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Guiding principles for the optimal use of data analytics by physicians at the point of care

https://policybase.cma.ca/en/permalink/policy11812
Last Reviewed
2020-02-29
Date
2016-02-27
Topics
Health information and e-health
  1 document  
Policy Type
Policy document
Last Reviewed
2020-02-29
Date
2016-02-27
Topics
Health information and e-health
Text
Electronic tools are now being used more widely in medicine than ever before. A majority of physicians in Canada have adopted electronic medical records (EMRs)-75% of physicians use EMRs to enter or retrieve clinical patient notes, and 80% use electronic tools to access laboratory/diagnostic test results. The increased use of point-of-care tools and information repositories has resulted in the mass digitization and storage of clinical information, which provides opportunities for the use of big data analytics. Big data analytics may come to be understood as the process of examining clinical data in EMRs cross-referenced with other administrative, demographic and behavioural data sources to reveal determinants of patient health and patterns in clinical practice. Its increased use may provide opportunities to develop and enhance clinical practice tools and to improve health outcomes at both point-of-care and population levels. However, given the nature of EMR use in Canada, these opportunities may be restricted to primary care practice at this time. Physicians play a central role in finding the right balance between leveraging the advantages of big data analytics and protecting patient privacy. Guiding Principles for the Optimal Use of Data Analytics by Physicians at the Point of Care outlines basic considerations for the use of big data analytics services and highlights key considerations when responding to requests for access to EMR data, including the following: * Why will data analytics be used? Will the safety and effectiveness of patient care be enhanced? Will the results be used to inform public health measures? * What are the responsibilities of physicians to respect and protect patient and physician information, provide appropriate information during consent conversations, and review data sharing agreements and consult with EMR vendors to understand how data will be used? As physicians will encounter big data analytics in a number of ways, this document also outlines the characteristics one should be looking for when assessing the safety and effectiveness of big data analytics services: * protection of privacy * clear and detailed data sharing agreement * physician-owned and -led data collaboratives * endorsement by a professional or recognized association, medical society or health care organization * scope of services and functionality/appropriateness of data While this guidance is not a standalone document-it should be used as a supplemental reference to provincial privacy legislation-it is hoped that it can aid physicians to identify suitable big data analytics services and derive benefits from them. Introduction This document outlines basic considerations for the use of big data analytics services at the point of care or for research approved by a research ethics board. This includes considerations when responding to requests for access to data in electronic medical records (EMRs). These guiding principles build on the policies of the Canadian Medical Association (CMA) on Data Sharing Agreements: Principles for Electronic Medical Records/Electronic Health Records,1 Principles Concerning Physician Information2 and Principles for the Protection of Patients' Personal Health Information,3 the 2011 clinical vignettes Disclosing Personal Health Information to Third Parties4 and Need to Know and Circle of Care,5 and the Canadian Medical Protective Association's The Impact of Big Data on Healthcare and Medical Practice.6 These guiding principles are for information and reference only and should not be construed as legal or financial advice, nor is this document a substitute for legal or other professional advice. Physicians must always comply with all legislation that applies to big data analytics, including privacy legislation. Big data analytics in the clinical context involves the collection, use and potential disclosure of patient and physician information, both of which could be considered sensitive personal information under privacy legislation. Big data analytics has the potential to improve health outcomes, both at the point of care and at a population level. Doctors have a key role to play in finding the right balance between leveraging the advantages of big data (enhanced care, service delivery and resource management) and protecting patient privacy.7 Background A majority of physicians in Canada have adopted EMRs in their practice. The percentage of physicians using EMRs to enter or retrieve clinical patient notes increased from 26% in 2007 to 75% in 2014. Eighty percent of physicians used electronic tools to access laboratory/diagnostic test results in 2014, up from 38% in 2010.8 The increasingly broad collection of information by physicians at the point of care, combined with the growth of information repositories developed by various governmental and intergovernmental bodies, has resulted in the mass digitization and storage of clinical information. Big data is the term for data sets so large and complex that it is difficult to process them using traditional relational database management systems, desktop statistics and visualization software. What is considered "big" depends on the infrastructure and capabilities of the organization managing the data.9 Analytics is the discovery and communication of meaningful patterns in data. Analytics relies on the simultaneous application of statistics, computer programming and operations research. Analytics often favours data visualization to communicate insight, and insights from data are used to guide decision-making.10 For physicians, big data analytics may come to be understood as the process of examining the clinical data in EMRs cross-referenced with other administrative, demographic and behavioural data sources to reveal determinants of patient health and patterns in clinical practice. This information can be used to assist clinical decision-making or for research approved by a research ethics board. There are four types of big data analytics physicians may encounter in the provision of patient care. They are generally performed in the following sequence, in a continuous cycle11,12,13,14: 1. Population health analytics: Health trends are identified in the aggregate within a community, a region or a national population. The data can be derived from biomedical and/or administrative data. 2. Risk-based cost analysis: Populations are segmented into groups according to the level of risk to the patient's health and/or cost to the health system. 