CHAPTER 16
Health Information Technology and Health Policy
Allison Viola
In this chapter, you will learn how to
• Describe key issues in health information technology and health policy in the United States
• Explain the Precision Medicine Initiative and the impact on health information technology
• Identify the policies and challenges remaining to attain interoperability
• Discuss how financial incentives have changed the use of health information technology
• Describe the new privacy and consent management landscape
• Explain the changes in the ONC Health IT certification program
• Describe the Quality Data Model to enhance quality reporting
The Linkage Between Health Policy and Health IT: Why It’s Important
With increasing pressure to reduce healthcare costs and to improve patient health and the care experience, the U.S. healthcare system has used, and continues to use, technological advances to help achieve those goals. But with the opportunities technology can offer, there is also the need to develop a supporting framework of nationwide policies. Jumpstarting these efforts was the passage of the American Recovery and Reinvestment Act (ARRA) of 2009 that established the Centers for Medicare and Medicaid Services (CMS) Electronic Health Record (EHR) Incentive Program and codified the Office of the National Coordinator for Health Information Technology (ONC) to implement standards and certification requirements to support the meaningful use of EHRs. Regulations ensued that helped build the policy foundation from which thousands of eligible hospitals and eligible providers have adopted, implemented, and meaningfully used EHRs. Pushing the healthcare industry forward with the passage of federal, state, and local policies has accelerated the movement toward digitized records. With this foundation, there is increasing demand for data capture—structured and unstructured—to promote new programs and capabilities such as President Barack Obama’s Precision Medicine Initiative (PMI), which was launched in 2015, and computable privacy to electronically satisfy patient consent rights without the need for human intervention. New payment models have emerged with these technological abilities through the introduction of the Medicare Access and Children’s Health Insurance Program (CHIP) Reauthorization Act of 2015 (MACRA) that transitions the healthcare payment system from a fee-for-service model to one that rewards value-based service and improved outcomes.
All of these initiatives and more cannot be successful without health information transmitted from one provider to another that supports patient care and research. Interoperable health IT and health information policies have been implemented to enable the movement of data through privacy protections, as well as standards, governance, and certification requirements. Without these guardrails, the U.S. healthcare system cannot truly be a system.
Precision Medicine Initiative
The terms “personalized medicine” and “precision medicine” have been used interchangeably. For consistency, the more recent term “precision medicine” is used in this chapter. Over the last few years, precision medicine has captured the healthcare industry’s attention as it struggles to reduce medical costs, improve patient care, and achieve better health for populations. Due to the rising costs of pharmaceuticals, the rising prevalence of chronic diseases such as diabetes, cardiovascular disease, and obesity, and the increasing attention given to patient safety issues, precision medicine has risen to the top of our national debate on healthcare and finding solutions to these challenges. As stated in a White House press release from 2015, “As a result of our traditional ‘one-size-fits-all-approach [to healthcare],’ treatments can be very successful for some patients but not for others. This is changing with the emergence of precision medicine, an innovative approach to disease prevention and treatment that takes into account individual differences in people’s genes, environments, and lifestyles. Precision medicine gives clinicians tools to better understand the complex mechanisms underlying a patient’s health, disease, or condition, and to better predict which treatments will be most effective.”1
On January 20, 2015, during his State of the Union address, President Obama announced the PMI and called for $215 million in fiscal year 2016 to support this effort. More specifically, he sought $130 million in funding for the National Institutes of Health (NIH) to build a national, large-scale research group (cohort) composed of one million or more Americans who volunteer to participate in precision medicine research through the contribution of their data. The data collected will be quite diverse—including medical, genetic, metabolic and microorganism, environmental and lifestyle, patient-generated, and device-collected data.2 To conduct research and enable clinicians to tailor their treatments for individuals, this data must be readily available and portable to support the purposes described previously. This cohort, now called the All of Us Project, will enable researchers to analyze a wider range of diseases and allow for statistical determinations to associate between genetic and/or environmental exposures. The rich data from this cohort will enable researchers to
