Clinical Decision Support Systems - Part 2

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Objectives

  • Identify the challenges and barriers to building and using CDSSs.
  • Discuss legal and regulatory considerations related to the distribution of CDSSs.
  • Describe current initiatives that will impact the future and effectiveness of CDSSs.

This lecture has three objectives. By the end of this lecture, you should be able to identify the challenges and barriers to building and using CDSSs, discuss legal and regulatory considerations related to the distribution of CDSSs, and describe current initiatives that will impact the future and effectiveness of CDSSs.

CDSS Challenges

  • Achievement of the five rights of clinical decision support
    • Requires communicating the
      1. Right information
      2. To the right person
      3. In the right format
      4. Through the right channel
      5. At the right time

As published on the Agency for Healthcare Research and Quality (AHRQ) website, the CDS Five Rights model states CDS-supported improvements in desired healthcare outcomes can be achieved by communicating the right information, to the right person, in the right format, through the right channel and at the right time.

The five rights model comes from Osheroff, et al., and is a good summary of what is needed for effective delivery.

CDS Five Rights Model

As a framework for supporting clinical decisions to improve outcomes, the CDS Five Rights model states CDS-supported improvements in desired healthcare outcomes can be achieved if communication occurs in the following manner:

  1. The right information: Evidence-based, suitable to guide action, pertinent to the circumstance.

  2. To the right person: Considering all members of the care team, including clinicians, patients, and their caretakers.

  3. In the right CDS intervention format: Such as an alert, order set, or reference information to answer a clinical question.

  4. Through the right channel: For example, a clinical information system (CIS) such as an electronic medical record (EMR), personal health record (PHR), or a more general channel, such as the Internet or a mobile device.
     
  5. At the right time in workflow: For example, at time of decision/action/need.

However, achieving the five rights for CDS is challenging. The report Clinical Decision Support Systems: State of the Art explains “achieving the five rights for CDS presents challenges, and the challenges differ depending on how closely the CDS is tied to what the clinician already intends to do. Clinicians may initially want certain reminders or, after performance assessments, agree that they need other reminders, but in either situation they are choosing to receive the reminders. The key issue in reminding the user about things they choose to be reminded about is the timing of the reminder. For instance, should reminders for preventive care be given to the physician in advance of the patient visit (for example, the day before), or should the reminders appear during the patient’s visit?”

Challenges in Designing or Selecting CDSS

  • Whose decisions are being supported?
  • What information is presented?
  • When is the information being presented?
  • How is the information being presented?

Clinical decision support systems offer so much potential to improve patient care and outcomes. Similar challenges in designing and selecting clinical decision support systems to the five rights model are posed as questions in the report Clinical Decision Support Systems: State of the Art. The questions listed are whose decisions are being supported, what information is presented, when it is presented, and how it is presented to the user.

Each one of these questions should be explored and answered before building or selecting a clinical decision support system. If any are ignored, the chances that end-users will use it and the expected system benefits gained are limited. For example, consider the question: when the intervention will be presented? Depending on the information, the best time to deliver could be at the point of care—for example, delivering an alert about drug-to-drug interactions at the time of prescribing. Other information, such as providing the names of patients being seen on a given day who need immunizations, could occur prior to the patient encounter. Knowing when the information from the CDS should be presented automatically or “on demand”, that is, when the user chooses to access the information, is no small feat, and ties to the answers to the other questions, for example, whose decisions are being supported.

User Control

  • User presentation
    • Automatically
    • On demand
  • User reaction

Looking further at the challenge of knowing when the information from the CDS should be presented, that is, automatically or “on demand,” another factor that must be considered and presents its own set of challenges is deciding how much control the user has over the decision to use clinical decision support. In other words control over whether users are required to accept the CDS suggestion, whether they can easily ignore it, or whether it takes significant effort to override the advice.