3. Care management: Clinicians are enabled to manage patient care according to defined care pathways and clinical protocols informed by population health analytics and risk-based cost analysis. Care management includes the following: o Clinical decision support: Outcomes are predicted and/or alternative treatments are recommended to clinicians and patients at the point of care. o Personalized/precision care: Personalized data sets, such as genomic DNA sequences for at-risk patients, are leveraged to highlight best practice treatments for patients and practitioners. These solutions may offer early detection and diagnosis before a patient develops disease symptoms. o Clinical operations: Workflow management is performed, such as wait-times management, mining historical and unstructured data for patterns to predict events that may affect care. o Continuing education and professional development: Longitudinal performance data are combined across institutions, classes, cohorts or programs with correlating patient outcomes to assess models of education and/or develop new programs. 4. Performance analytics: Metrics for quality and efficiency of patient care are cross-referenced with clinical decision-making and performance data to assess clinical performance. This cycle is also sometimes understood as a component of "meaningful" or "enhanced" use of EMRs. How might physicians encounter big data analytics? Many EMRs run analytics both visibly (e.g., as a function that can be activated at appropriate junctures in the care pathway) and invisibly (e.g., as tools that run seamlessly in the background of an EMR). Physicians may or may not be aware when data are being collected, analyzed, tailored or presented by big data analytics services. However, many jurisdictions are strengthening their laws and standards, and best practices are gradually emerging.15 Physicians may have entered into a data sharing agreement with their EMR vendor when they procured an EMR for their practice. Such agreements may include provisions to share de-identified (i.e., anonymized) and/or aggregate data with the EMR vendor for specified or unspecified purposes. Physicians may also receive requests from third parties to share their EMR data. These requests may come from various sources: * provincial governments * intergovernmental agencies * national and provincial associations, including medical associations * non-profit organizations * independent researchers * EMR vendors, service providers and other private corporations National Physician Survey results indicate that in 2014, 10% of physicians had shared data from their EMRs for the purposes of research, 10% for chronic disease surveillance and 8% for care improvement. Family physicians were more likely than other specialists to share with public health agencies (22% v. 11%) and electronic record vendors (13% v. 2%). Specialists were more likely than family physicians to share with researchers (59% v. 37%), hospital departments (47% v. 20%) and university departments (28% v. 15%). There is significant variability across the provinces with regard to what proportion of physicians are sharing information from their EMRs, which is affected by the presence of research initiatives, research objectives defined by the approval of a research ethics board, the adoption rates of EMRs among physicians in the province and the functionality of those EMRs.16 For example, there are family practitioners across Canada who provide data to the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). The CPCSSN is a multi-disease EMR surveillance and research system that allows family physicians, epidemiologists and researchers to understand and manage chronic care conditions for patients. Health information is collected from EMRs in the offices of participating family physicians, specifically information about Canadians suffering from chronic and mental health conditions and three neurologic conditions, including Alzheimer's and related dementias.17 In another example, the Canadian Partnership Against Cancer's Surgical Synoptic Reporting Initiative captures standardized information about surgery at the point of care and transmits the surgical report to other health care personnel. Surgeons can use the captured information, which gives them the ability to assess adherence to the clinical evidence and safety procedures embedded in the reporting templates, to track their own practices and those of their community.18 The concept of synoptic reporting-whereby a physician provides anonymized data about their practice in return for an aggregate report summarizing the practice of others -can be expanded to any area in which an appropriate number of physicians are willing to participate. Guiding principles for the use of big data analytics These guiding principles are designed to give physicians a starting point as they consider the use of big data analytics in their practices: * The objective of using big data analytics must be to enhance the safety and/or effectiveness of patient care or for the purpose of health promotion. * Should a physician use big data analytics, it is the responsibility of the physician to do so in a way that adheres to their legislative, regulatory and/or professional obligations. * Physicians