• Develop ways to measure risk for a range of diseases based on environmental exposures, genetic factors, and interactions between the two
• Identify the causes of individual differences in response to commonly used drugs (commonly referred to as pharmacogenomics)
• Discover biological markers that signal increased or decreased risk of developing common diseases
• Use mobile health (mHealth) technologies to correlate activity, physiological measures, and environmental exposures with health outcomes
• Develop new disease classifications and relationships
• Empower study participants with data and information to improve their own health
• Create a platform to enable trials of targeted therapies
The PMI consists of two main components: a near-term focus on cancers and a long-term focus on generating knowledge that is applicable to a wider range of health and diseases. To support these goals, the following federal agencies are working in concert to help push this initiative forward and implement the necessary components as described in the following list:
• The National Institutes of Health (NIH) is responsible for building the cohort program and collecting data from the one million or more U.S. volunteer participants. Specifically, the National Cancer Institute is working to expand cancer precision medicine clinical trials, examine drug resistance in cancer patients, develop new preclinical models, and establish a national cancer knowledge system.
• The Food and Drug Administration (FDA) is developing new regulatory approaches to evaluate next-generation genomic sequencing technologies. The agency launched precisionFDA in late 2015, a crowd-sourced, cloud-based platform where next-generation sequencing (NGS) software methods can be tested, developed, and validated.
• The Office for Civil Rights (OCR) is developing regulatory guidance and other tools to help individuals and Health Insurance Portability and Accountability Act (HIPAA) covered entities understand their rights to donate information for research.
• The Department of Veterans Affairs (VA) and Department of Defense (DoD) are collaborating to enroll veterans and active-duty men and women through the Million Veteran Program.2
The interoperability of health IT plays a critical role in developing a framework for the future, one in which data stored in disparate EHRs and other health IT solutions can be accessed, shared, and used in far greater ways than are being accomplished today. Once this is achieved, the ability to leverage the data as a resource—particularly for such data-intensive initiatives as PMI—is endless.
Interoperability
An emerging complex and technological healthcare system, such as what we’re experiencing now, requires the ability for patients, providers, hospitals, and other stakeholders within the health ecosystem to exchange health information and be able to use that information for informed decision-making about care delivery. Successful interoperability allows for healthcare IT systems to work together within and across organizational boundaries in order to advance effective delivery of healthcare to individuals and communities. Three levels of interoperability exist to achieve this goal:
• Foundational interoperability allows for data exchange from one healthcare IT system to another and does not require interpretation of the data.
• Structural interoperability defines the format of data exchanged where there is uniform movement of healthcare data from one system to another. The clinical or operational purpose remains the same as it moves throughout the system and has the ability to be interpreted at the data field level.
• Semantic interoperability at the highest level allows for two or more systems or elements to exchange information and use the information that has been exchanged. This level of interoperability leverages the structuring of the data exchange and the classification of the data so that the receiver has the ability to interpret the data.3
As a result of the CMS EHR Incentive Program implementation that encouraged the adoption, implementation, and use of healthcare IT, Congress enacted the Medicare Access and CHIP Reauthorization Act of 2015. Through this law, Congress declared it a national objective to achieve widespread exchange of health information through interoperable certified EHR technology nationwide by December 31, 2018.4 MACRA defines interoperability as “the ability of two or more health information systems or components to exchange clinical and other information and to use the information that has been exchanged using common standards as to provide access to longitudinal information for health care providers in order to facilitate coordinated care and improved patient outcomes.”4 As a result of this legislation, CMS has issued proposed regulations to implement the requirements outlined in the law provisions.
