- Email: [email protected]
Integrate personalized medicine into clinical practice to improve patient safety
Disponible en ligne sur
www.sciencedirect.com IRBM 34 (2013) 53–55
Digital technologies for healthcare
Integrate personalized medicine into clinical practice to improve patient safety N. Douali ∗ , M.-C. Jaulent INSERM UMR S 872, Eq 20, Medicine Faculty, Pierre and Marie Curie University, 75006 Paris, France Received 9 November 2012; received in revised form 20 December 2012; accepted 2 January 2013 Available online 5 February 2013
Abstract Medical practice is based on the experience of practitioners and on learned medical knowledge. This knowledge is based on studies of patient’s population. Modern medicine is facing a variety of clinical forms and also variable patients’ responses to treatment. Pharmacogenomics has brought insights to this variability and has led to the development of personalized medicine. The adoption of personalized medicine is slowed down by a number of technical and methodology barriers. The concept of personalized medicine should not be only limited to genetics but must reuse all patient information to get the most suitable patient profile. In this paper we present a methodology for the integration of personalized medicine into clinical practice. © 2013 Elsevier Masson SAS. All rights reserved.
1. Introduction Historically, medical practice was based on the experience of practitioners and on learned medical knowledge. This knowledge is based on studies of patient’s population. Modern medicine is facing a variety of clinical forms and also variable patients’ responses to treatment. The notion of “every patient is an individual case” requires a new clinical methodology and practitioners should take into account personalized clinical parameters [1]. Since the mapping of the human genome in 2003, the pace of discovery, product development, and clinical adoption of what is called “personalized medicine” has accelerated [2]. The explosion of genomic sequences has provided unprecedented opportunities for the global analysis of complex diseases processes. The new challenge is to translate the success obtained in genomic research into clinical practice and to guide physicians in diagnosis, predictive approaches and therapy. We can use genetic information and patients profiling in a way that we never have before. A profile of a patient’s genetic variation can guide the selection of drugs or treatment protocols that minimizes harmful side effects or ensures a more successful outcome [3]. ∗
Corresponding author. E-mail address: [email protected] (N. Douali).
1959-0318/$ – see front matter © 2013 Elsevier Masson SAS. All rights reserved. http://dx.doi.org/10.1016/j.irbm.2013.01.001
Electronic health records (EHRs) play an important role in improving the patients care, including the patient’s history and all examinations performed for the patient. Among the most important features offered by EHR are the exchange and interoperability of information about the patient’s health [4]. New tools for performing certain functions of providing health care, such as prescription drugs, medical support in the delivery of evidencebased care [5], including the incorporation of the evolution of practice guidelines in point of care information accessible formats. The advantages of EHR has pushed the development of several tools and added new features, such as prescription drugs, medical decision support or even incorporate clinical practice guidelines (CPG). The advantage of using a clinical decision support system (CDSS) is proven [6], to improve them, it is necessary to integrate new knowledge and data for patients profiling. The use of heterogeneous data and knowledge is a big methodological problem.
1.1. Personalized medicine Since the mapping of human genome in 2003, the discovery of new gene and adoption of genomic medicine, are accelerated. Personalized medicine is considered as an extension of traditional medicine to better understand and treat diseases, but with greater precision [2].
54
N. Douali, M.-C. Jaulent / IRBM 34 (2013) 53–55
The President’s Council of Advisors on Science and Technology said about personalized medicine “refers to the tailoring of medical treatment to the individual characteristics of each patient. It does not literally mean the creation of drugs or medical devices that are unique to a patient, but rather the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or their response to a specific treatment. Preventive or therapeutic interventions can then be concentrated on those who will benefit, sparing expenses and side effects for those who will not” [7]. Personalized medicine should not be limited to a genetic profiling but to use all patient’s available information to most appropriate profiles. The scientific evidence can provide greater safety for patients. 1.2. Clinical decision support system Clinical decision support systems (CDSS) have been developed to improve patient safety and care processes [8,9]. Several studies and reviews have shown these tools to be effective [10–13] in the areas of diagnosis and treatment. A decade ago, the crossing the quality chasm report highlighted the importance of using CDSS to reduce the frequency of medical errors, to ensure that best practice is followed and to reduce care costs [14]. The development of CDSS involves two knowledge-management tasks: consideration of possibilities for the integration of the CDSS into the care system workflow and knowledge-management for correct decision-making. In this paper we present a new methodology to integrate personalized medicine in clinical practice using CDSS.