The report Clinical Decision Support Systems: State of the Art, explains, “these decisions involve not only whether the CDS is set up to be displayed on demand, so that users have full control over whether they choose to access it, but also the circumstances under which users can, after viewing the CDS information, choose whether to accept it. The two aspects of control are related and they connect with how closely the CDS advice matches a clinician’s intention. CDS may be designed to:

  1. remind clinicians of things they intend to do, but should not have to remember;
  2. provide information when clinicians are unsure what to do;
  3. correct errors clinicians have made; or
  4. recommend that the clinicians change their plans.

Conceived of in this way, it should be obvious that the users’ reactions to CDS may differ with these diverse intents.”

CDS Intent and Key Issues

Building on to the challenges already described, the table from the Clinical Decision Support Systems: State of the Art, summarizes three clinical decision support intents, and matches each to a user’s intention along with a key issue.

  1. The first CDS intent is an automatic intervention – a reminder of actions a user intends to do but should not have to remember. As one would expect, timing is a key issue.

  2. Next in CDS intent is an on demand intervention – one that provides information when a user is unsure of what to do, or a request for consultation. In this instance, it is speed and ease of access that the user is looking for. As the report states, “Users may recognize the need for information, but may be willing to access it only if they can do so efficiently. If access is too difficult or time-consuming, potential users may choose not to use the CDS.”

  3. The third intervention’s intent, to correct user’s errors and/or recommend a user change plans, could be either an automatic or on-demand intervention. For an automatic intervention, the key issues are timing, autonomy, and user control over the response. For an on demand intervention, they are speed, ease of access, autonomy, and user control over the response. For this CDS intent, users balance the change planned with the desire for autonomy with other demands such as improving patient safety or decreasing practice costs. Another key issue related to autonomy that was previously discussed is the amount of control users have over how they respond to the CDS.

The report, Clinical Decision Support Systems: State of the Art, goes on to explain, “while some of these issues have been addressed by research, there are no universally accepted guidelines regarding them, in part because clinicians often differ in their preferences. In addition, there are varying clinical approaches that are justified, which makes designing effective CDS a challenge. How these issues are addressed will influence the ultimate impact and effectiveness of CDS.”

Challenges in Building CDSS

  • Impact on Care Process and Patient Health Outcomes
    • Match of CDS to user intentions
    • User control, disruptiveness, and risk
    • Integration of CDS into work processes
  • Impact on Structure

The report, Clinical Decision Support Systems: State of the Art, cited several studies and provided insight into other challenges in the building and using of clinical decision support systems. Discussions were split between the impact on care process and patient health outcomes and the impact on structure.

For the first one, impact on care process and patient health outcomes, the three challenges identified were:

  • matching of clinical decision support to user intentions,
  • user control, disruptiveness, and risk, and
  • integration of CDS into work processes.

Each one of these challenges presents issues which need to be addressed when building clinical decision support systems. For example, according to the report, “…integrating CDS into the workflow often requires unique customization to local processes, and sometimes to changes in processes (when previous clinical processes were found to be inefficient or ineffective). CDS also for the CDS can be disruptive if the clinical systems are not well-integraneeds to be minimally disruptive to the clinician’s “cognitive workflow” and this, too, can be a challenge. For instance, accessing the data needed or if the necessary data are not in a form that the CDS can use. If the lack of data leads to inappropriate alerts, these alerts may be overridden. In addition, to the extent that using CDS or following its advice is disruptive to the clinician’s work or thought processes, the CDS is likely to be ignored.”

Another group of discussion points addressed studies on the structural impact of CDS. The conclusion was “It is important to recognize that the development, implementation, and maintenance of CDS will have an impact on the structure or work system in which it will be used. The changes that the CDS will introduce need to be incorporated in the planning so that the impact on clinician time is not excessive.”

Barriers to Using CDSS

  1. Acquisition and validation of patient data
  2. Modeling of medical knowledge
  3. Elicitation of medical knowledge

Described in Chapter 20 of Biomedical Informatics: Computer Applications in Health Care and Biomedicine are six barriers to the effective implementation of CDS. The first three identified by Musen, Shahar, and Shortliffe are

[1] Acquisition and validation of patient data – The issues here are the need to have:

  • effective techniques for capturing data accurately, completely, and efficiently and
  • a standardized way to express clinical situations that a computer can interpret.