are responsible for the privacy of their individual patients. Physicians may wish to refer to the CMA's policy on Principles for the Protection of Patients' Personal Health Information.19 * Physicians are responsible for respecting and protecting the privacy of other physicians' information. Physicians may wish to refer to the CMA's policy on Principles Concerning Physician Information.20 * When physicians enter into and document a broad consent discussion with their patient, which can include the electronic management of health information, this agreement should convey information to cover the elements common to big data analytics services. * Physicians may also wish to consider the potential for big data analytics to inform public health measures and enhance health system efficiency and take this into account when responding to requests for access to data in an EMR. * Many EMR vendors provide cloud-based storage to their clients, so information entered into an EMR may be available to the EMR vendor in a de-identified and/or aggregate state. Physicians should carefully read their data sharing agreement with their EMR vendor to understand how and why the data that is entered into an EMR is used, and/or they should refer to the CMA's policy on the matter, Data Sharing Agreements: Principles for Electronic Medical Records/Electronic Health Records.21 * Given the dynamic nature of this emerging tool, physicians are encouraged to share information about their experiences with big data analytics and its applications with colleagues. Characteristics of safe and effective big data analytics services 1. Protection of privacy Privacy and security concerns present a challenge in linking big data in EMRs. As data are linked, it becomes increasingly difficult to de-identify individual patients.22 As care is increasingly provided in interconnected, digital environments, physicians are having to take on the role of data stewardship. To that end, physicians may wish to employ conservative risk assessment practices-"should we" as opposed to "can we" when linking data sources-and obtain express patient consent, employing a "permission-based" approach to the collection and stewardship of data. 2. A clear and detailed data sharing agreement Physicians entering into a contract with an EMR vendor or other third party for provision of services should understand how and when they are contributing to the collection of data for the purposes of big data analytics services. There are template data sharing agreements available, which include the basic components of safe and effective data sharing, such as the model provided by the Information and Privacy Commissioner of Ontario.23 Data sharing agreements may include general use and project-specific use, both of which physicians should assess before entering into the agreement. When EMR access is being provided to a ministry of health and/or regional health authority, the data sharing agreement should distinguish between access to administrative data and access to clinical data. Physicians may wish to refer to the CMA's policy on Data Sharing Agreements: Principles for Electronic Medical Records/Electronic Health Records.24 3. Physician-owned and -led data collaboratives In some provinces there may exist opportunities to share clinical data in physician-owned and -led networks to reflect on and improve patient care. One example is the Physicians Data Collaborative in British Columbia, a not-for-profit organization open to divisions of family practice.25 Collaboratives such as this one are governed by physicians and driven by a desire to protect the privacy and safety of patients while producing meaningful results for physicians in daily practice. Participation in physician-owned data collaboratives may ensure that patient data continue to be managed by physicians, which may lead to an appropriate prioritization of physicians' obligations to balance patient-centred care and patient privacy. 4. Endorsement by a professional or other recognized association or medical society or health care organization When considering use of big data analytics services, it is best to select services created or endorsed by a professional or other recognized association or medical society. Some health care organizations, such as hospitals, may also develop or endorse services for use in their clinical environments. Without such endorsement, physicians are advised to proceed with additional caution. 5. Scope of services and functionality/appropriateness of data Physicians may wish to seek out information from EMR vendors and service providers about how big data analytics services complement the process of diagnosis and about the range of data sources from which these services draw. While big data analytics promises insight into population health and practice trends, if it is not drawing from an appropriate level of cross-referenced sources it may present a skewed picture of both.26 Ultimately, the physician must decide if the sources are appropriately diverse. Physicians should expect EMR vendors and service providers to make clear how and why they draw the information they do in the provision of analytics services. Ideally, analytics services should integrate population health analytics, risk-based cost analysis, care management services (such as point-of-care decision support tools) and performance analytics. Physicians should expect EMR vendors to allocate sufficient health informatics resources to information management, technical infrastructure, data protection and response to breaches in privacy, and data extraction and analysis.27,28 Physicians may also wish to consider the appropriateness of data analytics services in the context of their practices. Not all data will be useful for some medical specialties, such as those treating conditions that are relatively rare in the overall population. The potential for new or enhanced clinical practice tools informed by big data analytics may be restricted to primary care practice at this time.29 Finally, predictive analytics often make treatment