Health Policy
Original Contribution
Knowledge Bases, Clinical Decision Support Systems, and Rapid Learning in Oncology By Peter Paul Yu, MD Palo Alto Medical Foundation, Palo Alto, CA
Abstract One of the most important benefits of health information technology is to assist the cognitive process of the human mind in the face of vast amounts of health data, limited time for decision making, and the complexity of the patient with cancer. Clinical decision support tools are frequently cited as a technologic solution to this problem, but to date useful clinical decision support
systems (CDSS) have been limited in utility and implementation. This article describes three unique sources of health data that underlie fundamentally different types of knowledge bases which feed into CDSS. CDSS themselves comprise a variety of models which are discussed. The relationship of knowledge bases and CDSS to rapid learning health systems design is critical as CDSS are essential drivers of rapid learning in clinical care.
Introduction
Clinical Trials
Rapid learning health systems (RLS) are health care systems that align culture and technology to aggregate individual patient data across populations that share common characteristics defining a health state in order to generate knowledge and learning. Although the Institute of Medicine has pointed to the RLS model as critical for accelerating improvements in health outcomes, no such systems have been designed or implemented in health care to date. It is important to realize that health care systems need not be limited to hospital-based systems or to large medical groups, but could be composed of independent entities that choose to share data to support common goals, including patients and professional medical societies. Learning results from the transformation of such data into knowledge and the subsequent application of that knowledge to patient care. Thus there is a sequential process of transforming data into knowledge and knowledge into learning. Data sources are varied and include translational science data, clinical trials data, patient sourced data, and health systems data on operational processes and patient outcomes. When data repositories are analyzed and correlations observed and models generated to make sense of data, a knowledge base is established. The resulting knowledge base may then generate a clinical practice guideline or an algorithm. Knowledge bases are elastic, expanding and evolving as new data are acquired and models revisited.1 However, knowledge achieves clinical utility only when it becomes actionable to improve patient health. One can think of learning as the process of applying knowledge to improve patient outcomes, quality, and value of health care.
The first knowledge base derives from clinical trials that have been peer-reviewed and published in medical literature. Clinical trials produce data to either support or refute a clinical model expressed as a null hypothesis. If a sufficient number of clinical trials are available, a meta-analysis of pooled data across trials can be performed whereby the aggregation of data creates greater statistical power to observe clinical benefit. The individual trials whose data are combined in a meta-analysis differ in exact study design and execution, but the principle is established that the loss of methodological rigor of trial design is more than compensated for by the improved ability to detect signals of clinical utility. In other words, the need for defining homogenous study populations is driven by the constraints of study population size. With larger data sets the need for rigid control of patient variables diminishes. If sufficiently convincing, a US Food and Drug Administration drug indication or compendium listing can codify the knowledge base on a new therapeutic. Another accepted method to create knowledge bases from clinical trial data is systematic reviews of medical literature. Here, the aggregation does not occur at the level of clinical trial data, but rather at the level of the knowledge derived from each trial. The knowledge from each trial is subjectively weighted for the quality of the study and remaining gaps in knowledge are addressed through expert consensus opinion. The result is a knowledge base expressed as a clinical practice guideline issued by an authoritative body, typically a professional society. This model of distilling medical literature into clinical practice guidelines has been endorsed by Agency for Healthcare Research and Quality.2 However, there are significant limitations to this process such as the length of time required to build the knowledge base, lack of transparency about the degree to which a guideline is dependent on expert opinion, and the difficulty of refining the knowledge base over time as new literature accumulates. Nevertheless, both of these examples, meta-analysis
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Table 1. Knowledge Bases Clinical Trials
Systems Biology
Health Care Systems
Meta-analysis and systematic reviews
Cancer biology
Operational data
Rules-based CDSS
Probabilistic CDSS
CDSS developed in local context
Population based
Individual based
Health system based
Abbreviation: CDSS, clinical decision support systems.
and clinical practice guidelines, make it clear that data aggregation and synthesis are pivotal to learning.
