experts out of four). A detailed report on the process of consensus building will be presented elsewhere [24]. The finalized guideline included 153 recommendations. Based on source GL categories, our recommendations were divided into five groups (Generic- 50 recommendations; Minority populations-13 recommendations; Normal Ejection Fraction- 12 recommendations; Reduced Ejection Fraction- 63 recommendations; Co-morbidities- 24 recommendations) and further subgroups. Step 2: Generating computer interpretable nursing GL During the first two instructional online meetings, the knowledge engineer (ES) showed the clinical editor (MT) and team how to use the tool and build a GL for demonstration purposes. In the last meeting the clinical editor used Gesher, and the knowledge engineer observed the work and provided directions or suggestions, when needed. Later on, most of the work was conducted by the clinical editor and knowledge engineer. First, we needed to partially formalize the logic embedded in each of the GL recommendations. To accomplish that, we created a table where each of the GL recommendations was presented using a formal logical structure- see example in Table 1. We also defined - and disambiguated when needed- each of the GL concepts. For example, we used American Heart Association instructions to provide an explicit definition of the concept hypertension as a systolic blood pressure ≥140 mmHg or a diastolic blood pressure ≥90 mmHg [25]. Table 1: Initial logical formalization of GL recommendations Source GL (section) HFSA (6.6 Recommendations for Diet and Nutrition) HFSA (6.14 Recommendations for Routine Health Care Maintenance)
GL Recommendation (free text)
GL Recommendation (logical formalization)
Document type and dose of naturoceutical products. Naturoceutical (e.g. ephedra, ephedrine, or its metabolites) use is not recommended for symptomatic HF patients. Recommend pneumococcal vaccine and annual influenza vaccination in all patients with HF in the absence of known contraindications.
1. IF Heart Failure THEN document type and dose of naturoceutical products. 2. IF Heart Failure THEN recommend discontinue the use of naturoceutical products (e.g. ephedra, ephedrine, or its metabolites) 1. IF Heart Failure THEN perform/recommend Pneumococcal vaccination AND perform/recommend annual Influenza vaccination
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Next, we used Gesher to generate a semi-formal GL representation that included control structures such as sequential or parallel ordering of sub-plans. It was done using the “Hierarchical Plan Builder” interface in Gesher where expert clinicians are supposed to specify the procedural aspects of the GL. Figure 1 provides an example of our procedural knowledge specification. As presented, the "Nonpharmacologic Management and Healthcare Maintenance" sub-plan of the heart failure GL was composed of different parallel recommendations. Each of the recommendations was defined as pertaining to a certain type of clinical actions specified in Asbru (e.g. Education, Procedure, etc.- see Frame 1) and added to the hierarchical or parallel flowchart of suggested nurse actions (Frame 2). For each recommendation or plan, properties such as “eligibility criteria” were defined, when needed (Frame 3). For composite plans including several GL recommendations, the procedural hierarchy (such as sequential or parallel order of recommendations) was also specified (Frame 4). Gesher enabled an explicit representation of a sub-plan hierarchy in a tree-view display (Frame 5). We also identified a list of declarative concepts that were further specified in the following section (Frame 6).
Figure 1: Semi-formal guideline specification using the “Hierarchical Plan Builder” interface. At the next phase of the GL specification process, we specified the declarative concepts of the GL. Most of these concepts were textually defined during the GL preparation steps. We used Gesher’s specific interface called the “Knowledge Map”. This interface allowed us to describe each concept in the guideline by several available attributes. These attributes include a textual description; a description of the type of concepts; a description of the possible values; and a definition of temporal aspects such as the period during which a certain measurement of the concept value is valid in the context of applying this guideline (i.e., for how long is a specific measurement valid). For example, see the specification of the concept of hypertension on Figure 2. According to American Heart Association, hypertension is a high blood pressure defined as a systolic blood pressure ≥140 mmHg or a diastolic blood pressure ≥90 mmHg (ref) (Frame 1). The concepts and their relations are graphically displayed in the map area (Frame 2) and defined numerically (Frame 3). All the GL concepts were successfully specified.
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Figure 2: Guideline concepts specification using the “Knowledge Map” interface. Finally, Gesher allowed us to easily present the hierarchical structure of the resulted GL. Figure 3 shows the tree view of the GL hierarchy (Frame 1) and the interactive flow-chart available for each of the created sub-plans (frame 2).