[2] Modeling of medical knowledge – Described by Musen, Shahar, and Shortliffe as “deciding what clinical distinctions and patient data are relevant, identifying the concepts and relationships among concepts that bear on the decision-making task, and ascertaining a problem-solving strategy that can use the relevant clinical knowledge to reach appropriate conclusions.”

[3] Elicitation of medical knowledge – keeping the knowledge-base up-to-date is portrayed by Musen, Shahar, and Shortliffe as an important problem for CDSS

  1. Representation of and reasoning about medical knowledge
  2. Validation of system performance
  3. Integration of decision-support tools

The last three barriers to the effective implementation of CDS are:

[4] Representation of and reasoning about medical knowledge – Musen, Shahar, and Shortliffe stated “among the ongoing research challenges is the need to refine the computational techniques for encoding the wide range of knowledge used in problem-solving by medical experts.” Another part to this mentioned in chapter 20 of Biomedical Informatics: Computer Applications in Health Care and Biomedicine is the need to obtain an understanding of the psychology of human problem-solving  for use in the development of clinical decision support tools that more closely simulate the process by which expert clinicians move from observations to diagnoses or management plans.

[5] Validation of system performance – Here Musen, Shahar, and Shortliffe pointed out issues of having a responsible party for validating the clinical knowledge bases and the challenges in determining how best to evaluate the performance of the tools that use the knowledge particularly when a “gold standard” in which to perform the evaluation doesn’t exist.

[6] Integration of decision-support tools – Recognizing that integration challenges exist and are inherently tied to issues of networking and systems interfaces, Musen, Shahar, and Shortliffe state the need for “more innovative research on how best to tie knowledge-based computer tools to programs designed to store, manipulate, and retrieve patient-specific information.”

Legal Barriers

  • Lack of detailed case laws for CDSS
    • Which category of law
      • Negligence law
      • Product liability law

One legal barrier to the implementation of clinical decision support systems is the lack of detailed case law on issues for dealing with clinical decision support systems and which category of law the systems will fall into. Chapter 20 of Biomedical Informatics: Computer Applications  in Health Care and Biomedicine contains the following explanation regarding this barrier:

“Under negligence law (which governs medical malpractice), a product or activity must meet reasonable expectations for safety. The principle of strict liability, on the other hand, states that a product must not be harmful. Because it is unrealistic to require that decision support programs make correct assessments under all circumstances—we do not apply such standards to physicians themselves—the determination of which legal principle to apply will have important implications for the dissemination and acceptance of such tools.”

  • Liability borne by
    • Physicians
    • Developers of systems

Another legal barrier described in Chapter 20 of Biomedical Informatics: Computer Applications  in Health Care and Biomedicine is the issue of who will bear the liability. Should it be the physicians or the builders of the systems? Chapter 20 delved further into this issue. Musen, Shahar, and Shortliffe state:

“a related question is the potential liability borne by physicians who could have accessed such a program, and who chose not to do so, and who made an incorrect decision when the system would have suggested the correct one. As with other medical technologies, precedents suggest that physicians will be liable in such circumstances if the use of consultant programs has become the standard of care in the community. Several guidelines have been suggested for assigning legal liability to builders of knowledge-based medical decision-support systems or to the physicians using them [Allaërt & Dusserne, 1992].”

Regulatory Barriers

There are also regulatory barriers that could affect distribution of clinical decision support systems. One identified by Musen, Shahar, and Shortliffe in chapter 20 Biomedical Informatics: Computer Applications in Health Care and Biomedicine  is the validation of decision-support tools before their release and what role the government should play.

Where should the government fall with regards to prerelease regulations of medical software? As cited in chapter 20 “Current policy of the Food and Drug Administration (FDA) in the United States indicates that such tools will not be subject to federal regulation if a trained practitioner is assessing the program’s advice and making the final determination of care (Young, 1987).”