Systems Biology Systems biology is a second data source whose rapid expansion is improving our ability to create and refine disease models to better understand the causation, classification, and potential therapeutic interventions of human disease. In oncology, systems biology denotes our understanding of the molecular causes of cancer and disruptions to regulatory control of cellular growth and cancer immunology. It is challenging to maintain systems biology– derived knowledge bases given the volume, velocity, and variety at which new molecular data accumulates. Each oncology patient generates terabytes of systems biology data composed of genomics, transcriptomics, proteomics, and metabolomics data, collectively referred to as panomics. Unlike medical literature, which is dependent on the timeframe of clinical trial design, trial activation, accrual completion, and data maturation, cancer systems biology follows the speed of panomic research. The Cancer Genome Atlas3 will accelerate cancer systems biology knowledge through sequencing of cancer genomes and selective analysis of associated post-transcriptional events. The Global Alliance for Genomics and Health4 will facilitate interoperable sharing of genomic and clinical data sets among researchers. The technology to support biobanks of human specimens and high-throughput molecular analysis is rapidly becoming affordable. The application of computational biology to create disease models based on molecular data holds great promise to accelerate targeted therapies. Models of carcinogenesis are being rapidly constructed from mappings of signal transduction, feedback, and regulatory pathways.5 Visualizing operative networks and discounting the background noise of gratuitous molecular aberrations resulting from an unstable cancer genome and defective DNA repair mechanisms will require iterative model building and complex analytic techniques through computational biology. Computational biology can accelerate the building of molecular models with those models subsequently refined by patient outcomes health data in response to molecularly targeted therapies.4,6,7,8 The potential for computational models to simulate clinical trials has been cited by the Institute of Medicine.9 It is important to note that the knowledge bases derived from clinical trials and systems biology are not mutually incompatible. Rather they can function in a dependent manner. A robust underlying molecular model adds to the confidence that the findings from a relatively small clinical trial are meaningful and may be sufficiently persuasive to make the conduct of larger Copyright © 2015 by American Society of Clinical Oncology
confirmatory phase III trial both unnecessary and indefensible, especially in orphan diseases. Another example of the potential interplay between clinical trials and systems biology is the identification of outlier patients with exceptionally strong tumor responses to treatment not seen in the remainder of the trial patient cohort. Such outlier responders would in the past have been ignored because the trial was designed to study the drug and not the patient. Instead, a focus on the patient to understand such exceptional responses could elucidate novel mechanisms of carcinogenesis and add to the systems biology knowledge base. Cancer registries can be utilized to build cancer system biology knowledge bases by collecting information on individual patient treatment responses or lack thereof from the off-label use of targeted therapies. The transition from single analyte biomarker tests to multiplex panel testing, next-generation sequencing or whole exome sequencing is raising the potential for wide-spread use of off-label targeted therapies and raises troubling questions over payment for such off-label use in the absence of a knowledge base to support their use. A mechanism to collect such patient experiences would help ensure that generalizable knowledge was identified and added to the knowledge base.10 N-of-1 observations such as these, whether documented through a clinical trial or through off-label use of a targeted therapy conducted under monitored novel patient access mechanism, can rapidly expand our understanding of cancer and our therapeutic options.
Health Care Systems Health care systems data is a rapidly expanding third type of knowledge base that is increasingly available in digital format. These are observational data sets. Sources of health care systems data are varied and include electronic health records (EHRs), patient-reported data, laboratory information systems and data repositories, administrative claims data, cancer registries, and postmarketing surveillance. These are diverse and rich data streams that directly facilitate improving the value of health care delivery through comparative effectiveness research, reduction in unwarranted variation in health care delivery, and quality improvement. A special attribute of health care systems data is that it reflects real-world patient experiences; these patients are not preselected by the eligibility criteria of clinical trials, which require homogenous populations with limited comorbidities in order to better pick up weak signals of efficacy. The value of clinical trial knowledge bases is tempered by the restrictions placed on the study population by trial eligibility requirements. Health care systems knowledge bases can provide urgently needed information on whether clinical trial results are replicable in general cancer populations and whether the clinical trial treatment regimen as published should be modified for clinical use in patients with comorbidities and multiple medications that may increase the risk for harm. Health systems operations knowledge bases are complementary to literature-based knowledge bases, again emphasizing that the three knowledge bases described are interdependent.
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Table 2. Types of CDSS
RLS The process of transitioning from data to knowledge bases and finally to learning can be greatly accelerated by use of health information technology (HIT). The volume and diversity of data and the necessity for data aggregation and analytics require that HIT be the backbone of rapid learning. The vision of transforming health care and health research through HIT has led to the RLS model where EHRs and other repositories of digital health information are made available for individualand population-based learning.11 When the primary focus is on improving the quality and value of health care interventions, RLS is particularly dependent on health care operations data that capture processes and outcomes of routine health care delivery. This rapid learning occurs on two levels. For the individual provider and patient, the learning results from accessing a knowledge base that informs the patient’s clinical situation through CDSS. With learning defined as the application of knowledge to patient care, CDSS is the technology that accelerates the dissemination of new knowledge into the routine care of patients with cancer. The second level of learning in RLS occurs at the health system level when aggregated and large patient health data sets drive generalizable knowledge by creating or adding to an evergrowing health systems operations knowledge base.