Figure 3: Interface for interactive exploration of hierarchical diagrams of clinical guidelines The final computer interpretable nursing specific heart failure GL included 167 recommendations, which is about 9% more than the original expert consensus GL (that included 153 recommendations). We had more recommendations in the final GL product mostly because some of the original recommendations were further specified into several formal parts. For example, free text GL recommendation that stated “Document type and dose of naturoceutical products. Naturoceutical (e.g. ephedra, ephedrine, or its metabolites) use is not recommended for
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symptomatic HF patients.” was divided into two recommendations on documentation and use of the naturoceutical products. The clinical editor invested an additional 10 hours to complete the GL. The final set of 167 recommendations consisted of: 102 (61%) drug related recommendations; 43 (26%) recommendations on observations (including lab results, clinical tests, physiological measures etc.); 20 (12%) education suggestions for heart failure patients; and 2 (1%) recommendations for procedures. Evaluation Most of the GL specification work was conducted through an active collaboration between the knowledge engineer and clinical editor. At each phase of the process, the quality of knowledge specification was also discussed between the team members. Clinical experts were consulted several times throughout the specification process. When the formalization process was completed, one of the experts evaluated the quality and consistency of knowledge representation in the final product. Qualitative criteria for guideline evaluation included: 1. the level of similarity between free text and computer interpretable guidelines and; 2. clarity and appropriateness of guideline language in the computer interpretable format. According to the expert’s opinion, the GL knowledge was adequately presented in the final product. Discussion The goal of this study was to examine the feasibility of nursing GL specification with a GL modeling language (Asbru), using a graphical framework for specification of clinical knowledge (Gesher). One of the study prerequisites was having a GL that is appropriate for the population of interest and setting. Unfortunately, no GL exists specifically for homecare nurses treating almost 2.8 million heart failure patients each year. To overcome this challenge, we combined a team of clinical experts that reviewed the potential guideline sources and decided whether these are applicable for homecare nurses. Because homecare nurses’ clinical responsibilities differ between states and settings, one of the major challenges of the GL review process was to generate a generalizible set of recommendations. Our goal was to generate a widely applicable set of heart failure nursing specific guidelines that should be reviewed and customized by local nurses in each particular state and home health agency. After an initial Delphi round, experts’ opinions were split on 15% of the recommendations mostly because of unclear boundaries of nursing scope of practice. To resolve that, we asked our experts to select recommendations based on an “ideal” world situation in which nurses are allowed to practice to the full scope of their practice. We also underlined that our GL are intended to be used in an electronic format as a form of clinical decision support, ideally at the point of care. Although currently there are no legislative acts requiring nurses in home health to use clinical decision support systems (the Meaningful Use regulation focuses mostly on inpatient or primary care settings), it is expected that in the near future those tools will become a necessity, if not a requirement. Our approach to GL review resulted in a comprehensive, hierarchical, nursing specific heart failure GL that included 153 recommendations on clinical assessments, treatments and pharmacological aspects of care. This was the longest step of our process that took approximately 6 months. The next phase focused on preparing GL for integration into a computer interpretable format. At this step, we partially formalized the logic embedded in each of the GL recommendations and defined GL concepts. This preparation phase was also time consuming because we needed to consult with our clinical experts on the explicit definitions for each of the GL concepts. With the directions from experts, our team was able to explicitly define and logically represent the vast majority of GL concepts and recommendations. This step lasted approximately 2 months. During the next phases, we used Gesher to further specify the resulting heart failure GL. The clinical editor that conducted most of the GL specification, was a PhD student and had some experience in the field of health informatics, however, he never used Gesher before this project. The three online introductory meetings provided the clinical editor with a sufficient set of tools to use the software. Later on in the process, we conducted weekly meetings to overview the progress and quality of the knowledge specification process. We did not encounter any major issues during the creation of semi-formal GL representation. Using Gesher’s tools, such as the “Hierarchical Plan Builder”, enabled us to gradually specify the heart failure nursing GL. Also, Gesher offered intuitive and relatively easy to use tools to generate Asbru based, hierarchical GL. We constructed a rich set of ordering of GL sub-plans some of which were sequential, parallel, or periodical (as defined in Asbru). The hierarchical structure of the GL was further enhanced by the ability to specify eligibility criteria (or filter condition