New Payment Models
Since 1997, when the Medicare Sustainable Growth Rate (SGR) was designed and subsequently implemented to rein in Medicare Part B spending, Congress has continually passed legislation to prevent the inevitable payment cuts to providers. Despite these efforts, a permanent solution to this risk had not been developed until the passage of MACRA, which repeals the SGR. Not only does this legislation provide assurance, it also makes significant changes in the way Medicare issues payment to providers, by transitioning away from the “fee-for-service or quantity” model to one that supports value and the care rendered to patients. Payments will now be “value based,” thus supporting the quality of care.5
In preparation for this transition toward “value” over “volume,” earlier in 2015 Health and Human Services (HHS) Secretary Sylvia Burwell outlined new payment goals that center on alternative payment models (APMs). APMs emphasize improved patient outcomes and thus require provider accountability for the quality and cost of care patients receive. The following list provides some examples of improving outcomes and how APMs work hand in hand to support the U.S. healthcare system:
• Bundled payment model Providers are reimbursed together for the entire cost of an “episode of care.” An example is a hip replacement where the lab tests and other services are all paid for in the same lump sum—whether those tests or services are conducted once, twice, or five times. This circumstance creates an incentive to deliver better care that makes patients healthier, reduces duplicate or redundant unnecessary services, and lowers readmission rates.
• Accountable-care organizations (ACOs) Within an ACO, providers partner together on a patient’s care and are rewarded when they are able to deliver better care at lower cost. In a patient-centered medical home (PCMH) model, instead of doctors working individually, care coordinators oversee all the care a patient receives. The results of this collaborative effort mean patients are more likely to receive the right tests and medications rather than getting duplicative tests and procedures. The PCMH model typically offers patients access to a doctor or other clinician 7 days a week, 24 hours a day, including through extended office hours on evenings and weekends.
In her announcement, Secretary Burwell outlined the department’s goals and timeline in shifting away from volume and toward value:
• The first goal is for 30 percent of all Medicare provider payments to be in APMs that are tied to how good providers care for their patients, instead of how much care they provide—and to do it by 2016. The goal would then be to get to 50 percent by 2018.
• The second goal is for virtually all Medicare fee-for-service payments to be tied to quality and value; at least 85 percent in 2016 and 90 percent in 2018.
On May 9, 2016, CMS issued a proposed rule that repeals the SGR methodology for updates to the physician fee schedule (PFS) and substantially changes the way Medicare-enrolled practitioners are reimbursed (hospitals are currently not included in this program). This new proposal, Merit-based Incentive Payment System (MIPS), aims to streamline three existing programs, the Physician Quality Reporting System (PQRS), the Physician Value-based Payment Modifier (VM), and the Medicare EHR Incentive Program for Eligible Professionals (EPs). To avoid program and reporting duplication and burden for providers, this program will establish a new framework for rewarding quality reporting, resource use, and use of certified EHR technology (CEHRT) accomplished via a unified approach called the Quality Payment Program that will transition over a period of time from 2015 through 2021 and beyond and allow participants to choose from two pathways—MIPS and the Advanced APMs. (The EHR Incentive Program will evolve into the Advancing Care Information (ACI) component of the MIPS program.)6
The MIPS program reimbursement is based upon a Composite Performance Score (CPS) that is divided among the following four weighted categories and, based upon the score achieved, will determine the payment that clinicians receive. Under the proposed rule, the first performance period for MIPS will begin on January 1, 2017, and run throughout the calendar year, with the first MIPS payment period beginning in 2019.
• Quality Eligible clinicians will report a selection of six quality measures with an emphasis on outcome measurement.
• Resource Use CMS will assess all available resource use measures, which will be based upon claims data and thus will require no reporting from clinicians.
• Clinical Practice Improvement Activity As a new category not currently part of the existing reporting programs, this includes care coordination, shared decision making, safety checklists, and expanding practice access.