Fig. 1. Diagram representing the CBFCM method.
scientific literature with high level of evidence especially in pharmacogenomics. 3. Results
2. Methods 2.1. Case based fuzzy cognitive maps Case based fuzzy cognitive map (CBFCM) is a hybrid decision-making computing technique [15]. CBFCM is represented as nodes (concepts) that illustrate the different aspects of the system’s behavior. Concepts may represent variables, states, events, inputs and outputs, which are essential to model a system [16]. The value of each node (concept) is represented as fuzzy set. Fig. 1 is a graphical representation of CBFCM method. A patient N is described by a set of clinical parameters. These clinical parameters can be clinical signs, age, gender, genetic profile or biological results.
The proposed methodology was validated in cardiovascular diseases. For each patient, signs/symptoms/biological parameters and genetic profile were taken into account by the system, for the proposal of a diagnosis. Using a patient database of 86 patients (cardiovascular diseases), we had: 91% (79/86 patients) of diagnosis, proposed by the system, were in fully agreement with the confirmed diagnosis. We present, below, an example of a RDF/N3 file containing results generated by the inference engine. In this example, patient 1 is most likely to have a coronary heart disease:
2.2. Information model The semantic web framework based on CBFCM integrates heterogeneous data: clinical data (signs, symptoms. . .), biological data (lab test. . .), and imaging and omics data. To create a patient clinical profile we need to use all these types of data [17]. 2.3. Knowledge model CPG pertain evidence-based knowledge and their routine use improves the quality of care [18] (Fig. 2). In our model we integrate a medical knowledge from CPG and also from the
Fig. 2. CPG fuzzy formalization method.
N. Douali, M.-C. Jaulent / IRBM 34 (2013) 53–55
(: patient 1 SnomedCt: heart failure) fl:pi 0.5. (:patient 1 SnomedCt: cardiac dysrhythmias) fl:pi 0.124428856919004. [2]
(: patient 1 SnomedCt: coronary heart disease) fl:pi 0.9996644247991672. (: patient 1 SnomedCt: 0.03452451818391727.
cardiomyopathy)
[3] [4]
fl:pi [5]
(: patient 1 SnomedCt: valvular heart disease) fl:pi 0.1968036438932337.
[6]
(: patient 1 SnomedCt: hypertensive heart disease) fl:pi 0. [7]
0.6823910879843522. (: patient 1 SnomedCt: 0.05239040191009479.
endocarditis)
fl:pi [8]
4. Conclusions [9]
The CBFCM approach allowed us to integrate heterogeneous clinical data to perform a personalized patient profile. This method can identify causal relationships between clinical, biological, genetic concepts and decision concept (diagnosis or treatment). The use of CBFCM enables to incorporate several sources of knowledge (several CPGs, knowledge from literature), which is of great advantage since all knowledge is rarely embedded in a unique CPG. Indeed, knowledge of a medical field is usually broad, complex and closely related to other areas so that several knowledge sources are needed to cover and modelled the medical domain in question. We have implemented the knowledge bases, rules and databases in the same technical environment without compatibility constraints, using semantic web tools. The success rate of 91% shows the functionality of the model and its future usefulness in clinical practice. These results are partly due to genomic profiling but also the use of different data to specify a diagnostic and therapeutic support. The conducted study allowed us to test cognitive approaches reasoning to enable personalized medicine. The advantage of this approach is to enable the sharing and reuse of knowledge and simplify maintenance.
