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 Table of Contents  
Year : 2016  |  Volume : 2  |  Issue : 3  |  Page : 129-133

Erratum: A commentary on digital medicine innovation and alignment to evidenced-based clinical measures

Date of Web Publication24-Nov-2016

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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2226-8561.198413

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How to cite this article:
. Erratum: A commentary on digital medicine innovation and alignment to evidenced-based clinical measures. Digit Med 2016;2:129-33

How to cite this URL:
. Erratum: A commentary on digital medicine innovation and alignment to evidenced-based clinical measures. Digit Med [serial online] 2016 [cited 2023 Mar 24];2:129-33. Available from: http://www.digitmedicine.com/text.asp?2016/2/3/129/198413

Correction to: Digital Medicine online publication, November 24, 2016; DOI: 10.4103/2226-8561.194689.

Due to a publisher error, a number of author corrections were not made to the article prior to its publication; the publisher wishes to apologies to all concerned. The corrected version of the article appears in full below


A commentary on digital medicine innovation and alignment to evidenced-based clinical measures

Kimberly Harding*

President of Monarch Innovation Partners Inc., Rockville, MD, USA

*Address for correspondence:

Kimberly Harding, Monarch Innovation Partners, Rockville, MD, USA.

E.mail: [email protected]

  Background Top

The next generation of digital medicine capabilities such as technology that support regenerative and precision medicine, diagnostic, therapeutic and theranostic nanomedicine, healthcare sensor technology, and three-dimensional medical printing, have ushered in a new patient engagement and treatment healthcare ecosystem that requires a new perspective in the area of evidenced-based outcomes from a triple aim perspective - affordability, healthcare quality, and patient satisfaction.

The evolutionary model of disruptive technology that involves human-to-human, human-to-machine, and machine-to-human interactions has a classic arch of adoption, and digital medicine is no exception.

However, there is a multi-dimensional factor that digital medicine innovators must remain cognizant of in their quest for meaningful breakthroughs that deliver sustainable healthcare outcomes in order for the art and science of digital medicine to progress as a reliable model for diagnostic testing, biosurveillance, and treatment delivery. This involves the quantifiable and qualitative alignment of digital medicine's direct contribution to measurable improvements to the triple aim in a learning health system.

  Learning Health System Framework Top

The Institute of Medicine's (IOM) definition of a learning health system, is one of the most widely known and cited frameworks that ties health care policy, health information exchange and knowledge sharing, as well the human capital and technological infrastructure that supports it, as the core attributes of a mature healthcare delivery network. Specifically, IOM describes three key components of a learning health system in the following manner:[1]
"In order to achieve a continuously learning healthcare system that provides the best care at lower cost healthcare stakeholders must (1) manage rapidly increasing complexity; (2) achieve greater value in health care; and (3) capture opportunities from technology, industry, and policy."

According to the IOM, healthcare delivery stakeholders and their suppliers need to focus on the following areas as part of the culture of a learning health system:[2]

  • Adaptation to the pace of change
  • Stronger synchronicity of efforts
  • New clinical research paradigm
  • Clinical decision supports systems
  • Tools for database linkage, mining, and use
  • Notion of clinical data as a public good
  • Incentives aligned for practice-based evidence
  • Public engagement
  • Trusted scientific broker
  • Leadership

An example of the interpretation of this framework was the US Department of Health and Human Services strategy to develop a 10-year Healthcare Interoperability Roadmap, from the Office the National Coordinator. They pictorially defined how the principles of a learning health system directly enable healthcare stakeholders (patients, providers, public health specialist, clinical researchers and payers) the ability to obtain the triple aim [Figure 1].
Figure 1: Learning health system. Source: US Department of Health and Human Services Office of the National Coordinator. No Claim to Orig. US Govt. Works

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  Current Digital Medicine Solutions Successfully Aligned to Evidenced -based Clinical Measures Top