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in Asbru) for each of the recommendations. For example, some recommendations were intended only for elderly patients or patients with severe heart failure (New York Heart Association class III or IV). The “Knowledge Map” interface allowed us, in a reasonable time, to represent, examine and confirm all the GL concepts. The relatively easy specification of the guideline concepts can be also explained by the physiologically oriented focus of the heart failure GL. Most of the recommendations were based on physiological phenomena such as blood pressure or cardiac arrhythmia. Future work is needed to fully understand the applicability of similar tools to enable specification of more holistically oriented nursing concepts, such as grief or patient’s motivation for self care. Overall, the project of CIG creation lasted about 11 months with the longest step being the experts’ consensus. The Gesher software is available for a licensed use (for more details contact [email protected]). The quality of knowledge integration was adequate, according to the expert’s opinion. The experienced knowledge engineer was also satisfied with the final product. However, because of the exploratory nature of our project, no further attempts to evaluate CIG quality were implemented. Similar projects might require a more thorough expert evaluation, preferably by an interdisciplinary group of experts including academics, practicing clinicians and knowledge engineers). Another possible scenario is evaluation of the project through application of the CIG to a series of comprehensive case studies that include wide range of practical clinical cases. In the upcoming final step of our project, we will encode our semi-formal CIG to a fully formal, standardized format using one of the 13 American Nurses Association recognized standardized nursing terminologies. Our terminology of choice is the Omaha System, a well established terminology developed through more than three decades of research and refinement [26]. This terminology is particularly applicable for the needs of our project as it focuses on care provided in community-based settings. The Omaha System is currently used in many electronic health record products internationally and in the U.S. to provide a standardized interface representation and reference mapping for nursing care. It is also integrated into the U.S. National Library of Medicine Unified Medical Language System (UMLS) and the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) [26]. This final step is necessary to enable standardized representation of our heart failure CIG at the point of care for nurses. We are also considering several homecare venues for implementation of our heart failure CIG as a clinical decision support tool for nurses. Limitations Our project is not without limitations. First, the original guideline created as a result of experts consensus, might be biased by experts’ opinions. Also, the resulted CIG was physiologically oriented (i.e. focused on biological measures such as blood pressure and sodium blood levels) while only a few more abstract and holistic concepts, such as grief, were integrated into the final CIG. More research is needed to understand the applicability of the current tools to integrate these concepts into CIG. Lastly, we examined the feasibility of nursing CIG creation using only one modeling language (Asbru) and one graphical application for GL knowledge specification (Gesher), thus the generalizibilty of our findings might be limited. Conclusions This pioneering study demonstrates the feasibility of integrating nursing specific guidelines into a computer interpretable format using readily available tools. First, our team was able to achieve experts’ consensus on the relevant heart failure homecare nursing GL. Then we generated a complex, hierarchical and time-oriented computer interpretable GL using Asbru as a GL modeling language and Gesher as a graphical tool for gradual knowledge specification. Both of the tools proved sufficient and enabled us to create an adequate GL knowledge representation. Future work should explore the applicability of the tools for nursing specialties other than heart failure and develop methods for comprehensive quality evaluation of the resulting computer interpretable guidelines. Acknowledgments The study received financial support through the Faculty Senate Research Committee Awards, Office of Nursing Research, University of Pennsylvania, School of Nursing. We would like to gratefully acknowledge the experts who guided the selection of the heart failure guidelines: 1) Dr. Kathy Bowles RN, PhD, FAAN, FACMI; 2) Dr. Susan Mcgeary RN PhD; 3) Dr. Barbara Riegel RN, DNSc, FAAN, FAHA; 4) Ms. Katie Winkler RN, CHFN. We would also like to acknowledge that pre-doctoral funding support has been provided for Ruth Masterson Creber through NIH/NINR (1F31NR014086-01) as well as the National Hartford Centers of Geriatric Nursing Excellence Patricia G. Archbold Scholarship program.
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1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.
18. 19. 20. 21. 22. 23. 24. 25.
References Straus SE, Richardson WS, Glasziou P, Haynes. B. Evidence-based medicine: how to practice and teach EBM. 3rd ed. Edinburgh: Churchill Livingstone; Oxford press. 2006. Institute of Medicine. Knowing what works in health care: a roadmap for the nation. IOM, Washington, DC. 2008. Institute of Medicine. Crossing the quality chasm: a new health system for the 21st century. IOM, Washington, DC. 2001. Bright TJ, Wong A, Dhurjati R, Bristow E, Bastian L, Coeytaux RR, et al. Effect of clinical decision-support systems: a systematic review. Ann Intern Med 2012;157:29-43. Lobach D, Sanders GD, Bright TJ, Wong A, Dhurjati R, Bristow E, et al. Enabling health care decisionmaking through clinical decision support and knowledge management. Evid Rep Technol Assess 2012;(203):1-784. Office of the National Coordinator for HIT. Meaningful Use. 2012; Available at: http://www.healthit.gov/policy-researchers-implementers/meaningful-use. Accessed 01/01, 2013. Center for Medicare & Medicaid Services. Meaningful Use: stage 2 overview. CMS, Washington, DC.2012. Peleg M, Boxwala AA, Tu S, Zeng Q, Ogunyemi O, Wang D, et al. The InterMed approach to sharable computer-interpretable guidelines: a review. J Am Med Inform Assoc 2004;11:1-10. Institute of Medicine. The Patient-Centered Medical Home: A Path Toward Health Equity? IOM, Washington, DC. 2012. Dunlay SM, Shah ND, Shi Q, Morlan B, VanHouten H, Long KH, et al. Lifetime costs of medical care after heart failure diagnosis. Circ Cardiovasc Qual Outcomes 2011;4:68-75. Norton C, Georgiopoulou VV, Kalogeropoulos AP, Butler J. Epidemiology and cost of advanced heart failure. Prog Cardiovasc Dis 2011;54:78-85. The National Association for Home Care & Hospice. Basic statistics about homecare. 