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
References [1] Herment A, Kachenoura N, Lefort M, et al. Automated segmentation of the aorta from phase contrast MR images: validation against expert tracing
[18]
55
in healthy volunteers and in patients with a dilated aorta. J Magn Reson Imaging 2010;31:881–8. Margaret A, Hamburg MD, Francis S, Collins MD. The path to personalized medicine. N Engl J Med 2010;363:301–4. Landau R, Bollag LA, Kraft JC. Pharmacogenetics and anaesthesia: the value of genetic profiling. J Anaesth 2012. Downing GJ, Boyle SN, Brinner KM, Osheroff JA. Information management to enable personalized medicine: stakeholder roles in building clinical decision support. BMC Med Inform Decis Mak 2009. Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L, et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003;10(6):523–30. Bassa A, Miguel dV, Cobos A, Torremadé E, Bergo˜nón S, Crespo C, et al. Impact of a clinical decision support system on the management of patients with hypercholesterolemia in the primary healthcare setting. Dis Manag Health Outcomes 2005;13(1):65–72. President’s Council of Advisors on Science and Technology. Priorities for personalized medicine. 2008. Sep, http://www.whitehouse.gov/ files/documents/ostp/PCAST/pcast report v2.pdf. Accessed November 19, 2012. Sittig DF, Wright A, Osheroff JA, Middleton B, Teich JM, Ash JS, et al. Grand challenges in clinical decision support. J Biomed Inform 2008;41(2):387–92. Balas EA, Weingarten S, Garb CT, Blumenthal D, Boren SA, Brown GD. Improving preventive care by prompting physicians. Arch Intern Med 2000;160(3):301–8. Hunt DL, Haynes RB, Hanna SE, Smith K. Effects of computerbased clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA 1998;280(15): 1339–46. 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. BMJ 2005;330(7494): 765. Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005;293(10):1223–38. Kucher N, Koo S, Quiroz R, Cooper JM, Paterno MD, Soukonnikov B, et al. Electronic alerts to prevent venous thromboembolism among hospitalized patients. N Engl J Med 2005;352(10):969–77. Institute of Medicine. Crossing the quality chasm: a new health system for the 21st century. National Academy Press; 2001. Douali N, Papageorgiou E, De Roo J, Jaulent MC. Case based fuzzy cognitive maps: new method for medical reasoning: comparison study between CBFCM/FCM. In: IEEE International Conference on Fuzzy systems (FUZZ). Taipei. 2011, 844–850. Douali N, De Roo J, Jaulent MC. Clinical diagnosis support system based on case based fuzzy cognitive maps and semantic web. Stud Health Technol Inform 2012;180:295–9. Douali N, De Roo J, Jaulent MC. Decision support system based semantic web for personalized patient care. Stud Health Technol Inform 2012;180:1203–5. Garfield FB, Garfield JM. Clinical judgement and clinical practice guidelines. Int J Technol Assess Health Care 2000;16:1050–60.
www.sciencedirect.com IRBM 34 (2013) 53–55
Digital technologies for healthcare
Integrate personalized medicine into clinical practice to improve patient safety N. Douali ∗ , M.-C. Jaulent INSERM UMR S 872, Eq 20, Medicine Faculty, Pierre and Marie Curie University, 75006 Paris, France Received 9 November 2012; received in revised form 20 December 2012; accepted 2 January 2013 Available online 5 February 2013
Abstract Medical practice is based on the experience of practitioners and on learned medical knowledge. This knowledge is based on studies of patient’s population. Modern medicine is facing a variety of clinical forms and also variable patients’ responses to treatment. Pharmacogenomics has brought insights to this variability and has led to the development of personalized medicine. The adoption of personalized medicine is slowed down by a number of technical and methodology barriers. The concept of personalized medicine should not be only limited to genetics but must reuse all patient information to get the most suitable patient profile. In this paper we present a methodology for the integration of personalized medicine into clinical practice. © 2013 Elsevier Masson SAS. All rights reserved.
1. Introduction Historically, medical practice was based on the experience of practitioners and on learned medical knowledge. This knowledge is based on studies of patient’s population. Modern medicine is facing a variety of clinical forms and also variable patients’ responses to treatment. The notion of “every patient is an individual case” requires a new clinical methodology and practitioners should take into account personalized clinical parameters [1]. Since the mapping of the human genome in 2003, the pace of discovery, product development, and clinical adoption of what is called “personalized medicine” has accelerated [2]. The explosion of genomic sequences has provided unprecedented opportunities for the global analysis of complex diseases processes. The new challenge is to translate the success obtained in genomic research into clinical practice and to guide physicians in diagnosis, predictive approaches and therapy. We can use genetic information and patients profiling in a way that we never have before. A profile of a patient’s genetic variation can guide the selection of drugs or treatment protocols that minimizes harmful side effects or ensures a more successful outcome [3]. ∗
Corresponding author. E-mail address: [email protected] (N. Douali).