CDSS CDSS are HIT applications that relate individual patient health data to established knowledge bases and thereby assist in clinical decision making and health management (Figure 1). CDSS can utilize any of the three previously described knowledge bases, but to function properly the triggers and data inputs into CDSS must be in a machine readable format. Narrative texts do not provide usable data unless they contain extractable data captured in structured fields or tagged by metadata that renders them searchable, and this remains a formidable barrier with the EHRs in use today. CDSS do not make decisions regarding medical care, but they provide information that may be relevant to a patient medical problem or present therapeutic choices in the management of disease that enable shared decision making
Outcomes
Shared Decision Making
Clinical Decision Support Systems
Health Care Systems KB
Systems Biology KB
Patient Data
CPGs KB
Figure 1. Contribution of clinical decision support systems to patient outcomes. CPG, clinical practice guideline; KB, knowledge base.
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CDSS Type
CDSS Example
Information management
Info Button, Up ToDate
Situational awareness
Alerts, dashboards
Patient specific
Logic-based guidance on diagnostic and therapeutic choices
Abbreviation: CDSS, clinical decision support systems.
between patients and providers. CDSS are critical elements to building an RLS, since they are instruments to realize learning applied to patient care. Musen et al12 has described three categories of CDSS (Table 2). First, CDSS can provide access to medical literature or educational material related to a clinical question and thereby augments the knowledge of the health care provider or patient. The Info button within an EHR is a technical solution to provide contextually relevant disease information.13 A second type of CDSS helps to focus the clinician on specific health data, an example of which are drug-drug interactions alerts. A graphical user interface that presents patient outcomes data in a visual display, thereby promoting clinical insights, is another example. Prebuilt reports, dashboards, and oncology flow sheets improve the usability of EHRs and can be invaluable for visualizing patterns of care. In an era when clinicians are struggling with less available time to spend with patients and with ever more data to contend with, this type of CDSS is critical for improving situational awareness and reducing the risk of medical errors. The third type of CDSS uses artificial intelligence or computational medicine to provide guidance on diagnostic or treatment interventions based on patient-specific data. Such CDSS can be characterized as computer-based systems that integrate a medical knowledge base with an individual patient’s data through a CDSS engine that applies a prespecified logic. CDSS engines use a variety of logics and knowledge bases to drive their clinical decision-support function. Which logic is best depends on the type of knowledge base that underlies the CDSS in question. Rules-based CDSS rely on one or more conditional statements triggered by predefined data elements and result in clinical decision support in the form of the rulesgenerated guidance. Rules can be linked, either linearly or by branched logic trees to guide patient decision making. Oncologists are familiar with National Comprehensive Cancer Network guidelines that provide a sequence of steps displayed as flow diagrams. American Society of Clinical Oncology (ASCO) guidelines are textually based and tolerant of greater ambiguity reflecting clinical reality but can still be distilled into a rulesbased format.14 ASCO’s RLS model, CancerLinQ has prototyped the conversion of five ASCO guidelines into machineready format.15 CDSS based on medical literature– derived knowledge bases are well suited for rules-based design. Rules-based design does not easily adapt to conditions where the underlying triggers are multiple and the clinical context multidimensional— conditions frequently encountered in oncology. Under these conditions, probabilistic design is more flexible, allowing for selective weighing of clinical factors and
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rapid model development.12 Here the probabilities of clinical events are determined and predictive algorithms constructed to predict likely or less likely outcomes resulting from clinical decision making. This process more closely approximates human judgment.16 CDSS based on cancer biology and health systems knowledge bases require flexibility in order to recognize and react to new knowledge. Inferential CDSS draws its force from specialized statistical analytic methods used in artificial intelligence including Bayesian-based logic, artificial neural networks, and support vector machines.17,18,19,20 Finally CDSS derived from health systems knowledge bases will inherently reflect the blend of culture, experiences, and resources unique to the local environment.
Systems Biology–Based Knowledge Bases The Global Alliance for Genomics and Health is an international effort to create a digital system for sharing of laboratorybased research, translational and clinical data. CancerLinq will be a major contributor of clinical data to such collaborative efforts, data that have been prepared through computational analysis of clinically meaningful end points. CDSS will assist oncologists with the appropriate selection of diagnostic and predictive molecular tests and CDSS will use those results to trigger suggestions of possible actionable interventions. Such interventions will support routine cancer care as well as clinical trials or registry studies based on systems biology– based models of cancer.