2010;202.547.7424. Albert NM, Sumodi V, Wilkinson S, Vopat L, Willis C, Bittel B, et al. Heart failure (HF): are nurses providing patients with optimal education of self-care principles? J Heart Lung Transplant. 2001;20:244. Delaney C, Apostolidis B, Lachapelle L, Fortinsky R. Home care nurses' knowledge of evidence-based education topics for management of heart failure. Heart Lung 2011;40:285-292. Albert NM. Evidence-based nursing care for patients with heart failure. AACN Adv.Crit.Care. 2006;17:170-83; quiz 184-5. Washburn SC, Hornberger CA. Nurse educator guidelines for the management of heart failure. J.Contin.Educ.Nurs. 2008;39:263-267. Hunt SA, Abraham WT, Chin MH, Feldman AM, Francis GS, Ganiats TG, et al. 2009 focused update incorporated into the ACC/AHA 2005 guidelines for the diagnosis and management of heart failure in adults: A report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines: Developed in collaboration with the International Society for Heart and Lung Transplantation. Circulation 2009;119:e391-e479. Heart Failure Society of America, Lindenfeld J, Albert NM, Boehmer JP, Collins SP, Ezekowitz JA, et al. HFSA 2010 comprehensive heart failure practice guideline. J Card Fail 2010;16:e1-194. Musen MA, Fagan LM, Combs DM, Shortliffe EH. Use of a domain model to drive an interactive knowledgeediting tool. International Journal of Human Computer Studies 1999;51:479-495. Shalom E, Shahar Y, Taieb-Maimon M, Bar G, Yarkoni A, Young O, et al. A quantitative assessment of a methodology for collaborative specification and evaluation of clinical guidelines. J Biomed Inform 2008;41:889-903. Shahar Y, Young O, Shalom E, Galperin M, Mayaffit A, Moskovitch R, et al. A framework for a distributed, hybrid, multiple-ontology clinical-guideline library, and automated guideline-support tools. J Biomed Inform 2004;37:325-344. Hatsek A, Shahar Y, Taieb-Maimon M, Shalom E, Klimov D, Lunenfeld E. A scalable architecture for incremental specification and maintenance of procedural and declarative clinical decision-support knowledge. Open Med Inform J 2010;4:255-277. Shahar Y, Miksch S, Johnson P. The Asgaard project: a task-specific framework for the application and critiquing of time-oriented clinical guidelines. Artif Intell Med 1998;14:29-51. Radhakrishnan K, Topaz M, Masterson Creber R. Adopting heart failure guidelines for nursing care in home health settings: challenges and solutions. Journal of Cardiovascular Nursing [In review] 2013. American Heart Association. Hypertension. 2013; Available at: http://www.heart.org/HEARTORG/Conditions/HighBloodPressure/High-BloodPressure_UCM_002020_SubHomePage.jsp. Accessed 03/03, 2013.
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26. Martin KS. The Omaha System: A Key to Practice, Documentation, and Information Management. St Louis, MO: Elsevier Saunders; 2005.
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best practices for treating patients with heart failure [13-15]. Wide gaps in nurses’ expertise [14,15], could have implications on patient outcomes [14-16]. To solve the problem, evidence-based recommendations from GL could be incorporated into homecare agencies’ electronic health records for clinician decision support at the point of care. Homecare differs significantly from inpatient settings; it is a unique outpatient setting with its own knowledge base (e.g. focus on chronic health conditions), clinical roles (e.g. nurses provide most of the care) and treatment pace (e.g. relatively infrequent meetings between providers and patients). Unfortunately, there is a lack of published reports on how to integrate homecare specific nursing GL into electronic format. Moreover, there are no reports on the applicability of the current methodologies (such as GL modeling languages) for GL knowledge acquisition for homecare nursing. As healthcare is moving to patients’ homes, there is a critical need for more evidence on clear ways to incorporate the most recent evidence based knowledge to everyday nursing practice. The overall purpose of this novel study was to explore the feasibility of encoding the nursing practice knowledge presented in a GL into a computer interpretable format. We focused on heart failure homecare relevant nursing guidelines extracted from American Heart Association [17] and Heart Failure Society of America GL [18]. Our team was one of the first to apply the process of knowledge acquisition to create homecare nursing specific heart failure CIG. The aim of this paper is to describe the challenges and solutions experienced during the process of CIG creation and evaluate the applicability of the process for nursing GL implementation. Methods and procedure: Guideline knowledge acquisition process To present GL in a computer interpretable format, there is a need in knowledge acquisition process defined as “transformation of knowledge from the forms in which it is available in the world into forms that can be used by a knowledge system” [19]. During this process, a distinction is made between tacit clinical knowledge and conscious, explicit knowledge. The tacit knowledge is the kind we would most likely want to incorporate into our knowledge database repository, also called the knowledge base. In textual GL much of the knowledge is implicit, and should be explicitly specified and defined through the knowledge acquisition process. This process is usually performed by knowledge engineers (familiar with the knowledge base domain); clinical experts (familiar with the clinical domain); and clinical editors (clinically trained editors with knowledge in informatics) [20]. The final goal is to make the implicit GL knowledge explicit [19]. For the GL framework we used the Digital Electronic Guideline Library (DeGeL) [21] and its’ tools that support the design and runtime tasks involved in guideline-based care. Within DeGeL, Shalom and colleagues [20] have previously developed and validated a three-phase knowledge acquisition methodology for specification of GLs. We implemented a modified