1959-0318/$ – see front matter © 2013 Elsevier Masson SAS. All rights reserved. http://dx.doi.org/10.1016/j.irbm.2013.01.001
Electronic health records (EHRs) play an important role in improving the patients care, including the patient’s history and all examinations performed for the patient. Among the most important features offered by EHR are the exchange and interoperability of information about the patient’s health [4]. New tools for performing certain functions of providing health care, such as prescription drugs, medical support in the delivery of evidencebased care [5], including the incorporation of the evolution of practice guidelines in point of care information accessible formats. The advantages of EHR has pushed the development of several tools and added new features, such as prescription drugs, medical decision support or even incorporate clinical practice guidelines (CPG). The advantage of using a clinical decision support system (CDSS) is proven [6], to improve them, it is necessary to integrate new knowledge and data for patients profiling. The use of heterogeneous data and knowledge is a big methodological problem.
1.1. Personalized medicine Since the mapping of human genome in 2003, the discovery of new gene and adoption of genomic medicine, are accelerated. Personalized medicine is considered as an extension of traditional medicine to better understand and treat diseases, but with greater precision [2].
54
N. Douali, M.-C. Jaulent / IRBM 34 (2013) 53–55
The President’s Council of Advisors on Science and Technology said about personalized medicine “refers to the tailoring of medical treatment to the individual characteristics of each patient. It does not literally mean the creation of drugs or medical devices that are unique to a patient, but rather the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or their response to a specific treatment. Preventive or therapeutic interventions can then be concentrated on those who will benefit, sparing expenses and side effects for those who will not” [7]. Personalized medicine should not be limited to a genetic profiling but to use all patient’s available information to most appropriate profiles. The scientific evidence can provide greater safety for patients. 1.2. Clinical decision support system Clinical decision support systems (CDSS) have been developed to improve patient safety and care processes [8,9]. Several studies and reviews have shown these tools to be effective [10–13] in the areas of diagnosis and treatment. A decade ago, the crossing the quality chasm report highlighted the importance of using CDSS to reduce the frequency of medical errors, to ensure that best practice is followed and to reduce care costs [14]. The development of CDSS involves two knowledge-management tasks: consideration of possibilities for the integration of the CDSS into the care system workflow and knowledge-management for correct decision-making. In this paper we present a new methodology to integrate personalized medicine in clinical practice using CDSS.
Fig. 1. Diagram representing the CBFCM method.
scientific literature with high level of evidence especially in pharmacogenomics. 3. Results
2. Methods 2.1. Case based fuzzy cognitive maps Case based fuzzy cognitive map (CBFCM) is a hybrid decision-making computing technique [15]. CBFCM is represented as nodes (concepts) that illustrate the different aspects of the system’s behavior. Concepts may represent variables, states, events, inputs and outputs, which are essential to model a system [16]. The value of each node (concept) is represented as fuzzy set. Fig. 1 is a graphical representation of CBFCM method. A patient N is described by a set of clinical parameters. These clinical parameters can be clinical signs, age, gender, genetic profile or biological results.
The proposed methodology was validated in cardiovascular diseases. For each patient, signs/symptoms/biological parameters and genetic profile were taken into account by the system, for the proposal of a diagnosis. Using a patient database of 86 patients (cardiovascular diseases), we had: 91% (79/86 patients) of diagnosis, proposed by the system, were in fully agreement with the confirmed diagnosis. We present, below, an example of a RDF/N3 file containing results generated by the inference engine. In this example, patient 1 is most likely to have a coronary heart disease:
2.2. Information model The semantic web framework based on CBFCM integrates heterogeneous data: clinical data (signs, symptoms. . .), biological data (lab test. . .), and imaging and omics data. To create a patient clinical profile we need to use all these types of data [17]. 2.3. Knowledge model CPG pertain evidence-based knowledge and their routine use improves the quality of care [18] (Fig. 2). In our model we integrate a medical knowledge from CPG and also from the
Fig. 2. CPG fuzzy formalization method.
N. Douali, M.-C. Jaulent / IRBM 34 (2013) 53–55
(: patient 1 SnomedCt: heart failure) fl:pi 0.5. (:patient 1 SnomedCt: cardiac dysrhythmias) fl:pi 0.124428856919004. [2]
(: patient 1 SnomedCt: coronary heart disease) fl:pi 0.9996644247991672. (: patient 1 SnomedCt: 0.03452451818391727.
cardiomyopathy)
[3] [4]
fl:pi [5]
(: patient 1 SnomedCt: valvular heart disease) fl:pi 0.1968036438932337.