recommendations that are designed to improve the health outcomes in a population, and these recommendations may conflict with physicians' ethical obligations to act in the best interests of individual patients and respect patients' autonomous decision-making).30 References 1 Canadian Medical Association. Data sharing agreements: principles for electronic medical records/electronic health records [CMA policy]. Ottawa: The Association; 2009. Available: http://policybase.cma.ca/dbtw-wpd/Policypdf/PD09-01.pdf 2 Canadian Medical Association. Principles concerning physician information [CMA policy]. CMAJ 2002 167(4):393-4. Available: http://policybase.cma.ca/dbtw-wpd/PolicyPDF/PD02-09.pdf 3 Canadian Medical Association. Principles for the protection of patients' personal health information [CMA policy]. Ottawa: The Association; 2010. Available: http://policybase.cma.ca/dbtw-wpd/Policypdf/PD11-03.pdf 4 Canadian Medical Association. Disclosing personal health information to third parties. Ottawa: The Association; 2011. Available: www.cma.ca/Assets/assets-library/document/en/advocacy/CMA_Disclosure_third_parties-e.pdf 5 Canadian Medical Association. Need to know and circle of care. Ottawa: The Association; 2011. Available: www.cma.ca/Assets/assets-library/document/en/advocacy/CMA_Need_to_know_circle_care-e.pdf 6 Canadian Medical Protective Association. The impact of big data on healthcare and medical practice. Ottawa: The Association; no date. Available: https://oplfrpd5.cmpa-acpm.ca/documents/10179/301372750/com_14_big_data_design-e.pdf 7 Kayyali B, Knott D, Van Kuiken S. The 'big data' revolution in US health care: accelerating value and innovation. New York: McKinsey & Company; 2013. p. 1. 8 College of Family Physicians of Canada, Canadian Medical Association, Royal College of Physicians and Surgeons of Canada. National physician survey, 2014. National results by FP/GP or other specialist, sex, age and all physicians. Q7. Ottawa: The Colleges and Association; 2014. Available: http://nationalphysiciansurvey.ca/wp-content/uploads/2014/08/2014-National-EN-Q7.pdf 9 Anonymous. Data, data everywhere. The Economist 2010 Feb 27. Available: www.economist.com/node/15557443 10 Anonymous. Data, data everywhere. The Economist 2010 Feb 27. Available: www.economist.com/node/15557443 11 Canada Health Infoway. Big data analytics in health. Toronto: Canada Health Infoway; 2013. Available: www.infoway-inforoute.ca/index.php/resources/technical-documents/emerging-technology/doc_download/1419-big-data-analytics-in-health-white-paper-full-report (accessed 2014 May 16). 12 Ellaway RH, Pusic MV, Galbraith RM, Cameron T. 2014 Developing the role of big data and analytics in health professional education. Med Teach 2014;36(3):216-222. 13 Marino DJ. Using business intelligence to reduce the cost of care. Healthc Financ Manage 2014;68(3):42-44, 46. 14 Porter ME, Lee TH. The strategy that will fix health care. Harv Bus Rev 2013;91(10):50-70. 15 Baggaley C. Data protection in a world of big data: Canadian Medical Protective Association information session [presentation]. 2014 Aug 20. Available: https://oplfrpd5.cmpa-acpm.ca/documents/10179/301372750/com_2014_carmen_baggaley-e.pdf 16 College of Family Physicians of Canada, Canadian Medical Association, Royal College of Physicians and Surgeons of Canada. National physician survey, 2014. National results by FP/GP or other specialist, sex, age and all physicians. Q10. Ottawa: The Colleges and Association; 2014. Available: http://nationalphysiciansurvey.ca/wp-content/uploads/2014/08/2014-National-EN-Q10.pdf 17 Canadian Primary Care Sentinel Surveillance Network. Available: http://cpcssn.ca/ (accessed 2014 Nov 15). 18 Canadian Partnership Against Cancer. Sustaining action toward a shared vision: 2012-2017 strategic plan. Toronto: The Partnership; no date. Available: www.partnershipagainstcancer.ca/wp-content/uploads/sites/5/2015/03/Sustaining-Action-Toward-a-Shared-Vision_accessible.pdf 19 Canadian Medical Association. Principles for the protection of patients' personal health information [CMA policy]. Ottawa: The Association; 2011. Available: http://policybase.cma.ca/dbtw-wpd/Policypdf/PD11-03.pdf 20 Canadian Medical Association. Principles for the protection of patients' personal health information [CMA policy]. Ottawa: The Association; 2011. Available: http://policybase.cma.ca/dbtw-wpd/Policypdf/PD11-03.pdf 21 Canadian Medical Association. Data sharing agreements: principles for electronic medical records/electronic health records [CMA policy]. Ottawa: The Association; 2009. Available: http://policybase.cma.ca/dbtw-wpd/Policypdf/PD09-01.pdf 22 Weber G, Mandl KD, Kohane IS. Finding the missing link for big biomedical data . JAMA 2014;311(24):2479-2480. doi:10.1001/jama.2014.4228. 23 Information and Privacy Commissioner of Ontario. Model data sharing agreement. Toronto: The Commissioner; 1995. Available: www.ipc.on.ca/images/Resources/model-data-ag.pdf 24 Canadian Medical Association. Data sharing agreements: principles for electronic medical records/electronic health records [CMA policy]. Ottawa: The Association; 2009. Available: http://policybase.cma.ca/dbtw-wpd/Policypdf/PD09-01.pdf 25 Physicians Data Collaborative. Overview. Available: www.divisionsbc.ca/datacollaborative/home 26 Cohen IG, Amarasingham R, Shah A, Xie B, Lo B. The legal and ethical concerns that arise from using complex predictive analytics in health care. Health Aff 2014;33(7):1139-1147. 27 Rhoads J, Ferrara L. Transforming healthcare through better use of data. Electron Healthc 2012;11(1):e27. 28 Canadian Medical Protective Association. The impact of big data and healthcare and medical practice. Ottawa: The Association; no date. Available: https://oplfrpd5.cmpa-acpm.ca/documents/10179/301372750/com_14_big_data_design-e.pdf 29 Genta RM, Sonnenberg A. Big data in gastroenterology research. Nat Rev Gastroenterol Hepatol 2014;11(6):386-390. 30 Cohen IG, Amarasingham R, Shah A, Xie B, Lo B. The legal and ethical concerns that arise from using complex predictive analytics in health care. Health Aff 2014;33(7):1139-1147.