version of this methodology that included the following steps: 1) initiating an expert consensus, and 2) training the clinical editor and gradually formalizing the GL into a computer interpretable format. Step 1: Experts’ consensus The first stage towards GL specification is identifying the best available source GL. In case no specific GL for the clinical condition exists, it is common to create consensus GL using experts’ opinions [20]. In this study, we aimed to generate heart failure CIG for nurses in homecare settings. Unfortunately, we did not identify any nursing specific homecare heart failure GL in the literature. To construct the GL, we examined the best available heart failure GL sources (American Heart Association GL [17] and Heart Failure Society of America GL [18]) and extracted homecare relevant nursing GL. To evaluate the relevance of each of the GL recommendation for homecare nursing, we invited four experts in heart failure homecare. Two experts were PhD prepared nurses from academic settings and had more than 20 years of experience in heart failure research. The two additional experts were practice experts with more than five years clinical and managerial experience in homecare settings and had advanced education in heart failure care (one nurse was PhD prepared while the other was Heart Failure Nurse Clinical Specialist). The experts revised the GL through series of Delphi rounds until 100% agreement was achieved on the final set of recommendations. Step 2: Generating computer interpretable nursing GL To generate nursing CIG, we used DeGeL's software providing a graphical framework for specification of clinical knowledge, called Gesher [22]. The clinical editor (MT) and the clinical team were trained by the knowledge engineer (ES) during three online meetings. Gesher supports a number of GL modeling languages; for this project we used Asbru [23], which is a skeletal plan-representation language for the modeling of time-oriented, hierarchical treatment GL. We chose Asbru because it provides a powerful mechanism to express extended hierarchical time-
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oriented actions and plans caused by extended time-oriented states of an observed clinical event or symptom. For example, our GL included sections on initial and serial assessments needed for heart failure patients (such as blood pressure, pulse etc.), therefore a formal element that represents the assessment timing (initial versus performed at each following visit) was necessary. In addition, choosing Asbru was particularly applicable for our GL since we needed the ability to represent many actions and plans that need to be executed in parallel at a specific time point. For example, our GL included 19 parallel recommendations for the initial nursing assessment of heart failure patients admitted to homecare (e.g. “Assess patient’s ability to perform activities of daily living during the initial visit” and “Assess current and past history of alcohol use” [17]). Finally, the quality of GL knowledge coding from text to the formalized format was evaluated by one of the clinical experts. Results Step 1: Experts’ consensus We extracted the most relevant GL recommendations from both of the sources (American Heart Association GL [17] and Heart Failure Society of America GL [18]). After excluding duplicate recommendations, we had 162 recommendations relevant to homecare nursing, which constituted about 45% of the total guideline recommendations. After the first revision, the experts agreed that 85% of the recommendations were relevant to homecare nursing and their opinions were split on the remaining 15% of recommendations. The 15% of recommendations that the experts disagreed on included guidelines that could be considered outside the scope of practice of homecare nurses (such as complex heart failure pharmacological interventions or planning of complex medical procedures). During the second revision (Delphi round), experts shared their arguments for including or excluding each of the disputed GL recommendations. After this round, 100% agreement on the recommendations was achieved and 9 recommendations were excluded by a majority decision (three experts out of four). A detailed report on the process of consensus building will be presented elsewhere [24]. The finalized guideline included 153 recommendations. Based on source GL categories, our recommendations were divided into five groups (Generic- 50 recommendations; Minority populations-13 recommendations; Normal Ejection Fraction- 12 recommendations; Reduced Ejection Fraction- 63 recommendations; Co-morbidities- 24 recommendations) and further subgroups. Step 2: Generating computer interpretable nursing GL During the first two instructional online meetings, the knowledge engineer (ES) showed the clinical editor (MT) and team how to use the tool and build a GL for demonstration purposes. In the last meeting the clinical editor used Gesher, and the knowledge engineer observed the work and provided directions or suggestions, when needed. Later on, most of the work was conducted by the clinical editor and knowledge engineer. First, we needed to partially formalize the logic embedded in each of the GL recommendations. To accomplish that, we created a table where each of the GL recommendations was presented using a formal logical structure- see example in Table 1. We also defined - and disambiguated when needed- each of the GL concepts. For example, we used American Heart Association instructions to provide an explicit definition of the concept hypertension as a systolic blood pressure ≥140 mmHg or a diastolic blood pressure ≥90 mmHg [25]. Table 1: Initial logical formalization of GL recommendations Source GL (section) HFSA (6.6 Recommendations for Diet and Nutrition) HFSA (6.14 Recommendations for Routine Health Care Maintenance)
GL Recommendation (free text)
GL Recommendation (logical formalization)
Document type and dose of naturoceutical products. Naturoceutical (e.g. ephedra, ephedrine, or its metabolites) use is not recommended for symptomatic HF patients. Recommend pneumococcal vaccine and annual influenza vaccination in all patients with HF in the absence of known contraindications.