CancerLinQ CancerLinQ seeks to integrate all three knowledge bases; medical literature based, systems biology based, and health care systems based. By doing so in the context of a patient-centered care through CDSS, Cancer LinQ will achieve more rapid dissemination of knowledge into clinical practice. Concomitantly it will generate knowledge about improving the quality and cost effectiveness of patient care and generate new avenues of inquiry through observational N-of-1 experience. More than a decade ago, Sim et al1 called for: • Interoperable machine-interpretable evidence repositories derived from clinical trials, systematic reviews, and decision models; • Interoperable machine-readable and executable guidelines repositories; • A standardized system to link these two repositories to facilitate CDSS; • An informatics infrastructure for practice-based research networks to collect practice-based evidence. CancerLinq is designed to achieve those objectives.
Medical Literature Knowledge Bases Clinical practice guidelines are constrained by knowledge gaps in existing medical literature. Although expert opinion is now used to bridge these gaps, RLS can provide clinical correlations that support or challenge expert opinion with real world patient care experiences, leading to iterative improvements in clinical practice guidelines and their associated CDSS. The database of oncology pharmaceuticals will be augmented by mining patient EHRs for documentation of less common or long-term toxicities not evident in clinical trials and by capturing drug-drug interactions missed because trial eligibility criteria often exclude comorbid diseases treated with other pharmaceutical classes of drugs.
Health Care Systems Operations Knowledge Bases The large database of real-world patient experiences can serve to validate or refute clinical impressions or biases. Health services research into care delivery for sub-populations can provide new insights into health care disparities across ethnic, geographic, or socioeconomic classes. CDSS built to support ASCO’s Quality Oncology Practice Initiative can improve the value of cancer care by prospectively guiding clinical decision-making to achieve better quality of care in a patient-centered manner. Research into the delivery of palliative care and cancer survivorship care can be performed on health care delivery systems data and CDSS can support both clinicians and patients in delivering these aspects of cancer care.
Conclusion Health learning depends on a foundation of data that spring from multiple sources. By presuming that one source of data is inherently superior to others, we limit the scope of our vision. The challenges to health care can only be successfully met by embracing all sources of knowledge, all methods of learning, and a will to find new solutions that reflect a global vision of the use of HIT.9 Author’s Disclosures of Potential Conflicts of Interest Disclosures provided by the authors are available with this article at jop.ascopubs.org. Corresponding author: Peter Paul Yu, MD, Palo Alto Medical Foundation, 795 El Camino Real, Hematology-Oncology, SV 301, Palo Alto, CA 94301; e-mail: [email protected].
DOI: 10.1200/JOP.2014.000620; published online ahead of print at jop.ascopubs.org on February 24, 2015.
References 1. Sim I, Gorman P, Greenes RA, et al: Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc 8: 527-534, 2001
3. National Cancer Institute: The Cancer Genome Atlas: Genome Characterization Centers. http://cancergenome.nih.gov/abouttcga/overview/howitworks/ characterizationcenters
2. Lobach D, Sanders GD, Bright TJ, et al: Enabling Health Care Decisionmaking Through Clinical Decision Support and Knowledge Management. AHRQ Publication No. 12-E001-EF, 2012. http://www.ncbi.nlm.nih.gov/books/NBK97318/pdf/ TOC.pdf
4. Global Alliance for Genomics and Health. http://genomicsandhealth.org
Copyright © 2015 by American Society of Clinical Oncology
5. Shrager J, Tenenbaum JM: Rapid learning for precision oncology. Nat Rev Clin Oncol 11:109-118, 2014
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6. Kent WJ, Sagnet CW, Furey TS, et al: The Human Genome Browser at UCSC. http://genome.cshlp.org/content/12/6/996
(“Infobutton”) Knowledge Request, Release 2. http://www.hl7.org/implement/ standards/product_brief.cfm?product_id⫽208
7. Haussler R: Cloud Based Resource for Data Hosting, Visualization and Analysis Using UCSC Cancer Genomics Browser. http://itcr.nci.nih.gov/fundedprojects/projects/hausslerd/
14. Center for Medical Informatics/Yale School of Medicine: GuideLines Into Decision Support (GLIDES). http://medicine.yale.edu/cmi/glides/index.aspx
8. Brown MPS, Grundy WN, Lin D: Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci U S A 97: 262-267, 2000
15. Sledge GW Jr, Miller RS, Hauser R: CancerLinQ and the future of cancer care. ASCO Educational Book, Alexandria, VA, ASCO, 2013, pp 430-434 16. Eberhardt J, Bilchik A, Stojadinovic A: Clinical decision support systems: Potential with pitfalls. J Surg Oncol 105: 502-510, 2012