[6]
(: patient 1 SnomedCt: hypertensive heart disease) fl:pi 0. [7]
0.6823910879843522. (: patient 1 SnomedCt: 0.05239040191009479.
endocarditis)
fl:pi [8]
4. Conclusions [9]
The CBFCM approach allowed us to integrate heterogeneous clinical data to perform a personalized patient profile. This method can identify causal relationships between clinical, biological, genetic concepts and decision concept (diagnosis or treatment). The use of CBFCM enables to incorporate several sources of knowledge (several CPGs, knowledge from literature), which is of great advantage since all knowledge is rarely embedded in a unique CPG. Indeed, knowledge of a medical field is usually broad, complex and closely related to other areas so that several knowledge sources are needed to cover and modelled the medical domain in question. We have implemented the knowledge bases, rules and databases in the same technical environment without compatibility constraints, using semantic web tools. The success rate of 91% shows the functionality of the model and its future usefulness in clinical practice. These results are partly due to genomic profiling but also the use of different data to specify a diagnostic and therapeutic support. The conducted study allowed us to test cognitive approaches reasoning to enable personalized medicine. The advantage of this approach is to enable the sharing and reuse of knowledge and simplify maintenance.
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
References [1] Herment A, Kachenoura N, Lefort M, et al. Automated segmentation of the aorta from phase contrast MR images: validation against expert tracing
[18]
55
in healthy volunteers and in patients with a dilated aorta. J Magn Reson Imaging 2010;31:881–8. Margaret A, Hamburg MD, Francis S, Collins MD. The path to personalized medicine. N Engl J Med 2010;363:301–4. Landau R, Bollag LA, Kraft JC. Pharmacogenetics and anaesthesia: the value of genetic profiling. J Anaesth 2012. Downing GJ, Boyle SN, Brinner KM, Osheroff JA. Information management to enable personalized medicine: stakeholder roles in building clinical decision support. BMC Med Inform Decis Mak 2009. Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L, et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003;10(6):523–30. Bassa A, Miguel dV, Cobos A, Torremadé E, Bergo˜nón S, Crespo C, et al. Impact of a clinical decision support system on the management of patients with hypercholesterolemia in the primary healthcare setting. Dis Manag Health Outcomes 2005;13(1):65–72. President’s Council of Advisors on Science and Technology. Priorities for personalized medicine. 2008. Sep, http://www.whitehouse.gov/ files/documents/ostp/PCAST/pcast report v2.pdf. Accessed November 19, 2012. Sittig DF, Wright A, Osheroff JA, Middleton B, Teich JM, Ash JS, et al. Grand challenges in clinical decision support. J Biomed Inform 2008;41(2):387–92. Balas EA, Weingarten S, Garb CT, Blumenthal D, Boren SA, Brown GD. Improving preventive care by prompting physicians. Arch Intern Med 2000;160(3):301–8. Hunt DL, Haynes RB, Hanna SE, Smith K. Effects of computerbased clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA 1998;280(15): 1339–46. 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. BMJ 2005;330(7494): 765. Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005;293(10):1223–38. Kucher N, Koo S, Quiroz R, Cooper JM, Paterno MD, Soukonnikov B, et al. Electronic alerts to prevent venous thromboembolism among hospitalized patients. N Engl J Med 2005;352(10):969–77. Institute of Medicine. Crossing the quality chasm: a new health system for the 21st century. National Academy Press; 2001. Douali N, Papageorgiou E, De Roo J, Jaulent MC. Case based fuzzy cognitive maps: new method for medical reasoning: comparison study between CBFCM/FCM. In: IEEE International Conference on Fuzzy systems (FUZZ). Taipei. 2011, 844–850. Douali N, De Roo J, Jaulent MC. Clinical diagnosis support system based on case based fuzzy cognitive maps and semantic web. Stud Health Technol Inform 2012;180:295–9. Douali N, De Roo J, Jaulent MC. Decision support system based semantic web for personalized patient care. Stud Health Technol Inform 2012;180:1203–5. Garfield FB, Garfield JM. Clinical judgement and clinical practice guidelines. Int J Technol Assess Health Care 2000;16:1050–60.