1. IF Heart Failure THEN document type and dose of naturoceutical products. 2. IF Heart Failure THEN recommend discontinue the use of naturoceutical products (e.g. ephedra, ephedrine, or its metabolites) 1. IF Heart Failure THEN perform/recommend Pneumococcal vaccination AND perform/recommend annual Influenza vaccination
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Next, we used Gesher to generate a semi-formal GL representation that included control structures such as sequential or parallel ordering of sub-plans. It was done using the “Hierarchical Plan Builder” interface in Gesher where expert clinicians are supposed to specify the procedural aspects of the GL. Figure 1 provides an example of our procedural knowledge specification. As presented, the "Nonpharmacologic Management and Healthcare Maintenance" sub-plan of the heart failure GL was composed of different parallel recommendations. Each of the recommendations was defined as pertaining to a certain type of clinical actions specified in Asbru (e.g. Education, Procedure, etc.- see Frame 1) and added to the hierarchical or parallel flowchart of suggested nurse actions (Frame 2). For each recommendation or plan, properties such as “eligibility criteria” were defined, when needed (Frame 3). For composite plans including several GL recommendations, the procedural hierarchy (such as sequential or parallel order of recommendations) was also specified (Frame 4). Gesher enabled an explicit representation of a sub-plan hierarchy in a tree-view display (Frame 5). We also identified a list of declarative concepts that were further specified in the following section (Frame 6).
Figure 1: Semi-formal guideline specification using the “Hierarchical Plan Builder” interface. At the next phase of the GL specification process, we specified the declarative concepts of the GL. Most of these concepts were textually defined during the GL preparation steps. We used Gesher’s specific interface called the “Knowledge Map”. This interface allowed us to describe each concept in the guideline by several available attributes. These attributes include a textual description; a description of the type of concepts; a description of the possible values; and a definition of temporal aspects such as the period during which a certain measurement of the concept value is valid in the context of applying this guideline (i.e., for how long is a specific measurement valid). For example, see the specification of the concept of hypertension on Figure 2. According to American Heart Association, hypertension is a high blood pressure defined as a systolic blood pressure ≥140 mmHg or a diastolic blood pressure ≥90 mmHg (ref) (Frame 1). The concepts and their relations are graphically displayed in the map area (Frame 2) and defined numerically (Frame 3). All the GL concepts were successfully specified.
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Figure 2: Guideline concepts specification using the “Knowledge Map” interface. Finally, Gesher allowed us to easily present the hierarchical structure of the resulted GL. Figure 3 shows the tree view of the GL hierarchy (Frame 1) and the interactive flow-chart available for each of the created sub-plans (frame 2).
Figure 3: Interface for interactive exploration of hierarchical diagrams of clinical guidelines The final computer interpretable nursing specific heart failure GL included 167 recommendations, which is about 9% more than the original expert consensus GL (that included 153 recommendations). We had more recommendations in the final GL product mostly because some of the original recommendations were further specified into several formal parts. For example, free text GL recommendation that stated “Document type and dose of naturoceutical products. Naturoceutical (e.g. ephedra, ephedrine, or its metabolites) use is not recommended for
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symptomatic HF patients.” was divided into two recommendations on documentation and use of the naturoceutical products. The clinical editor invested an additional 10 hours to complete the GL. The final set of 167 recommendations consisted of: 102 (61%) drug related recommendations; 43 (26%) recommendations on observations (including lab results, clinical tests, physiological measures etc.); 20 (12%) education suggestions for heart failure patients; and 2 (1%) recommendations for procedures. Evaluation Most of the GL specification work was conducted through an active collaboration between the knowledge engineer and clinical editor. At each phase of the process, the quality of knowledge specification was also discussed between the team members. Clinical experts were consulted several times throughout the specification process. When the formalization process was completed, one of the experts evaluated the quality and consistency of knowledge representation in the final product. Qualitative criteria for guideline evaluation included: 1. the level of similarity between free text and computer interpretable guidelines and; 2. clarity and appropriateness of guideline language in the computer interpretable format. According to the expert’s opinion, the GL knowledge was adequately presented in the final product. Discussion The goal of this study was to examine the feasibility of nursing GL specification with a GL modeling language (Asbru), using a graphical framework for specification of clinical knowledge (Gesher). One of the study prerequisites was having a GL that is appropriate for the population of interest and setting. Unfortunately, no GL exists specifically for homecare nurses treating almost 2.8 million heart failure patients each year. To overcome this challenge, we combined a team of clinical experts that reviewed the potential guideline sources and decided whether these are applicable for homecare nurses. Because homecare nurses’ clinical responsibilities differ between states and settings, one of the major challenges of the GL review process was to generate a generalizible set of recommendations. Our goal was to generate a widely applicable set of heart failure nursing specific guidelines that should be reviewed and customized by local nurses in each particular state and home health agency. After an initial Delphi round, experts’ opinions were split on 15% of the recommendations mostly because of unclear boundaries of nursing scope of practice. To resolve that, we asked our experts to select recommendations based on an “ideal” world situation in which nurses are allowed to practice to the full scope of their practice. We also underlined that our GL are intended to be used in an electronic format as a form of clinical decision support, ideally at the point of care. Although currently there are no legislative acts requiring nurses in home health to use clinical decision support systems (the Meaningful Use regulation focuses mostly on inpatient or primary care settings), it is expected that in the near future those tools will become a necessity, if not a