Documents
Less detail
Last Reviewed
2020-02-29
Date
2008-08-20
Topics
Health systems, system funding and performance
Physician practice/ compensation/ forms
Health information and e-health
Resolution
GC08-95
The Canadian Medical Association, in consultation with provincial/territorial medical associations, the College of Family Physicians of Canada and the Royal College of Physicians and Surgeons of Canada, will work with professional regulatory/licensing bodies to establish a harmonized policy environment that would support physicians who are providing telehealth care in multiple jurisdictions.
Policy Type
Policy resolution
Last Reviewed
2020-02-29
Date
2008-08-20
Topics
Health systems, system funding and performance
Physician practice/ compensation/ forms
Health information and e-health
Resolution
GC08-95
The Canadian Medical Association, in consultation with provincial/territorial medical associations, the College of Family Physicians of Canada and the Royal College of Physicians and Surgeons of Canada, will work with professional regulatory/licensing bodies to establish a harmonized policy environment that would support physicians who are providing telehealth care in multiple jurisdictions.
Text
The Canadian Medical Association, in consultation with provincial/territorial medical associations, the College of Family Physicians of Canada and the Royal College of Physicians and Surgeons of Canada, will work with professional regulatory/licensing bodies to establish a harmonized policy environment that would support physicians who are providing telehealth care in multiple jurisdictions.
Less detail

Access to medical information

https://policybase.cma.ca/en/permalink/policy9280
Last Reviewed
2020-02-29
Date
2008-08-20
Topics
Ethics and medical professionalism
Health information and e-health
Resolution
GC08-113
The Canadian Medical Association objects to the current practice of insurers, employers and other third parties requesting and gaining access to unlimited medical information obtained as a result of patients signing forms that grant unrestricted 'consent for release of medical information' when claiming eligibility for disability benefits.
Policy Type
Policy resolution
Last Reviewed
2020-02-29
Date
2008-08-20
Topics
Ethics and medical professionalism
Health information and e-health
Resolution
GC08-113
The Canadian Medical Association objects to the current practice of insurers, employers and other third parties requesting and gaining access to unlimited medical information obtained as a result of patients signing forms that grant unrestricted 'consent for release of medical information' when claiming eligibility for disability benefits.
Text
The Canadian Medical Association objects to the current practice of insurers, employers and other third parties requesting and gaining access to unlimited medical information obtained as a result of patients signing forms that grant unrestricted 'consent for release of medical information' when claiming eligibility for disability benefits.
Less detail

Medical information

https://policybase.cma.ca/en/permalink/policy9281
Last Reviewed
2020-02-29
Date
2008-08-20
Topics
Ethics and medical professionalism
Health information and e-health
Health systems, system funding and performance
Resolution
GC08-114
The Canadian Medical Association and provincial/territorial medical associations will seek legislative amendments that make the requesting third party responsible for payment for the provision of medical information collected (with patient understanding and consent) for the purposes of a return to work program or accommodation in the workplace.
Policy Type
Policy resolution
Last Reviewed
2020-02-29
Date
2008-08-20
Topics
Ethics and medical professionalism
Health information and e-health
Health systems, system funding and performance
Resolution
GC08-114
The Canadian Medical Association and provincial/territorial medical associations will seek legislative amendments that make the requesting third party responsible for payment for the provision of medical information collected (with patient understanding and consent) for the purposes of a return to work program or accommodation in the workplace.