However, Musen, Shahar, and Shortliffe go on to point out that “programs that make decisions directly controlling the patient’s treatment (for example, closed loop systems that administer insulin or that adjust intravenous infusion rates or respirator settings) are viewed as medical devices subject to FDA regulation.”

The other barriers identified by Musen, Shahar, and Shortliffe include data privacy and security.

Legislative and Regulatory Efforts

  • Better align efforts across various stakeholders
  • Explore options to establish or leverage a public-private entity to facilitate collaboration
  • Accelerate CDS development and adoption though federal government programs and collaborations

The following legislative and regulatory efforts needed to support widespread adoption of clinical decision support systems were identified by the AHIC CDS Work Group recommendations in a letter to Secretary HHS Leavitt on April 22, 2008 and summarized by HIMSS in their Clinical Decision Support (CDS) Fact Sheet.

The AHIC CDS Work Group recommendations were to:

  • Better align quality measure development, CDS development, payment policy and evaluation efforts across various stakeholders so that system level changes to achieve a high performance healthcare system will be more likely to succeed.

  • Explore options to establish or leverage a public-private entity to facilitate collaboration across many CDS development and deployment activities.

  • Accelerate CDS development and adoption though federal government programs and collaborations.

One of these recommendations have been implemented as the next few sections will show.

Sources:

Future Directions for CDSS

  • ONC initiatives
  • IOM studies
  • Meaningful use objectives and measures

There are a number of projects shaping the future directions for clinical decision support systems. These include the Office of the National Coordinator’s initiatives, the Institute of Medicine’s studies, and the meaningful use criteria, objectives and measures. Each will be explored in the sections that follow.

ONC Initiatives

The Office of the National Coordinator for Health IT (ONC), which is charged with coordinating federal efforts regarding HIT adoption and meaningful use, has stated their commitment and facilitated a variety of initiatives to catalyze progress in CDS development and deployment in support of enhanced health and care. The major activities include:

Development of a Roadmap for National Action on Clinical Decision Support – This report recommended a series of activities to improve CDS development, implementation and use throughout the United States. The roadmap identified three critical pillars for fully realizing the promise of CDS:

  1. Making the best available clinical knowledge well-organized for CDS interventions, and

  2. Promoting the high adoption and effective use of CDS tools, and

  3. Continuously improving knowledge and CDS methods.

The CDS recommendations focused on improving health care quality through the effective use of CDS, facilitating collaboration across CDS initiatives, and accelerating CDS development and adoption through federal government programs and collaborations.

The ONC-sponsored Clinical Decision Support (CDS) Workshop brought together a large group of subject matter experts who shared their thoughts on a series of topics related to advancing the utility, usability, and meaningful use of CDS.

The CDS Federal Collaboratory focuses on CDS as a key health information technology component for improving the quality, safety, efficiency and effectiveness of health care. Encompassing a wide range of federal professionals, the CDS Federal Collaboratory serves as a forum for sharing CDS-related interests, perspectives and priorities. The Collaboratory also strives to foster communication about, and collaboration between CDS-related activities across many different agencies. The ONC for Health IT funded the Advancing CDS project to accelerate the successful implementation and effective use of CDS interventions. The project is designed to advance the widespread dissemination of successful CDS implementation practices to promote broad CDS adoption, improve the acceptance and usability of medication CDS systems through the development of a clinically important drug-to-drug interaction list, advance the practical sharing of effective CDS interventions across care settings, and identify CDS-related gaps and goals specific to a broad range of clinical specialties.

The final ONC initiative is an Institute of Medicine study to be carried out under a $989,000 contract awarded in September 2010. The next section provides more information on this upcoming work.