requirement. Our approach to GL review resulted in a comprehensive, hierarchical, nursing specific heart failure GL that included 153 recommendations on clinical assessments, treatments and pharmacological aspects of care. This was the longest step of our process that took approximately 6 months. The next phase focused on preparing GL for integration into a computer interpretable format. At this step, we partially formalized the logic embedded in each of the GL recommendations and defined GL concepts. This preparation phase was also time consuming because we needed to consult with our clinical experts on the explicit definitions for each of the GL concepts. With the directions from experts, our team was able to explicitly define and logically represent the vast majority of GL concepts and recommendations. This step lasted approximately 2 months. During the next phases, we used Gesher to further specify the resulting heart failure GL. The clinical editor that conducted most of the GL specification, was a PhD student and had some experience in the field of health informatics, however, he never used Gesher before this project. The three online introductory meetings provided the clinical editor with a sufficient set of tools to use the software. Later on in the process, we conducted weekly meetings to overview the progress and quality of the knowledge specification process. We did not encounter any major issues during the creation of semi-formal GL representation. Using Gesher’s tools, such as the “Hierarchical Plan Builder”, enabled us to gradually specify the heart failure nursing GL. Also, Gesher offered intuitive and relatively easy to use tools to generate Asbru based, hierarchical GL. We constructed a rich set of ordering of GL sub-plans some of which were sequential, parallel, or periodical (as defined in Asbru). The hierarchical structure of the GL was further enhanced by the ability to specify eligibility criteria (or filter condition
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in Asbru) for each of the recommendations. For example, some recommendations were intended only for elderly patients or patients with severe heart failure (New York Heart Association class III or IV). The “Knowledge Map” interface allowed us, in a reasonable time, to represent, examine and confirm all the GL concepts. The relatively easy specification of the guideline concepts can be also explained by the physiologically oriented focus of the heart failure GL. Most of the recommendations were based on physiological phenomena such as blood pressure or cardiac arrhythmia. Future work is needed to fully understand the applicability of similar tools to enable specification of more holistically oriented nursing concepts, such as grief or patient’s motivation for self care. Overall, the project of CIG creation lasted about 11 months with the longest step being the experts’ consensus. The Gesher software is available for a licensed use (for more details contact [email protected]). The quality of knowledge integration was adequate, according to the expert’s opinion. The experienced knowledge engineer was also satisfied with the final product. However, because of the exploratory nature of our project, no further attempts to evaluate CIG quality were implemented. Similar projects might require a more thorough expert evaluation, preferably by an interdisciplinary group of experts including academics, practicing clinicians and knowledge engineers). Another possible scenario is evaluation of the project through application of the CIG to a series of comprehensive case studies that include wide range of practical clinical cases. In the upcoming final step of our project, we will encode our semi-formal CIG to a fully formal, standardized format using one of the 13 American Nurses Association recognized standardized nursing terminologies. Our terminology of choice is the Omaha System, a well established terminology developed through more than three decades of research and refinement [26]. This terminology is particularly applicable for the needs of our project as it focuses on care provided in community-based settings. The Omaha System is currently used in many electronic health record products internationally and in the U.S. to provide a standardized interface representation and reference mapping for nursing care. It is also integrated into the U.S. National Library of Medicine Unified Medical Language System (UMLS) and the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) [26]. This final step is necessary to enable standardized representation of our heart failure CIG at the point of care for nurses. We are also considering several homecare venues for implementation of our heart failure CIG as a clinical decision support tool for nurses. Limitations Our project is not without limitations. First, the original guideline created as a result of experts consensus, might be biased by experts’ opinions. Also, the resulted CIG was physiologically oriented (i.e. focused on biological measures such as blood pressure and sodium blood levels) while only a few more abstract and holistic concepts, such as grief, were integrated into the final CIG. More research is needed to understand the applicability of the current tools to integrate these concepts into CIG. Lastly, we examined the feasibility of nursing CIG creation using only one modeling language (Asbru) and one graphical application for GL knowledge specification (Gesher), thus the generalizibilty of our findings might be limited. Conclusions This pioneering study demonstrates the feasibility of integrating nursing specific guidelines into a computer interpretable format using readily available tools. First, our team was able to achieve experts’ consensus on the relevant heart failure homecare nursing GL. Then we generated a complex, hierarchical and time-oriented computer interpretable GL using Asbru as a GL modeling language and Gesher as a graphical tool for gradual knowledge specification. Both of the tools proved sufficient and enabled us to create an adequate GL knowledge representation. Future work should explore the applicability of the tools for nursing specialties other than heart failure and develop methods for comprehensive quality evaluation of the resulting computer interpretable guidelines. Acknowledgments The study received financial support through the Faculty Senate Research Committee Awards, Office of Nursing Research, University of Pennsylvania, School of Nursing. We would like to gratefully acknowledge the experts who guided the selection of the heart failure guidelines: 1) Dr. Kathy Bowles RN, PhD, FAAN, FACMI; 2) Dr. Susan Mcgeary RN PhD; 3) Dr. Barbara Riegel RN, DNSc, FAAN, FAHA; 4) Ms. Katie Winkler RN, CHFN. We would also like to acknowledge that pre-doctoral funding support has been provided for Ruth Masterson Creber through NIH/NINR (1F31NR014086-01) as well as the National Hartford Centers of Geriatric Nursing Excellence Patricia G. Archbold Scholarship program.