Text
The Canadian Medical Association and provincial/territorial medical associations will seek legislative amendments that make the requesting third party responsible for payment for the provision of medical information collected (with patient understanding and consent) for the purposes of a return to work program or accommodation in the workplace.
Less detail

Guiding Principles for Physician Electronic Medical Records (EMR) Adoption in Ambulatory Clinical Practice

https://policybase.cma.ca/en/permalink/policy9117
Last Reviewed
2019-03-03
Date
2008-02-23
Topics
Health information and e-health
  1 document  
Policy Type
Policy document
Last Reviewed
2019-03-03
Date
2008-02-23
Topics
Health information and e-health
Text
GUIDING PRINCIPLES FOR PHYSICIAN ELECTRONIC MEDICAL RECORDS (EMR) ADOPTION IN AMBULATORY CLINICAL PRACTICE The following principles outline what is important to physicians and why as they make the decision to adopt electronic medical record systems (EMRs) in ambulatory clinical practice. Physician adoption of the EMR has the potential to transform patient care and the quality of health statistics and health research in Canada, as long as the right conditions are met and the guiding principles outlined here are adhered to. Adoption of EMRs in clinical ambulatory practices will lead to significant improvements in data comprehensiveness, clinical relevance and quality — and this, in turn, will lead to improved clinical decision support, core data sets and health statistics that meet the primary goal of enhancing health care delivery, treatment and outcomes. PRINCIPLES General Policy
Privacy. A physician’s ethical and legal responsibility as data steward of the patient’s medical information must be protected and enhanced.1
Choice. There must be appropriate independence of choice that respects physicians’ professional and business autonomy. Physicians must be free to choose the EMR product that best meets the needs of their practice model, type and size.
Voluntary. Physician adoption of EMRs must be voluntary, not mandated or coerced.
Non-discriminatory. Programs designed to offset physicians’ costs or encourage them to adopt EMRs must be non-discriminatory (i.e., not tied to a single EMR product or health care practice model). While such restrictions may be attractive to some payors and administrators, they discriminate against physicians who do not meet their criteria and risk creating two “classes” of physicians and patients.
Outcome-related incentives. Incentives for EMR adoption should be tied to clinical benefits and outcomes, not driven by cost containment. Financial incentives or bonuses that are tied to clinical outcomes may encourage EMR utilization and optimize the use of these systems in ambulatory clinical practices. 1 For more detail on the physician’s ethical responsibilities as data steward of patient information please refer to the CMA Code of Ethics and Professionalism, Guiding Principles for the Optimal Use of Data Analytics by Physicians at the Point of Care, and Guiding Principles for Physicians Recommending Mobile Health Applications to Patients. Page 2 Financial
Unrestricted. Funding for EMRs in physician offices must be equally available to all physicians, and not restricted to a single EMR product or physician practice model.
Funding. Cost analyses have determined that the majority of the benefits from EMRs accrue to the health care system (i.e., payors and patients) and not to individual physicians. It is only reasonable that those who benefit most should assume the costs.
Comprehensive. The cost of implementing an EMR system goes beyond acquisition of hardware and software. Funding for physician adoption of EMRs must be comprehensive and include costs associated with the initial purchase, as well as implementation, change management, ongoing operation, and evergreening of the system.
Save harmless. Early adoptors who need to update or replace their existing systems, as well as physicians whose EMR vendor goes out of business, must not be disadvantaged. These physicians must not be penalized or excluded from funding programs, and should be provided with the necessary transition support. Business
Vendor sustainability. Vendor stability is critical to EMR adoption by physicians. This can be achieved through vendor compliance with technical and business requirements that address fiscal sustainability as well as EMR product quality, technical standards and capabilities.
Due diligence. Because physician practices vary in type, size and needs, there is no “one-size-fits-all” EMR solution. Physicians must assess the needs of their individual practice to determine the best product.
Workflow re-engineering. Implementation of EMRs in ambulatory clinical practice may require workflow adjustment or re-engineering. Assessments of workflow and practice needs must be part of EMR change management programs.
HR impact. Adoption of EMRs in ambulatory clinical practices will have an impact on human resources. Provision should be made for physician and office staff retraining, retention and turnover.
Support and service agreements. Physician use of EMRs in ambulatory clinical practice requires appropriate support and service agreements not only to provide the necessary infrastructure and connectivity, but also to guarantee ongoing, accessible and reliable technical support. Physicians must be able to access patient records in their EMR system at all times, regardless of where the records are physically stored (e.g., off-site with an alternate service provider, or onsite in a local client server).