If we look at the current portfolio of digital medicine capabilities, we see a strong trend of aligning the use of mobile phone alerts, electronic-prescribing, mobile body area networks (MBANs) for remote health monitoring and biosurveillance (i.e., active and passive sensor technology), and telehealth solutions at the forefront of digital medicine solutions meeting critical patient engagement outcomes for wellness, care, and disease management efforts. An example is the use of electronic prescribing (e-prescribing) and remote surveillance of medication compliance as a link to clinical measures associated with medication adherence. HealthIT.gov collected the outcomes of providers who used Electronic Health Record (EHR) systems for electronic prescribing and patient's behavioral response. Their summarized findings of the use of EHR prompts and reminders to improve quality of patient care are the following:[3]

  • 92% were happy their doctor used electronic -prescribing
  • 90% reported rarely or only occasionally going to the pharmacy and having prescription not ready
  • 76% reported it made obtaining medications easier
  • 63% reported fewer medication errors.

Innovators within digital medicine must continue this trend as part of their overall vision and mission for effective product design and development or run the high risk of poor provider adoption and compromised quality and patient safety in the delivery of care.

  Aligning Digital Medicine with New Performance-based Care Models Top

Learning from the successful outcomes from the current wave in digital health solutions (i.e. mobile health apps, telehealth, MBANs) will pave the way for the next generation of digital health solutions directly impacting evidenced-based outcomes. However, the criteria for covering these newly digitized methods of care delivery require new and adaptable healthcare policies and machine executable clinical quality logic. As a result, data-level semantic models associated with emerging digital medicine offerings will need to be harmonized with clinical quality ontologies, such as standardized clinical measures. If harmonization can be established, it will bridge the gap of aligning new digital medicine offerings to the virtues of the triple aim within a patient encounter. This will be essential for performance-based care models and the standards of care used as benchmarks for healthcare administration and financing.

An essential goal of new digital methods that provide diagnostic services, healthcare monitoring and therapeutic care is to be integrated into evidenced-based clinical workflows, in order to support performance-based provider models. A specific case would be performance-based care models utilizing standardized clinical quality measures such as Accountable Care Organizations (ACOs).[4] The concept of an ACO provider model and similar care model designs, align to the principles of the triple aim. Provider organizations are incentivized by healthcare agencies, and private and government-based payers to adopt as much of these tenets as possible, to achieve organizational goals for the triple aim in the western world. These performance-based care delivery models consist of specific terms and conditions associated with the type and level of care that healthcare providers deliver, for risk-adjusted population groups. Employer groups and healthcare payer organizations that fund the benefits for care delivery can quantifiable tie, actuarially, predictable clinical quality, and optimized administrative and financial savings for the cost of care.

For example, the National Committee for Quality Assurance (NCQA) and Healthcare Effectiveness Data Information Set (HEDIS) is one of the leading clinical outcomes measure set adopted across US-based healthcare provider and payer organizations for performance-based programs designed to achieve the triple aim.

An abbreviated list of the provider-based healthcare quality measures that HEDIS provides for ACO certification encompass the following clinical care domains [Table 1].[5]
Table 1: Accountable Care Organizations abbreviated table of clinical measures

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NCQA has collaborated with healthcare stakeholders to develop and establish mature semantic models tied to care and disease management clinical rule logic of the HEDIS measure set to the medical approaches and devices used to verify that the practitioner is in the compliance to the tenets of the measure. These semantic models and executable clinical logic have evolved within the past two decades. As a result, health information technology data standards and metadata attributes build in EHR systems are optimized to meet these measure sets as part of the patient encounter experience. NCQA also publishes HEDIS measure technical specifications to enable technology-based systems that support the collection of data to verify compliance to the measures possible.[6] However, these measure frameworks, like NCQA-HEDIS, are heavily reliant on highly predictable methods of care delivery across care settings and techniques.