9. Institute of Medicine: Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington, DC, National Academies Press, 2013
17. Forsberg JA, Eberhardt J, Boland PJ, et al: Estimating survival in patients with operable skeletal metastases: An application of a Bayesian belief network. PLoS ONE 6(5): e19956, 2011
10. Schilsky RL: Implementing personalized cancer care. Nat Rev Clin Oncol 11:432-438, 2014
18. Kim SY, Moon SK, Jung DC, et al: Pre-operative prediction of advanced prostate cancer using clinical decision support systems: Accuracy comparison between support vector machine and artificial neural network. Korean J Radiol 12:588-594, 2011
11. Abernethy AP, Etheredge LM, Ganz PA, et al: Rapid-learning system for cancer care. J Clin Oncol 28: 4268-4274, 2010 12. Musen MA, Greenes RA, Middleton B: Clinical decision-support systems, in Shortliffe EH, Cimino JJ (eds): Biomedical Informatics: Computer Applications in Health Care and Biomedicine. London, United Kingdom, Springer London, 2014 13. Health Level 7 International: Section 3: Clinical and Administrative Domains: HL7 Version 3 Standard—Context Aware Knowledge Retrieval Application
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19. Bennett CC, Hauser K: Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach. Artif Intell Med 57:9-19, 2013 20. Faith-Michael EU, Obot O, Barker K, et al: An experimental comparison of fuzzy logic and analytic hierarchy process for medical decision support systems. Comput Methods Programs Biomed 103:10-27, 2011
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AUTHOR’S DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST Knowledge Bases, Clinical Decision Support Systems, and Rapid Learning in Oncology The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I ⫽ Immediate Family Member, Inst ⫽ My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO’s conflict of interest policy, please refer to www.asco.org/rwc or jop.ascopubs.org/site/misc/ifc.xhtml. Peter Paul Yu Stock or Other Ownership: Apple, Contrafect, Citrix Systems Inc, EMC Corp, Google, IBM, Oracle, Amazon Research Funding: Berg Pharmaceuticals (Inst)
Copyright © 2015 by American Society of Clinical Oncology
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Original Contribution
Knowledge Bases, Clinical Decision Support Systems, and Rapid Learning in Oncology By Peter Paul Yu, MD Palo Alto Medical Foundation, Palo Alto, CA
Abstract One of the most important benefits of health information technology is to assist the cognitive process of the human mind in the face of vast amounts of health data, limited time for decision making, and the complexity of the patient with cancer. Clinical decision support tools are frequently cited as a technologic solution to this problem, but to date useful clinical decision support
systems (CDSS) have been limited in utility and implementation. This article describes three unique sources of health data that underlie fundamentally different types of knowledge bases which feed into CDSS. CDSS themselves comprise a variety of models which are discussed. The relationship of knowledge bases and CDSS to rapid learning health systems design is critical as CDSS are essential drivers of rapid learning in clinical care.
Introduction
Clinical Trials
Rapid learning health systems (RLS) are health care systems that align culture and technology to aggregate individual patient data across populations that share common characteristics defining a health state in order to generate knowledge and learning. Although the Institute of Medicine has pointed to the RLS model as critical for accelerating improvements in health outcomes, no such systems have been designed or implemented in health care to date. It is important to realize that health care systems need not be limited to hospital-based systems or to large medical groups, but could be composed of independent entities that choose to share data to support common goals, including patients and professional medical societies. Learning results from the transformation of such data into knowledge and the subsequent application of that knowledge to patient care. Thus there is a sequential process of transforming data into knowledge and knowledge into learning. Data sources are varied and include translational science data, clinical trials data, patient sourced data, and health systems data on operational processes and patient outcomes. When data repositories are analyzed and correlations observed and models generated to make sense of data, a knowledge base is established. The resulting knowledge base may then generate a clinical practice guideline or an algorithm. Knowledge bases are elastic, expanding and evolving as new data are acquired and models revisited.1 However, knowledge achieves clinical utility only when it becomes actionable to improve patient health. One can think of learning as the process of applying knowledge to improve patient outcomes, quality, and value of health care.