Risk management strategies (liability and insurance) tied to EMR adoption must address the privacy, security, business continuity and professional liability requirements of physician practice in an electronic environment. Change management and transition
Critical to success. To fully realize the benefits from EMR adoption, the move from paper to electronic records requires change management support and services geared specifically to physician EMR adoption.
Ongoing. Change management is a key success factor in driving both uptake and optimal utilization of EMRs in ambulatory clinical practice. To realize the full benefits of EMR adoption on health care outcomes, physician change management programs must be ongoing, not one-time. Page 3
Comprehensive. Comprehensive change management for physicians who adopt EMRs must include the tools and services to assist with system needs assessment, EMR selection, implementation, workflow adjustment, and training for physicians and staff, as well as suggestions to maximize use of the EMR.
Physician driven and designed. Change management must meet the real and individual needs of physicians as they move to an EMR-based practice. This requires flexibility (not one-size-fits-all), “just in time” capacity and delivery, and a mechanism for evaluating the program.
Payor funded and delivered. Delivery and costs of these programs should be borne by payors as part of any physician EMR funding programs or agreements. Usability and human factors
User interface and usability. User interface and usability of EMR systems are critical success factors for physician acceptance and optimal utilization of EMRs in clinical practice.
Workflow. EMR adoption requires changes to physician workflow, such as history-taking and charting. Done properly, workflow changes related to EMRs should result in administrative efficiencies and improved clinical outcomes.
Core principles of practice must be respected. The EMR must allow the physician to practice comprehensive care, efficiently manage patients with multiple problems and respect the doctor-patient relationship where the patient’s values, wishes, advance directives and physical and social function are integral to medical care.
Training and education. Training in the use, benefits, shortcomings and opportunities of an EMR must become part of the medical education curricula in all stages of physician practice: undergraduate, postgraduate and continuing medical education.
Standardized data. Large data sets that record every observation are unworkable in practice. The EMR must allow the physician to record and access data in a standardized way.
Data quality. Data quality is critical to patient care. Physicians require access to accurate, clinically relevant data. Inaccurately recorded and unfiltered data does not benefit patient care. Clinical patient care
Management of patient records. EMR systems allow physicians to quickly access and manage patient data in an organized fashion (e.g., search, sort and retrieve data, spot trends, or flag charts). This leads to more efficient practices and enhances care delivery.
Referrals and patient summaries. The ability to transmit referral requests and reports electronically using an EMR greatly facilitates the consultation process. Core clinical data sets generated from the EMR can be used to share or hand off patient care among providers, facilitating both continuity of care and emergency access to relevant data.
Drugs and lab reports. Physician use of an EMR permits drug and lab data to be recorded and shared more accurately and efficiently. Benefits to patient care include automated prescription renewals, quick identification of patients affected by drug alerts, and collation of lab data to show trends.
Decision support. EMR adoption in ambulatory clinical practice makes clinical decision support (i.e., access to timely, appropriate, evidence-based information) possible at the point of care. This has the potential to enhance patient safety, care delivery and health outcomes. Page 4
Patient values and autonomy. Patient values and autonomy cannot become secondary to the "data management" requirements of the EMR. An EMR must provide the same (or better) standards of patient confidentiality as traditional paper-based records.
Accessibility. Patient data must always be collected and stored in an EMR with the primary goal of improving individual patient care. Data accessibility for clinical care is more important than compiling a large common data set. Health Research
Standardized data. Primary care is driven by symptoms, not diagnoses, and both must be recorded in the EMR in a standardized way.
Clinical coding. Primary care disorders are low-prevalence and will require a high degree of precision when data are coded.
Evidence-based care models. The episode-of-care data model demonstrates how symptoms and symptom clusters evolve over time. It is possible to derive the sensitivity and specificity of symptoms and symptom clusters to improve pre-test likelihood and avoid unproductive testing.
Core and aggregate data. Standardized data means that core data sets can be combined, and their aggregation allows identification and analysis of rarer conditions.
Documents
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Online continuing medical education

https://policybase.cma.ca/en/permalink/policy9892
Last Reviewed
2017-03-04
Date
2010-08-25
Topics
Population health/ health equity/ public health
Ethics and medical professionalism
Health information and e-health
Resolution
GC10-69
The Canadian Medical Association, in collaboration with provincial/territorial medical associations, calls on governments to ensure that the necessary technology is in place to guarantee that physicians in rural and remote locations have access to accredited online continuing medical education/continuing professional development.