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1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.
18. 19. 20. 21. 22. 23. 24. 25.
References Straus SE, Richardson WS, Glasziou P, Haynes. B. Evidence-based medicine: how to practice and teach EBM. 3rd ed. Edinburgh: Churchill Livingstone; Oxford press. 2006. Institute of Medicine. Knowing what works in health care: a roadmap for the nation. IOM, Washington, DC. 2008. Institute of Medicine. Crossing the quality chasm: a new health system for the 21st century. IOM, Washington, DC. 2001. Bright TJ, Wong A, Dhurjati R, Bristow E, Bastian L, Coeytaux RR, et al. Effect of clinical decision-support systems: a systematic review. Ann Intern Med 2012;157:29-43. Lobach D, Sanders GD, Bright TJ, Wong A, Dhurjati R, Bristow E, et al. Enabling health care decisionmaking through clinical decision support and knowledge management. Evid Rep Technol Assess 2012;(203):1-784. Office of the National Coordinator for HIT. Meaningful Use. 2012; Available at: http://www.healthit.gov/policy-researchers-implementers/meaningful-use. Accessed 01/01, 2013. Center for Medicare & Medicaid Services. Meaningful Use: stage 2 overview. CMS, Washington, DC.2012. Peleg M, Boxwala AA, Tu S, Zeng Q, Ogunyemi O, Wang D, et al. The InterMed approach to sharable computer-interpretable guidelines: a review. J Am Med Inform Assoc 2004;11:1-10. Institute of Medicine. The Patient-Centered Medical Home: A Path Toward Health Equity? IOM, Washington, DC. 2012. Dunlay SM, Shah ND, Shi Q, Morlan B, VanHouten H, Long KH, et al. Lifetime costs of medical care after heart failure diagnosis. Circ Cardiovasc Qual Outcomes 2011;4:68-75. Norton C, Georgiopoulou VV, Kalogeropoulos AP, Butler J. Epidemiology and cost of advanced heart failure. Prog Cardiovasc Dis 2011;54:78-85. The National Association for Home Care & Hospice. Basic statistics about homecare. 2010;202.547.7424. Albert NM, Sumodi V, Wilkinson S, Vopat L, Willis C, Bittel B, et al. Heart failure (HF): are nurses providing patients with optimal education of self-care principles? J Heart Lung Transplant. 2001;20:244. Delaney C, Apostolidis B, Lachapelle L, Fortinsky R. Home care nurses' knowledge of evidence-based education topics for management of heart failure. Heart Lung 2011;40:285-292. Albert NM. Evidence-based nursing care for patients with heart failure. AACN Adv.Crit.Care. 2006;17:170-83; quiz 184-5. Washburn SC, Hornberger CA. Nurse educator guidelines for the management of heart failure. J.Contin.Educ.Nurs. 2008;39:263-267. Hunt SA, Abraham WT, Chin MH, Feldman AM, Francis GS, Ganiats TG, et al. 2009 focused update incorporated into the ACC/AHA 2005 guidelines for the diagnosis and management of heart failure in adults: A report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines: Developed in collaboration with the International Society for Heart and Lung Transplantation. Circulation 2009;119:e391-e479. Heart Failure Society of America, Lindenfeld J, Albert NM, Boehmer JP, Collins SP, Ezekowitz JA, et al. HFSA 2010 comprehensive heart failure practice guideline. J Card Fail 2010;16:e1-194. Musen MA, Fagan LM, Combs DM, Shortliffe EH. Use of a domain model to drive an interactive knowledgeediting tool. International Journal of Human Computer Studies 1999;51:479-495. Shalom E, Shahar Y, Taieb-Maimon M, Bar G, Yarkoni A, Young O, et al. A quantitative assessment of a methodology for collaborative specification and evaluation of clinical guidelines. J Biomed Inform 2008;41:889-903. Shahar Y, Young O, Shalom E, Galperin M, Mayaffit A, Moskovitch R, et al. A framework for a distributed, hybrid, multiple-ontology clinical-guideline library, and automated guideline-support tools. J Biomed Inform 2004;37:325-344. Hatsek A, Shahar Y, Taieb-Maimon M, Shalom E, Klimov D, Lunenfeld E. A scalable architecture for incremental specification and maintenance of procedural and declarative clinical decision-support knowledge. Open Med Inform J 2010;4:255-277. Shahar Y, Miksch S, Johnson P. The Asgaard project: a task-specific framework for the application and critiquing of time-oriented clinical guidelines. Artif Intell Med 1998;14:29-51. Radhakrishnan K, Topaz M, Masterson Creber R. Adopting heart failure guidelines for nursing care in home health settings: challenges and solutions. Journal of Cardiovascular Nursing [In review] 2013. American Heart Association. Hypertension. 2013; Available at: http://www.heart.org/HEARTORG/Conditions/HighBloodPressure/High-BloodPressure_UCM_002020_SubHomePage.jsp. Accessed 03/03, 2013.
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26. Martin KS. The Omaha System: A Key to Practice, Documentation, and Information Management. St Louis, MO: Elsevier Saunders; 2005.
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