• Advancing Care Information (ACI) MACRA restructures the concept of “meaningful use of certified EHR technology” into the ACI category and requires the selection of current quality reporting measures adopted by the Stage 3 Meaningful Use program, from six objectives: protecting patient health information, e-prescribing, patient electronic access, coordinated care through patient engagement, health information exchange, and public health reporting. The EHR Incentive Program (“Meaningful Use”) is not being withdrawn; rather, it is being integrated into the MIPS program to improve efficiencies and value.
Computable Privacy
As individuals’ information continues to become more digitized—with the enactment of the CMS EHR Incentive Program—so does the increased risk of a breach. HIPAA was passed in 1996 to address the use and disclosure of individuals’ health information—called protected health information (PHI). This was the first time a set of national standards was established to protect certain health information.7 Since that time, technology has advanced to the point where it enables the automation of privacy compliance requirements. This concept is referred to as computable privacy, which is the “technical representation and communication of permission to share and use identifiable health information, including when law and applicable organizational policies enable information to be shared without need to first seek an individual’s permission. Once integrated effectively, using technology for privacy compliance saves time and resources, and can build trust and confidence in the system overall.”8
To support this effort, three essential layers of computable privacy must be implemented. HIPAA serves as the initial foundation allowing for disclosure of health information for the necessary treatment, payment, and operations (TPO). It also establishes the nationwide framework for these uses regardless of state requirements and laws. The second layer, basic choice, refers to the choice an individual makes about the use and disclosure of their health information, including the electronic exchange of health information regardless of the default rules such as HIPAA. The third, more detailed layer is a patient’s granular choice. At this level, an individual has choices regarding the distinction between legally sensitive clinical conditions, such as mental health or HIV/AIDS status, and allows for those choices to adjust over time to support decisions regarding the disclosure of this information to specific recipients within the healthcare system.8
Despite the technological advances in capturing and storing health information within an EHR and other healthcare IT solutions, compliance challenges continue to exist due to a variety of factors, one of which is varying state privacy laws and the ability to exchange information across state lines. Currently there are conflicting privacy and confidentiality laws, regulations, and policies that serve as an obstacle in implementation of electronic consent management, or computable privacy. These laws, regulations, and policies address consent rights and categories of information that are considered sensitive and may apply at different levels. These variations may create a complex environment based upon where the patient and provider are located, as the requirements may be in conflict. As a result of these challenges, software vendors and system developers must create more flexible systems that can support conflicting and changing privacy and confidentiality laws and requirements.
Other notable challenges exist that hinder the ability to deploy a comprehensive and common computable privacy program: the lack of structured data in patient consent forms and the lack of interoperability between healthcare IT systems. The current method most commonly used for collecting patient consent is through paper forms. Although these forms may be converted into a PDF image file, users are unable to capture discrete data elements and allow for machine-readable capabilities. Scanned consent forms do not contain structured electronic data, which is data that can be tagged or occupies searchable fields (e.g., name or address fields).9 To process consent decisions electronically, the data must be captured, tagged, and stored to allow for automated processing. As an example of this, should a patient choose to not disclose information regarding a diagnosis or other sensitive information, these data elements must be identified by the healthcare IT systems as such and be prevented from being shared with specified individuals.
The current consent management landscape can be separated into three distinct components and maturity levels:9
• Phase I, Current State Consent is captured on paper forms and is then scanned and stored into a healthcare IT system. There is no structured data in this process, so the information must be reviewed and analyzed by staff to determine the level of desired consent.
• Phase II, Current Growth This phase is experiencing both paper collected consent and then entered into the system manually as well as consent captured electronically from the beginning. Although this demonstrates an automated approach, there is little flexibility in preferences.
• Phase III, Future State Consent is collected electronically and structured data are collected through a standard process. This phase allows for healthcare IT to automatically interpret and process consent preferences as well as comply with all applicable laws and regulations. Granular decisions may be applied to certain data elements, such as mental health or HIV status, which are prevented from being shared with other providers. Although Phase III is not widely implemented, pilot programs have been testing this approach and have successfully demonstrated this capability through electronic means.
