Discussion:Clinical Decision Support Systems

 

Clinical decision support (CDS) systems refer to a variety of computer-based tools and software that health care professionals, including nurses, can use to inform the choices they make at the point of care. CDS systems are informatics tools that are aimed at increasing the effectiveness and quality of health care by connecting evidence, best practices, and knowledge with decisions made in the practice setting. Because of its focus on improving outcomes, CDS is included as one of the core components of meaningful use in the Health Information Technology for Economic and Clinical Health (HITECH) Act.

 

In this Discussion, you analyze the elements that can influence the design of a CDS system. You also assess the benefits of CDS and identify challenges that may occur in practice related to a CDS system.

 

To prepare

 

  • Review the information in this week’s Learning Resources on CDS systems. Reflect on why and how these systems are used in health care organizations and specific practice settings.

  • Search in the Walden Library for an article on a specific CDS system that has recently been researched, implemented, or evaluated. The article you select must have a publication date within the past 3 years (SEE ARTICLE ATTACHED IN FILE AREA)

  • Consider the requirements and guidelines (meaningful use, evidence-based practice guidelines, organizational regulations, core measures, etc.) that influenced the design of the CDS system.

  • Reflect on the benefits of using this CDS system in a practice setting. Why would an organization implement this specific system? What organizational, legal, or practice-related needs or issues does the system address?

  • Reflect on the practice-related problems that the system caused or could cause. For example, do you see an issue with alert fatigue, with too few alerts, with override capabilities, or with some other aspect of the system?

     

    Post by tomorrow 09/06/2016, a 550 words essay in APA format with a minimum of 3 references from the list provided under required readings. Apply the level 1 headings as numbered below:

    1) A brief summary of the CDS system highlighted in the article you selected, including the practice setting in which it can be or was used and the requirements and guidelines that influenced its design. (SEE ARTICLE ATTACHED IN FILE AREA)

    2) Explain the benefits of the CDS system to the practice setting.

    3) Identify potential problems that could or did arise related to the CDS system

     

     

     

    Required Readings

    Bredemeyer, J., & Androwich, I. (2012). Transitional research: Generating evidence for practice. In D. McGonigle & K. G. Mastrian (Eds.), Nursing informatics and the foundation of knowledge (pp. 471–485). Burlington, MA: Jones and Bartlett. (SEE ATTACHED FILES FOR THESE PAGES)

     This chapter describes information gathering and application processes of translational research. The authors also identify the importance of self-critique and persistent answer-seeking in evidence-based practice.

    McGonigle, D., & Mastrian, K. G. (2015). Nursing informatics and the foundation of knowledge (3rd ed.). Burlington, MA: Jones and Bartlett Learning.

 

  • Chapter 25, “Translational Research: Generating Evidence for Practice”
  • Chapter 16, “Informatics Tools to Promote Patient Safety and Clinical Outcomes”

 

 

 

Ensuring the safety of patients is the responsibility of everyone within a health care organization. This chapter analyzes the role that technological advancements in information storage and delivery plays in supporting safety practices.

 

Brokel, J. M. (2009). Infusing clinical decision support interventions into electronic health records. Urologic Nursing, 29(5), 345–352.

 

Retrieved from the Walden Library databases.

 

 This article describes a variety of decision support systems interventions that are available to nurses while using electronic health records. The author suggests how these interventions can be used by nurses for assessments, for diagnosing problems and identifying preferences, for performing interventions, and for evaluating outcomes.

 

 

 

Glaser, J. (2008). Clinical decision support: The power behind the electronic health record. Healthcare Financial Management, 62(7), 46–48, 50–51.

 

Retrieved from the Walden Library databases.

 

 In this article, the author considers the impact that clinical decision support has on patient care by establishing the relationship between EHR-based patient care and CDS-based applications.

 

 

 

Kesselheim, A. S., Cressweel, K., Phansalkar, S., Bates, D. W., & Shiekh, A. (2011). Clinical decision support systems could be modified to reduce “alert fatigue” while still minimizing the risk of litigation. Health Affairs, 30(12), 2310–2317.

 

Retrieved from the Walden Library databases.

 

 Clinical decision support (CDS) systems implemented to assist health care personnel with decision making help health care organizations use their resources most effectively. This article deals specifically with the ways CDSSs can help health care organizations save money.

 

 

 

Agency for Healthcare Research and Quality. (n.d.). National Guideline Clearinghouse. Retrieved January 2, 2014, from http://www.guideline.gov/

 

 NGC is a public resource for evidence-based clinical practice guidelines. Provided by the Agency for Healthcare Research and Quality (AHRQ) a part of the U.S. Department of Health & Human Services.