  Integrated Health Enterprise Healthcare Domains for Clinical Quality Efforts - A Method for Bridging The Next Generation of Digital Medicine to Clinical Quality Measures Top

Given the progress made with EHR and e-prescribing alignment to clinical measures, digital medicine innovators can proactively align product design to a health information technology framework, with proven success within clinical care settings worldwide for electronic medical records and medical devices - Integrated Health Enterprise (IHE). IHE is an international standards organization that provides a technology framework specifically designed to provide architectural guidelines and specifications on how to integrate health information technology and data exchange methods within the clinical setting. IHE is organized by clinical and operational domains. In each domain, users with clinical and operational experience identify integration and information sharing priorities and vendors of relevant information systems develop consensus, standards-based solutions to address them.[7] Systems developed in accordance with IHE communicate with one another better, are easier to implement, and enable care providers to use information more effectively.[8] IHE national deployment committees have been established in 17 countries across the globe. They are sanctioned by IHE International to conduct testing, education, outreach, collaboration with local health agencies, and other deployment-related activities.[9]

IHE has established a domain focused specifically on clinical quality initiatives entitled the IHE Quality, Research and Public Health Profiles. Below is a description from IHE that entails the scope of this domain:[10]

  • Birth and fetal death reporting (BFDR) describes the content and format to be used within the prepopulation data part of the Retrieve Form Request transaction from the RFD
  • Birth and fetal death reporting-enhanced (BFDR-E) profile provides a means to capture and communicate information needed to report births and fetal deaths for vital registration purposes. BFDR-E-builds on the earlier BFDR profile that utilizes actors and transactions defined in the Information Technology Infrastructure (ITI) Retrieve Form for Data Capture (RFD) profile to capture structured data using digital forms
  • Clinical research document describes content pertinent to the clinical research use case required within the ITI RFD prepopulation parameter
  • Clinical Research Process Content (CRPC) specifies content, which is appropriate to help automate the sharing of information among systems during the clinical research process using the transactions from the Retrieve Process for Execution (RPE) profile. Using the transactions from the RPE profile, CRPC will improve the recruitment for, setup, and performance of clinical studies
  • Data Element Exchange leverages the concept of a metadata registry to add mapping metadata to an annotated data capture form at the point of form design instead of the exchange of data instances
  • Drug Safety Content describes content pertinent to the drug safety use case required within the ITI RFD prepopulation parameter
  • Early Hearing Care Plan (EHCP) assists with the early detection, documentation of and intervention for hearing loss by enabling electronic communication of care plan content and instructions available to all authorized providers of care as jurisdictionally directed by the Public Health Early Hearing Detection and Intervention (EHDI) Program
  • EHDI defines a new Hearing Plan of Care document which replaces the earlier EHCP and defines the message content for a hearing screening device to communicate results to a receiving system
  • Family Planning (FP) describes the use of ITI's RFD to report encounter-level FP clinical visits. The profile describes the basic data elements that are needed to report quality metrics, a new FP prepopulate document in alignment with Patient Care Coordination (PCC) conventions, and maps the data elements to Clinical Data Architecture (CDA) (consisting of the Header, Pregnancy History, Pregnancy Status Review, Coded Vital Signs, Coded Social History, Coded Care Plan, and Coded Results sections)
  • Healthy weight (HW) captures and communicates information for managing and monitoring HW among clinical and public health surveillance systems
  • Maternal child health-birth and fetal death reporting define the EHR content that may be used to prepopulate and transmit birth and fetal death information to vital records systems for vital registration purposes
  • Newborn Admission Notification Information describes the content needed to communicate a timely newborn admission notification to public health to be used by EHDI screening programs
  • Physician Reporting to a Public Health Repository-Cancer Registry defines the data elements to be retrieved from the EMR and transmitted to the cancer registry or a healthcare provider
  • Quality Measure Execution-Early Hearing describes the content needed to communicate patient-level data to electronically monitor the performance of EHDI initiatives for newborns and young children
  • Redaction services profile (RSP) redacts data from a document in a user's current application to meet the requirements for exporting to an external system
  • Research matching publishes research process definitions to EHR systems to match patients and investigators with appropriate research studies
  • Retrieve Protocol for Execution (RPE) enables a healthcare provider to access a process definition, such as a research protocol and to execute automated activities, without leaving an EMR session
  • Structured data capture (SDC) profile utilized the IHE RFD profile for retrieving and submitting forms in a standardized and structured format. This supplement is based on the work of the US Office of the National Coordinator for Health Information Technology, Standards, and Interoperability Framework SDC Initiative
  • Vital records death reporting defines an RFD content profile that will specify derivation of source content from a medical summary document, by defining requirements for form filler content and form manager handling of the content.