The first knowledge base derives from clinical trials that have been peer-reviewed and published in medical literature. Clinical trials produce data to either support or refute a clinical model expressed as a null hypothesis. If a sufficient number of clinical trials are available, a meta-analysis of pooled data across trials can be performed whereby the aggregation of data creates greater statistical power to observe clinical benefit. The individual trials whose data are combined in a meta-analysis differ in exact study design and execution, but the principle is established that the loss of methodological rigor of trial design is more than compensated for by the improved ability to detect signals of clinical utility. In other words, the need for defining homogenous study populations is driven by the constraints of study population size. With larger data sets the need for rigid control of patient variables diminishes. If sufficiently convincing, a US Food and Drug Administration drug indication or compendium listing can codify the knowledge base on a new therapeutic. Another accepted method to create knowledge bases from clinical trial data is systematic reviews of medical literature. Here, the aggregation does not occur at the level of clinical trial data, but rather at the level of the knowledge derived from each trial. The knowledge from each trial is subjectively weighted for the quality of the study and remaining gaps in knowledge are addressed through expert consensus opinion. The result is a knowledge base expressed as a clinical practice guideline issued by an authoritative body, typically a professional society. This model of distilling medical literature into clinical practice guidelines has been endorsed by Agency for Healthcare Research and Quality.2 However, there are significant limitations to this process such as the length of time required to build the knowledge base, lack of transparency about the degree to which a guideline is dependent on expert opinion, and the difficulty of refining the knowledge base over time as new literature accumulates. Nevertheless, both of these examples, meta-analysis
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Table 1. Knowledge Bases Clinical Trials
Systems Biology
Health Care Systems
Meta-analysis and systematic reviews
Cancer biology
Operational data
Rules-based CDSS
Probabilistic CDSS
CDSS developed in local context
Population based
Individual based
Health system based
Abbreviation: CDSS, clinical decision support systems.
and clinical practice guidelines, make it clear that data aggregation and synthesis are pivotal to learning.
Systems Biology Systems biology is a second data source whose rapid expansion is improving our ability to create and refine disease models to better understand the causation, classification, and potential therapeutic interventions of human disease. In oncology, systems biology denotes our understanding of the molecular causes of cancer and disruptions to regulatory control of cellular growth and cancer immunology. It is challenging to maintain systems biology– derived knowledge bases given the volume, velocity, and variety at which new molecular data accumulates. Each oncology patient generates terabytes of systems biology data composed of genomics, transcriptomics, proteomics, and metabolomics data, collectively referred to as panomics. Unlike medical literature, which is dependent on the timeframe of clinical trial design, trial activation, accrual completion, and data maturation, cancer systems biology follows the speed of panomic research. The Cancer Genome Atlas3 will accelerate cancer systems biology knowledge through sequencing of cancer genomes and selective analysis of associated post-transcriptional events. The Global Alliance for Genomics and Health4 will facilitate interoperable sharing of genomic and clinical data sets among researchers. The technology to support biobanks of human specimens and high-throughput molecular analysis is rapidly becoming affordable. The application of computational biology to create disease models based on molecular data holds great promise to accelerate targeted therapies. Models of carcinogenesis are being rapidly constructed from mappings of signal transduction, feedback, and regulatory pathways.5 Visualizing operative networks and discounting the background noise of gratuitous molecular aberrations resulting from an unstable cancer genome and defective DNA repair mechanisms will require iterative model building and complex analytic techniques through computational biology. Computational biology can accelerate the building of molecular models with those models subsequently refined by patient outcomes health data in response to molecularly targeted therapies.4,6,7,8 The potential for computational models to simulate clinical trials has been cited by the Institute of Medicine.9 It is important to note that the knowledge bases derived from clinical trials and systems biology are not mutually incompatible. Rather they can function in a dependent manner. A robust underlying molecular model adds to the confidence that the findings from a relatively small clinical trial are meaningful and may be sufficiently persuasive to make the conduct of larger Copyright © 2015 by American Society of Clinical Oncology
confirmatory phase III trial both unnecessary and indefensible, especially in orphan diseases. Another example of the potential interplay between clinical trials and systems biology is the identification of outlier patients with exceptionally strong tumor responses to treatment not seen in the remainder of the trial patient cohort. Such outlier responders would in the past have been ignored because the trial was designed to study the drug and not the patient. Instead, a focus on the patient to understand such exceptional responses could elucidate novel mechanisms of carcinogenesis and add to the systems biology knowledge base. Cancer registries can be utilized to build cancer system biology knowledge bases by collecting information on individual patient treatment responses or lack thereof from the off-label use of targeted therapies. The transition from single analyte biomarker tests to multiplex panel testing, next-generation sequencing or whole exome sequencing is raising the potential for wide-spread use of off-label targeted therapies and raises troubling questions over payment for such off-label use in the absence of a knowledge base to support their use. A mechanism to collect such patient experiences would help ensure that generalizable knowledge was identified and added to the knowledge base.10 N-of-1 observations such as these, whether documented through a clinical trial or through off-label use of a targeted therapy conducted under monitored novel patient access mechanism, can rapidly expand our understanding of cancer and our therapeutic options.