EHR Incentive and Certification Programs
The American Recovery and Reinvestment Act of 2009 (ARRA) was enacted on February 17, 2009, and subsequently implemented by CMS in 2011.10 This program was created to incentivize eligible professionals (EPs), eligible hospitals, critical access hospitals (CAHs), and Medicare Advantage organizations to adopt and meaningfully use interoperable healthcare IT and qualified EHRs. The program was divided among three stages for providers, hospitals, and healthcare IT vendors and other stakeholders to prepare and meet the requirements as defined in the regulation.
Since 2011, there have been several regulatory modifications to the program and the industry is now preparing for and/or implementing stages 2 and 3. As of this writing, the OCR reports:11
• As of 2015, 95 percent of all eligible and critical access hospitals have demonstrated meaningful use of CEHRT through participation in CMS EHR Incentive Programs. Ninety-eight percent of all hospitals have demonstrated meaningful use and/or adopted, implemented, or upgraded (AIU) an EHR.
• As of the end of 2015, 56 percent of all U.S. office-based physicians (MD/DO) have demonstrated meaningful use of CEHRT in the CMS EHR Incentive Programs.
Some additional changes to the certification program include the publication of the 2015 Edition Health Information Technology (Health IT) Certification Criteria, 2015 Edition Base Electronic Health Record (EHR) Definition, and ONC Health IT Certification Program Modifications final rule. In this rule, ONC made changes to the certification program that strengthen the testing, certification, and surveillance of healthcare IT. ONC also clarified and expanded the responsibilities of ONC-Authorized Certification Bodies (ONC-ACBs) regarding surveillance of certified EHR technology and other certified healthcare IT under the program, requiring ONC-ACBs to conduct more frequent and more rigorous surveillance in the field. ONC-ACBs are entities that have received authorization from ONC to participate in the ONC Health IT Certification Program and make certification determinations for healthcare IT modules based upon test results that have been supplied by Accredited Testing Laboratories.7, 9, 12, 13, 14
To further expand and define its role with certification, ONC published a proposed rule, “ONC Health IT Certification Program: Enhanced Oversight and Accountability” on March 2, 2016, which proposes to expand ONC’s role to strengthen its oversight authority to directly review and evaluate the performance of certified healthcare IT in certain circumstances, such as in response to issues that could pose a public health or safety problem, compromise the security or privacy of patients’ health information, or other exigencies.15
Quality Measures
As both the digitization of health information and the adoption, implementation, and use of EHRs increase, so does the emphasis and importance placed on the value of care delivery. This is evidenced by the passage of MACRA in 2015 followed up by the MIPS/APM Incentive Payment proposed rule in 2016. Traditionally, quality measures have been reported through claims (administrative) data that are submitted to CMS for payment purposes and have not typically been clinical data. With the increased ability of EHRs to capture, store, and report clinical and administrative data, reporting quality measures has become less complex by allowing for a more consistent and standard way of reporting data to meet quality measurement requirements.
To improve and support the transition of quality measurement reporting from a claims-based process to one that is driven by clinical data collected in EHRs, the need to establish a model framework was evident. Therefore, based upon a request from ONC, the Agency for Healthcare Research and Quality (AHRQ) funded the development of a Quality Data Model (QDM). The QDM is a model of information that allows for the description of clinical concepts in a standardized format so that stakeholders who monitor clinical performance and outcomes can clearly and concisely communicate information and reduce ambiguity within performance measurement.16
The QDM also describes information in a way that enables healthcare IT vendors to interpret the data in a consistent manner and locate the required information within the healthcare IT system so that users of electronic health information have a mutual understanding. Currently, the QDM is used in over 90 measures within the EHR Incentive Program and other quality reporting programs that require electronic measure (eMeasures) reporting.