In addition to clinical profile domains that IHE provides with the help of the healthcare community, they also provide a framework for implementing these profiles. Digital Medicine innovators can align and adapt their architecture designs and system testing approaches to standards that are resident in current systems for a higher success rate in system integration within the clinical care setting. In addition, the IHE community sponsors international demonstration events to validate, through clinically relevant system-to-system testing scenarios that meet the data exchange requirements of providers effectively within a defined episode of care. It is anticipated as the digital medicine market expands, so will the need for IHE domains to keep pace with the next generation of digital medicine capabilities.

  Conclusion Top

Advocates for the expanded use of new and innovative digital medicine solutions will need to incorporate within their product development lifecycle, clinical quality measure criteria that align to the clinical quality and interoperability frameworks such as NCQA-HEDIS and IHE, for scalable solutions that enable the achievement of the triple aim. In addition, the digital medicine community will need to adapt essential health care-based enterprise architecture tenets, such as IHE, for software and hardware components that follow a maturity model similar to established health information technology solutions, such as EHR systems and mobile health solutions for operational sustainability and reusability. Finally, digital medicine domain will be an effective driver for the refinement and expansion of new and existing clinical quality measures, as its place within the care and disease management workflow is firmly established as a predictable form of care delivery. The future and possibilities are bright as we integrate the great potential of digital medicine innovation with industry-based clinical measures aligned to the triple aim for learning health system worldwide and the patients who receive care from them.

DOI: 10.4103/2226-8561.198413

  References Top

Smith M, Saunders R, Stuckhardt L, McGinnis JM, editors. Committee on the Learning Health Care System in America, Institute of Medicine. Washington, DC: National Academies Press; 2012.  Back to cited text no. 1
Aisner D, Olsen LA. The Learning Healthcare System Workshop. Washington, DC: The National Academies Press; 2007. p. 374.  Back to cited text no. 2
Duffy RL, Yiu SS, Molokhia E, Walker R, Perkins RA. Effects of electronic prescribing on the clinical practice of a family medicine residency. Fam Med 2010;42:358-63.  Back to cited text no. 3
NCQA. 2010 MHC Standards and Guidelines (Epub). Accessed from: http://www.store.ncqa.org/index.php/2012-aco-standards-and-guidelines-electronic-pub.html. [Last accessed on 2016 Jul 27].  Back to cited text no. 4
NCQA. Portals. Accessed from: http://www.ncqa.org/Portals/0/HEDISQM/HEDIS2013/ACO_Core_Measure_List_9.6.12.pdf. [Last accessed on 2016 Jul 29].  Back to cited text no. 5
NCQA. HEDIS 2016 Technical Specifications for ACO Measurement (Epub). Accessed from: http://www.store.ncqa.org/index.php/catalog/product/view/id/2260/s/hedis-2016-technical-specifications-for-aco-measurement-epub/category/58/. [Last accessed on 2016 Jul 29].  Back to cited text no. 6
IHE. Domains. Accessed from: http://www.ihe.net/IHE_Domains/. [Last accessed on 2016 Jul 29].  Back to cited text no. 7
IHE. IHE Main Page. Accessed from: http://www.ihe.net/. [Last accessed on 2016 Jul 29].  Back to cited text no. 8
IHE. IHE Worldwide. Accessed from: http://www.ihe.net/IHE_Worldwide/. [Last accessed on 2016 Jul 29].  Back to cited text no. 9
IHE. Quality Research and Public Health. Accessed from: http://www.ihe.net/Quality_Research_and_Public_Health/. [Last accessed on 2016 Jul 29].  Back to cited text no. 10


  [Figure 1]

  [Table 1]


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