Policy Type
Policy resolution
Last Reviewed
2017-03-04
Date
2010-08-25
Topics
Population health/ health equity/ public health
Ethics and medical professionalism
Health information and e-health
Resolution
GC10-69
The Canadian Medical Association, in collaboration with provincial/territorial medical associations, calls on governments to ensure that the necessary technology is in place to guarantee that physicians in rural and remote locations have access to accredited online continuing medical education/continuing professional development.
Text
The Canadian Medical Association, in collaboration with provincial/territorial medical associations, calls on governments to ensure that the necessary technology is in place to guarantee that physicians in rural and remote locations have access to accredited online continuing medical education/continuing professional development.
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E-health strategies

https://policybase.cma.ca/en/permalink/policy9908
Last Reviewed
2017-03-04
Date
2010-08-25
Topics
Health information and e-health
Health systems, system funding and performance
Resolution
GC10-90
The Canadian Medical Association will work with provincial/territorial medical associations to ensure investments made by the Canada Health Infoway are aligned with, and respect e-health strategies that are currently being implemented or developed within various jurisdictions.
Policy Type
Policy resolution
Last Reviewed
2017-03-04
Date
2010-08-25
Topics
Health information and e-health
Health systems, system funding and performance
Resolution
GC10-90
The Canadian Medical Association will work with provincial/territorial medical associations to ensure investments made by the Canada Health Infoway are aligned with, and respect e-health strategies that are currently being implemented or developed within various jurisdictions.
Text
The Canadian Medical Association will work with provincial/territorial medical associations to ensure investments made by the Canada Health Infoway are aligned with, and respect e-health strategies that are currently being implemented or developed within various jurisdictions.
Less detail
Last Reviewed
2017-03-04
Date
2010-08-25
Topics
Health information and e-health
Population health/ health equity/ public health
Physician practice/ compensation/ forms
Resolution
GC10-93
The Canadian Medical Association supports and will expedite research into the expansion of telemedicine and the utilization of emerging technologies, to directly link health care providers and patients.
Policy Type
Policy resolution
Last Reviewed
2017-03-04
Date
2010-08-25
Topics
Health information and e-health
Population health/ health equity/ public health
Physician practice/ compensation/ forms
Resolution
GC10-93
The Canadian Medical Association supports and will expedite research into the expansion of telemedicine and the utilization of emerging technologies, to directly link health care providers and patients.
Text
The Canadian Medical Association supports and will expedite research into the expansion of telemedicine and the utilization of emerging technologies, to directly link health care providers and patients.
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Medical records

https://policybase.cma.ca/en/permalink/policy9923
Last Reviewed
2017-03-04
Date
2010-08-25
Topics
Ethics and medical professionalism
Health care and patient safety
Health information and e-health
Resolution
GC10-106
The Canadian Medical Association will work with provincial/territorial medical associations and other stakeholders including patients to develop a national strategy for the long-term retention, retrieval and disposal of medical records.
Policy Type
Policy resolution
Last Reviewed
2017-03-04
Date
2010-08-25
Topics
Ethics and medical professionalism
Health care and patient safety
Health information and e-health
Resolution
GC10-106
The Canadian Medical Association will work with provincial/territorial medical associations and other stakeholders including patients to develop a national strategy for the long-term retention, retrieval and disposal of medical records.
Text
The Canadian Medical Association will work with provincial/territorial medical associations and other stakeholders including patients to develop a national strategy for the long-term retention, retrieval and disposal of medical records.
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Patients access to their electronic medical record

https://policybase.cma.ca/en/permalink/policy11924
Date
2016-08-24
Topics
Health information and e-health
Resolution
GC16-49
The Canadian Medical Association recommends that patients be able to access their electronic medical record and contribute information to it.
Policy Type
Policy resolution
Date
2016-08-24
Topics
Health information and e-health
Resolution
GC16-49
The Canadian Medical Association recommends that patients be able to access their electronic medical record and contribute information to it.
Text
The Canadian Medical Association recommends that patients be able to access their electronic medical record and contribute information to it.
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Funding criteria for any new electronic medical record initiative

https://policybase.cma.ca/en/permalink/policy11925
Date
2016-08-24
Topics
Health systems, system funding and performance
Health information and e-health
Resolution
GC16-50
The Canadian Medical Association recommends that funding criteria for any new electronic medical record initiative include the ability for patients to access and contribute to their record.
Policy Type
Policy resolution
Date
2016-08-24
Topics
Health systems, system funding and performance
Health information and e-health
Resolution
GC16-50
The Canadian Medical Association recommends that funding criteria for any new electronic medical record initiative include the ability for patients to access and contribute to their record.
Text
The Canadian Medical Association recommends that funding criteria for any new electronic medical record initiative include the ability for patients to access and contribute to their record.
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