Recently, a new generation of standards framework has emerged that can easily be assembled, implemented, and used within a wider variety of contexts—mobile phone applications, cloud communications, EHR-based data sharing, and more.17 Fast Healthcare Interoperability Resources (FHIR-pronounced “FIRE”) was developed by Health Level 7 International (HL7), a not-for-profit, standards-developing organization dedicated to providing a comprehensive framework and related standards for the exchange, integration, sharing, and retrieval of electronic health information that supports clinical practice and the management, delivery, and evaluation of health services.18 FHIR is currently a Draft Standard for Trial Use, but trial use has already begun because of the many advantages it offers. With the development of FHIR and the opportunities it provides for flexibility within healthcare IT, particularly the quality measurement realm, this new framework has been in place to align it with the QDM. Stakeholders had already begun to work with the QDM to reduce its complexity by merging the QDM with Virtual Medical Record for Clinical Decision Support (vMR-CDS) to give rise to the Quality Information and Clinical Knowledge (QUICK) model. This model was then further refined to align structurally and semantically as close as possible with FHIR to allow for common quality and interoperability but will also support future quality initiatives.19
Chapter Review
The U.S. healthcare industry is undergoing a profound transformation, particularly within healthcare IT, that will challenge policy makers, industry professionals, and other stakeholders to rethink the way clinical and business solutions are provided. The PMI is just the beginning in seeking cures for patient diseases, interoperability of technology and information, research, pharmacogenomics, privacy and security challenges, mobile health, and others. The success of this program requires cross-collaboration and engagement with a variety of federal agencies and other stakeholders to push this initiative forward and allow for the unprecedented collection and use of genomic data to improve health and healthcare. Despite this initiative, interoperability of technology and health information continues to present challenges, and much work is still needed in this area to support the PMI, new payment models, and other initiatives that require the collection, aggregation, and use of data. By implementing such programs and requirements as the ones announced by Secretary Burwell, CMS will be obliged to comply and thus push the industry forward on the interoperability journey.
Computable privacy has emerged as a solution to address increased privacy and security concerns and supporting legislation as our health information becomes more digitized. However, as our electronic data increases, so does the potential for breaches and other unauthorized access to that data. Therefore, the ability to drill down to the data element level and protect it permits more flexibility and peace of mind, particularly with sensitive data. Not only has data protection changed, but also providers are experiencing an improved process in quality data reporting with the integration and streamlining of quality reporting programs proposed by CMS. After years of prompting by stakeholders to align and harmonize quality reporting programs to reduce their burden, CMS, through the MIPS program, has enhanced reporting.
Questions
1. The Precision Medicine Initiative cohort that is composed of one million or more Americans will provide researchers with the ability to do which of the following?
A. Develop new disease classifications and relationships
B. Identify the causes of individual differences in response to commonly used drugs
C. Empower study participants with data and information to improve their own health
D. All of the above
2. What are the three levels of interoperability?
A. Structural, pseudonym, and biological
B. Foundational, structural, and semantic
C. Semantic, workflow, and classification
D. Technological, structural, and foundational
3. What did the Medicare Access and CHIP Reauthorization Act (MACRA) of 2015 repeal?
A. The Sustainable Growth Rate (SGR) Model
B. The EHR Incentive Model
C. The Physician Quality Reporting System
D. None of the above
4. Which three programs does the Merit-based Incentive Payment System aim to streamline to substantially change the way practitioners are reimbursed?
A. Physician Quality Reporting System (PQRS), Physician Vendor-based Payment Modifier (VM), and Medicare EHR Incentive Program for Eligible Practitioners (EPs)
B. Physician Quality Reporting System (PQRS), Physician Value-based Payment Modifier (VM), and Medicare EHR Incentive Program for Eligible Professionals (EPs)
C. Physician Querying Release System (PQRS), Physician Value-based Payment Mediator (VM), and Medicaid EHR Incentive Program for Eligible Professionals (EPs)
5. Which of the following lists two examples of alternative payment models?
A. Bundled payment model and synchronized payment model
B. Bundled payment model and integrated payment model
C. Integrated payment model and accountable-care organizations
D. Bundled payment model and accountable-care organizations
6. The current consent management landscape is separated into which of the following three components and maturity levels?