Health Care Systems Health care systems data is a rapidly expanding third type of knowledge base that is increasingly available in digital format. These are observational data sets. Sources of health care systems data are varied and include electronic health records (EHRs), patient-reported data, laboratory information systems and data repositories, administrative claims data, cancer registries, and postmarketing surveillance. These are diverse and rich data streams that directly facilitate improving the value of health care delivery through comparative effectiveness research, reduction in unwarranted variation in health care delivery, and quality improvement. A special attribute of health care systems data is that it reflects real-world patient experiences; these patients are not preselected by the eligibility criteria of clinical trials, which require homogenous populations with limited comorbidities in order to better pick up weak signals of efficacy. The value of clinical trial knowledge bases is tempered by the restrictions placed on the study population by trial eligibility requirements. Health care systems knowledge bases can provide urgently needed information on whether clinical trial results are replicable in general cancer populations and whether the clinical trial treatment regimen as published should be modified for clinical use in patients with comorbidities and multiple medications that may increase the risk for harm. Health systems operations knowledge bases are complementary to literature-based knowledge bases, again emphasizing that the three knowledge bases described are interdependent.
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Table 2. Types of CDSS
RLS The process of transitioning from data to knowledge bases and finally to learning can be greatly accelerated by use of health information technology (HIT). The volume and diversity of data and the necessity for data aggregation and analytics require that HIT be the backbone of rapid learning. The vision of transforming health care and health research through HIT has led to the RLS model where EHRs and other repositories of digital health information are made available for individualand population-based learning.11 When the primary focus is on improving the quality and value of health care interventions, RLS is particularly dependent on health care operations data that capture processes and outcomes of routine health care delivery. This rapid learning occurs on two levels. For the individual provider and patient, the learning results from accessing a knowledge base that informs the patient’s clinical situation through CDSS. With learning defined as the application of knowledge to patient care, CDSS is the technology that accelerates the dissemination of new knowledge into the routine care of patients with cancer. The second level of learning in RLS occurs at the health system level when aggregated and large patient health data sets drive generalizable knowledge by creating or adding to an evergrowing health systems operations knowledge base.
CDSS CDSS are HIT applications that relate individual patient health data to established knowledge bases and thereby assist in clinical decision making and health management (Figure 1). CDSS can utilize any of the three previously described knowledge bases, but to function properly the triggers and data inputs into CDSS must be in a machine readable format. Narrative texts do not provide usable data unless they contain extractable data captured in structured fields or tagged by metadata that renders them searchable, and this remains a formidable barrier with the EHRs in use today. CDSS do not make decisions regarding medical care, but they provide information that may be relevant to a patient medical problem or present therapeutic choices in the management of disease that enable shared decision making
Outcomes
Shared Decision Making
Clinical Decision Support Systems
Health Care Systems KB
Systems Biology KB
Patient Data
CPGs KB
Figure 1. Contribution of clinical decision support systems to patient outcomes. CPG, clinical practice guideline; KB, knowledge base.
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CDSS Type
CDSS Example
Information management
Info Button, Up ToDate
Situational awareness
Alerts, dashboards
Patient specific
Logic-based guidance on diagnostic and therapeutic choices
Abbreviation: CDSS, clinical decision support systems.
between patients and providers. CDSS are critical elements to building an RLS, since they are instruments to realize learning applied to patient care. Musen et al12 has described three categories of CDSS (Table 2). First, CDSS can provide access to medical literature or educational material related to a clinical question and thereby augments the knowledge of the health care provider or patient. The Info button within an EHR is a technical solution to provide contextually relevant disease information.13 A second type of CDSS helps to focus the clinician on specific health data, an example of which are drug-drug interactions alerts. A graphical user interface that presents patient outcomes data in a visual display, thereby promoting clinical insights, is another example. Prebuilt reports, dashboards, and oncology flow sheets improve the usability of EHRs and can be invaluable for visualizing patterns of care. In an era when clinicians are struggling with less available time to spend with patients and with ever more data to contend with, this type of CDSS is critical for improving situational awareness and reducing the risk of medical errors. The third type of CDSS uses artificial intelligence or computational medicine to provide guidance on diagnostic or treatment interventions based on patient-specific data. Such CDSS can be characterized as computer-based systems that integrate a medical knowledge base with an individual patient’s data through a CDSS engine that applies a prespecified logic. CDSS engines use a variety of logics and knowledge bases to drive their clinical decision-support function. Which logic is best depends on the type of knowledge base that underlies the CDSS in question. Rules-based CDSS rely on one or more conditional statements triggered by predefined data elements and result in clinical decision support in the form of the rulesgenerated guidance. Rules can be linked, either linearly or by branched logic trees to guide patient decision making. Oncologists are familiar with National Comprehensive Cancer Network guidelines that provide a sequence of steps displayed as flow diagrams. American Society of Clinical Oncology (ASCO) guidelines are textually based and tolerant of greater ambiguity reflecting clinical reality but can still be distilled into a rulesbased format.14 ASCO’s RLS model, CancerLinQ has prototyped the conversion of five ASCO guidelines into machineready format.15 CDSS based on medical literature– derived knowledge bases are well suited for rules-based design. Rules-based design does not easily adapt to conditions where the underlying triggers are multiple and the clinical context multidimensional— conditions frequently encountered in oncology. Under these conditions, probabilistic design is more flexible, allowing for selective weighing of clinical factors and
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rapid model development.12 Here the probabilities of clinical events are determined and predictive algorithms constructed to predict likely or less likely outcomes resulting from clinical decision making. This process more closely approximates human judgment.16 CDSS based on cancer biology and health systems knowledge bases require flexibility in order to recognize and react to new knowledge. Inferential CDSS draws its force from specialized statistical analytic methods used in artificial intelligence including Bayesian-based logic, artificial neural networks, and support vector machines.17,18,19,20 Finally CDSS derived from health systems knowledge bases will inherently reflect the blend of culture, experiences, and resources unique to the local environment.