IOM Studies

  • 1999 IOM study
    • To improve safety, health IT systems should be designed to make it “easy to do the right thing.”
  • 2010
    • Launch of a new study on ensuring the efficacy of information technology in improving healthcare safety

The Institute of Medicine has for many years published key bodies of work. A Health and Human Services press release on September 29, 2010 contained the following quote: “Since 1999, when the IOM published its ground-breaking study To Err Is Human, the Institute has been a leader in the movement to improve patient safety,” said David Blumenthal, M.D., national coordinator for health information technology.

Source: http://www.hhs.gov/news/press/2010pres/09/20100929b.html

In the 1999 report, IOM emphasized that, to improve safety, health IT systems should be designed to make it “easy to do the right thing.”

The study launched in 2010 is aimed at ensuring that health information technology (HIT) will achieve its full potential for improving patient safety in health care and will examine a comprehensive range of patient safety-related issues, including prevention of HIT-related errors and rapid reporting of any HIT-related patient safety issues. It will make recommendations concerning the potential effects of government policies and private sector actions in maximizing patient safety and avoiding medical errors through HIT.

Stage 1 Meaningful Use

  • ARRA/HITECH
    • Regulations
      • Meaningful Use Objectives
        • Eligible Professional
        • Hospital

The final endeavor having an impact on future directions for CDSS is the American Recovery and Reinvestment Act or ARRA and the associated Health Information Technology for Economic and Clinical Health provision. ARRA, officially Public Law 111-5 signed into law February 2009, provides many different stimulus opportunities, one of which is $19.2 billion for health IT. The Health Information Technology for Economic and Clinical Health, often referred to as HITECH, is a provision of the American Recovery and Reinvestment Act. The HITECH section of ARRA deals with many of the health information communication and technology provisions.

The Centers for Medicare and Medicaid EHR Incentive Programs Web site provides the following with regards to HITECH and the meaningful use of interoperable health information technology and qualified EHRs: “The Health Information Technology for Economic and Clinical Health Act, or the "HITECH Act" established programs under Medicare and Medicaid to provide incentive payments for the "meaningful use" of certified EHR technology. The Medicare and Medicaid EHR incentive programs will provide incentive payments to eligible professionals and eligible hospitals as they adopt, implement, upgrade or demonstrate meaningful use of certified EHR technology. The programs begin in 2011. These incentive programs are designed to support providers in this period of Health IT transition and instill the use of EHRs in meaningful ways to help our nation to improve the quality, safety and efficiency of patient health care.”

On July 13, 2010, two regulations were released, one of which defines the “meaningful use” objectives that providers must meet to qualify for the bonus payments, and the other which identifies the technical capabilities required for certified EHR technology. The Secretary of HHS published in the Federal Register a final rule that adopted standards, implementation specifications, and certification criteria for HIT. The final rule was released in conjunction with the Medicare and Medicaid EHR Incentive Programs final rule. The CMS regulations specify the objectives that providers must achieve in payment years 2011 and 2012 to qualify for incentive payments. The ONC regulations specify the technical capabilities that EHR technology must have to be certified and to support providers in achieving the “meaningful use” objectives.

Contained in the rule was the following meaningful use requirements that must be met to qualify for incentive payments:

  1. For the eligible professional: Implement one clinical decision support rule relevant to specialty or high clinical priority along with the ability to track compliance with that rule.

  2. For the hospital: Implement one clinical decision support rule related to a high priority hospital condition along with the ability to track compliance with that rule.

Summary

  • Challenges and barriers in building and using clinical decision support systems
  • Legal and regulatory barriers in the distribution of these systems
  • Future directions for clinical decision support systems

While there are challenges and barriers, including legal and regulatory ones, in the building, use, and distribution of clinical decision support systems, their benefits are seen as worth the work involved. Specific benefits identified on ONC’s Web site include: Increased quality of care and enhanced health outcomes, avoidance of errors and adverse events, and improved efficiency, cost-benefit, and provider and patient satisfaction.

To move forward requires further effort and a number of projects shaping the future directions for clinical decision support systems have taken place in the last few years, and more initiatives are ramping up. These include the ONC initiatives, IOM studies, and the meaningful use requirements tied to clinical decision support.

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