A. Phase I, Current State: Consent is captured on paper forms.
Phase II, Current Growth: This phase is experiencing both paper collected manually as well as consent captured electronically from the beginning.
Phase III, Future State: Consent is collected electronically and structured data are collected through a nonstandard process.
B. Phase I, Current State: Consent is captured on paper forms.
Phase II, Current Growth: This phase is experiencing both paper collected consent and then entered into the system manually.
Phase III, Future State: Consent is collected electronically and structured data are collected through a standard process.
C. Phase I, Current State: Consent is captured on paper forms and is then scanned and stored into a healthcare IT system.
Phase II, Current Growth: This phase is experiencing both paper collected consent and then entered into the system manually as well as consent captured electronically from the beginning.
Phase III, Future State: Consent is collected electronically and structured data are collected through a standard process.
D. Phase I, Current State: Consent is captured on paper forms and not scanned and stored into a healthcare IT system.
Phase II, Current Growth: This phase is experiencing both paper collected consent and then entered into the system manually.
Phase III, Future State: Consent is collected electronically and structured data are collected through a nonstandard process.
7. FHIR stands for:
A. Fast Healthcare Interoperability Reasons
B. Fast Healthcare Interactive Resources
C. Frequent Health Integrated Resources
D. Fast Healthcare Interoperability Resources
8. The Quality Information and Clinical Knowledge (QUICK) model is composed of:
A. Merging the Quality Data Model (QDM) with Virtual Medical Record for Clinical Decision Support (vMR)
B. Merging the Query Decision Model (QDM) with Virtual Medical Record for Clinical Decision Support (vMR)
C. Merging the Quality Data Model (QDM) with Virtual Medical Record for Clinical Quality Decisions (vMR)
Answers
1. D. The PMI cohort of one million or more Americans will provide researchers with the ability to develop new disease classifications and relationships, identify the causes of individual differences in response to commonly used drugs, and empower study participants with data and information to improve their own health.
2. B. The three levels of interoperability are foundational, structural, and semantic.
3. A. The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) repealed the Sustainable Growth Rate (SGR) model.
4. B. The Merit-based Incentive Payment System (MIPS) aims to streamline Physician Quality Reporting System (PQRS), Physician Value-based Payment Modifier (VM), and the Medicare EHR Incentive Program for Eligible Professionals (EPs) to substantially change the way practitioners are reimbursed.
5. D. Examples of alternative payment models are the bundled payment model and accountable-care organizations.
6. C. The current consent management landscape is separated into the following levels of maturity: Phase I Current State: Consent is captured on paper forms and is then scanned and stored into a healthcare IT system. Phase II Current Growth: This phase is experiencing both paper collected consent and then entered into the system manually as well as consent captured electronically from the beginning. Phase III Future State: Consent is collected electronically and structured data are collected through a standard process.
7. D. FHIR stands for Fast Healthcare Interoperability Resources.
8. A. The Quality Information and Clinical Knowledge (QUICK) model is composed of merging the Quality Data Model (QDM) with Virtual Medical Record for Clinical Decision Support (vMR).
References
1. The White House Office of the Press Secretary. (2015). Fact sheet: President Obama’s Precision Medicine Initiative. Accessed from www.whitehouse.gov/the-press-office/2015/01/30/fact-sheet-president-obama-s-precision-medicine-initiative.
2. The White House. (n.d.). What is the Precision Medicine Initiative? In The Precision Medicine Initiative. Accessed from www.whitehouse.gov/precision-medicine.
3. HIMSS. (n.d.). What is interoperability? Accessed from www.himss.org/library/interoperability-standards/what-is-interoperability.