Systems Biology–Based Knowledge Bases The Global Alliance for Genomics and Health is an international effort to create a digital system for sharing of laboratorybased research, translational and clinical data. CancerLinq will be a major contributor of clinical data to such collaborative efforts, data that have been prepared through computational analysis of clinically meaningful end points. CDSS will assist oncologists with the appropriate selection of diagnostic and predictive molecular tests and CDSS will use those results to trigger suggestions of possible actionable interventions. Such interventions will support routine cancer care as well as clinical trials or registry studies based on systems biology– based models of cancer.
CancerLinQ CancerLinQ seeks to integrate all three knowledge bases; medical literature based, systems biology based, and health care systems based. By doing so in the context of a patient-centered care through CDSS, Cancer LinQ will achieve more rapid dissemination of knowledge into clinical practice. Concomitantly it will generate knowledge about improving the quality and cost effectiveness of patient care and generate new avenues of inquiry through observational N-of-1 experience. More than a decade ago, Sim et al1 called for: • Interoperable machine-interpretable evidence repositories derived from clinical trials, systematic reviews, and decision models; • Interoperable machine-readable and executable guidelines repositories; • A standardized system to link these two repositories to facilitate CDSS; • An informatics infrastructure for practice-based research networks to collect practice-based evidence. CancerLinq is designed to achieve those objectives.
Medical Literature Knowledge Bases Clinical practice guidelines are constrained by knowledge gaps in existing medical literature. Although expert opinion is now used to bridge these gaps, RLS can provide clinical correlations that support or challenge expert opinion with real world patient care experiences, leading to iterative improvements in clinical practice guidelines and their associated CDSS. The database of oncology pharmaceuticals will be augmented by mining patient EHRs for documentation of less common or long-term toxicities not evident in clinical trials and by capturing drug-drug interactions missed because trial eligibility criteria often exclude comorbid diseases treated with other pharmaceutical classes of drugs.
Health Care Systems Operations Knowledge Bases The large database of real-world patient experiences can serve to validate or refute clinical impressions or biases. Health services research into care delivery for sub-populations can provide new insights into health care disparities across ethnic, geographic, or socioeconomic classes. CDSS built to support ASCO’s Quality Oncology Practice Initiative can improve the value of cancer care by prospectively guiding clinical decision-making to achieve better quality of care in a patient-centered manner. Research into the delivery of palliative care and cancer survivorship care can be performed on health care delivery systems data and CDSS can support both clinicians and patients in delivering these aspects of cancer care.
Conclusion Health learning depends on a foundation of data that spring from multiple sources. By presuming that one source of data is inherently superior to others, we limit the scope of our vision. The challenges to health care can only be successfully met by embracing all sources of knowledge, all methods of learning, and a will to find new solutions that reflect a global vision of the use of HIT.9 Author’s Disclosures of Potential Conflicts of Interest Disclosures provided by the authors are available with this article at jop.ascopubs.org. Corresponding author: Peter Paul Yu, MD, Palo Alto Medical Foundation, 795 El Camino Real, Hematology-Oncology, SV 301, Palo Alto, CA 94301; e-mail: [email protected].
DOI: 10.1200/JOP.2014.000620; published online ahead of print at jop.ascopubs.org on February 24, 2015.
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AUTHOR’S DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST Knowledge Bases, Clinical Decision Support Systems, and Rapid Learning in Oncology The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I ⫽ Immediate Family Member, Inst ⫽ My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO’s conflict of interest policy, please refer to www.asco.org/rwc or jop.ascopubs.org/site/misc/ifc.xhtml. Peter Paul Yu Stock or Other Ownership: Apple, Contrafect, Citrix Systems Inc, EMC Corp, Google, IBM, Oracle, Amazon Research Funding: Berg Pharmaceuticals (Inst)
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