4. Public Law 114-10, 114th Congress. Medicare Access and CHIP Reauthorization Act of 2015. Accessed from www.congress.gov/114/plaws/publ10/PLAW-114publ10.pdf.
5. Cragun, E. (2015, April 20). The most important details in the SGR repeal law. The Advisory Board Company. Accessed on October 4, 2016, from www.advisory.com/research/health-care-advisory-board/blogs/at-the-helm/2015/04/sgr-repeal.
6. Merit-Based Incentive Payment System (MIPS) and Alternative Payment Model (APM) Incentive Under the Physician Fee Schedule, and Criteria for Physician-Focused Payment Models Proposed Rule. (2016, May 9). Accessed from https://www.federalregister.gov/documents/2016/05/09/2016-10032/medicare-program-merit-based-incentive-payment-system-mips-and-alternative-payment-model-apm.
7. U.S. Department of Health and Human Services. (n.d.) Summary of the HIPAA Privacy Rule. Accessed on October 4, 2016, from www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/.
8. Precision Medicine Task Force. (2016, Feb. 26). Task force meeting [PowerPoint slides]. Accessed on October 4, 2016, from www.healthit.gov/FACAS/sites/faca/files/PMTF_Meeting_Slides_2016-02-26.pptx.
9. MITRE Corporation. (2014, Oct. 29). Electronic consent management: Landscape assessment, challenges, and technology. (Prepared for the ONC Office of the Chief Privacy Officer.) Accessed on October 4, 2016, from www.healthit.gov/sites/default/files/privacy-security/ecm_finalreport_forrelease62415.pdf.
10. Centers for Medicare and Medicaid Services. (n.d.). Electronic health records (EHR) incentive programs. Accessed on October 4, 2016, from www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/index.html?redirect=/ehrincentiveprograms/.
11. Office of the National Coordinator for Health Information Technology (ONC). (2016). Quick stats. Health IT Dashboard. Accessed on October 4, 2016, from http://dashboard.healthit.gov/quickstats/quickstats.php.
12. Merit-Based Incentive Payment System (MIPS) and Alternative Payment Model (APM) Incentive Under the Physician Fee Schedule, and Criteria for Physician-Focused Payment Models; Proposed Rule, 81 Fed. Reg. 28161 (May 9, 2016) (to be codified at 42 CFR pts. 414 & 495). Accessed on October 4, 2016, from www.federalregister.gov/documents/2016/05/09/2016-10032/medicare-program-merit-based-incentive-payment-system-mips-and-alternative-payment-model-apm.
13. ONC. (n.d.). ONC Health IT certification program. Accessed on October 4, 2016, from www.healthit.gov/policy-researchers-implementers/about-onc-health-it-certification-program.
14. ONC. (n.d.). Health IT dashboard. Accessed on October 4, 2016, from http://dashboard.healthit.gov/index.php.
15. National Quality Forum. (n.d.). QDM and vMR harmonization. Accessed on October 4, 2016, from https://www.healthit.gov/archive/archive_files/.../qdm_vmr_harmonization.pptx.
16. National Quality Forum. (n.d.). Quality Data Model (QDM): Technical questions and answers. Accessed on October 4, 2016, from www.qualityforum.org/Projects/n-r/Quality_Data_Model/Quality_Data_Model_(QDM)__Technical_Questions_and_Answers.aspx.
17. Health Level Seven International. (2015). Introducing HL7 FHIR. Accessed on October 4, 2016, from www.hl7.org/fhir/summary.html.
18. HL7 International. (n.d.). About HL7 International. Accessed from www.hl7.org/.
19. Slabodkin, G. (2016, May 19). CMS says meaningful use will live on in MACRA. Health Data Management. Accessed on October 4, 2016, from www.healthdatamanagement.com/news/cms-says-meaningful-use-will-live-on-in-macra.