 

 

 

 

 

Hammond, W. E., Jaffe, C., & Kush, R. D. (2009). Healthcare standards development: The value of nurturing collaboration. Journal of AHIMA, 80(7), 44–52. Retrieved from http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_043995.hcsp?dDocName=bok1_043995

 

 The authors examine how standards developing organizations (SDOs) collaborate to create technical standards. Those standards clarify communication between health care personnel and improve patient care.

 

 

 

 

 

McMaster University. (2012). Evidence-based practice resources. Retrieved from http://hsl.mcmaster.ca/resources/topic/eb/

 

 

 

This collection of resources on evidence-based practice (EBP) covers basic information about its methodologies. This includes a list of useful references, charts, and definitions pertaining to different facets of EBP application in health care organizations.

 

 

 

An Electronic Nursing Patient Care Plan Helps in Clinical Decision Support

Wong CM
a
, Wu SY

a
, Ting WH

b
, Ho KH

b
, Tong LH

a
, Cheung NT

a,b

a

Health Informatics Section, Hospital Authority, Hong Kong SAR
b
Information Technology Division, Hospital Authority, Hong Kong SAR

Abstract

Information technology can help to improve health care

delivery. The utilisation of informatics principle enhances the

quality of nursing practices through improved communication,

documentation and efficiency. The Nursing Profession

constitutes 34% of the total workforce in the Hong Kong

Hospital Authority (HA) and includes 21,000 nurses in 2012.

To enhance the quality of care and patient safety in both

hospitals and community care setting, it is essential that an

integrated electronic decision support system for nurses is

designed to track documentation and support care or service

including observations, decisions, actions and outcomes

throughout the care process at each point-of-care. The Patient

Care Plan project was set up to achieve these objectives. The

Project adheres to strict documentation information

architecture to ensure data sharing is freely available.

Preliminary results showed very promising improvement in

clinical care.

Keywords:

Patient Care Plan; Efficiency; Documentation.

Introduction

The Patient Care Plan Project (PCP) is a new nursing function

in the HA Clinical Management System (CMS) that depicts

the identified care needs, treatment goals and the progress

towards meeting the goals. Clear communication and

appropriate assessment form the basis of providing vital and

essential information for nursing interventions and initiating

patient care plan in daily operation.

Methods

The HA information architecture was developed in 2002 to

support a fast, robust and standardised electronic patient

record (ePR) from data captured from various CMS function

modules. The Generic Clinical Documentation (GCD) engine

would manage the lifecycle of various information

architecture elements such that the semantics of these

elements (regardless of their source, whether internal or

external) are preserved from design, creation, capture, use,

reuse, and analysis. Forms created using this process and

architecture will be semantic interoperable in different service

modules. The PCP is constructed using this architecture.

The PCP is designed to indicate and identifies high risk

patients based on assessments to provide early notifications to

medical officers, nurses and

AJCP / Review Article

Am J Clin Pathol 2014;142:741-747 741
DOI: 10.1309/AJCP4W5CCFOZUJFU

© American Society for Clinical Pathology

The Next Chapter in Patient Blood Management

Real-Time Clinical Decision Support

Lawrence Tim Goodnough, MD,1,2 and Neil Shah, MD1

From the Departments of 1Pathology and 2Medicine, Stanford University, Stanford, CA.

Key Words: Blood transfusion; Blood utilization; Clinical decision support; Best practice alert; Physician order entry;
Electronic medical records

Am J Clin Pathol December 2014;142:741-747

DOI: 10.1309/AJCP4W5CCFOZUJFU

ABSTRACT

Objectives: Blood transfusion was identified by the American
Medical Association as one of the top five most frequently
overused therapies. Utilization review has been required by
accreditation agencies, but retrospective review has been
ineffective due to labor-intense resources applied to only a
sampling of transfusion events. Electronic medical records
have allowed clinical decision support (CDS) to occur via a
best practices alert at the critical decision point concurrently
with physician order entry.

Methods: We review emerging strategies for improving
blood utilization.

Results: Implementation of CDS at our institution decreased
the percentage of transfusions in patients with a hemoglobin
level of more than 8 g/dL from 60% to less than 30%. Annual
RBC transfusions were reduced by 24%, despite concurrent
increases in patient discharge volumes and case mix
complexity. This resulted in acquisition costs savings (direct
blood product purchase costs) of $6.4 million over 4 years.

Conclusions: We have been able to significantly reduce
inappropriate blood transfusions and related costs through
an educational initiative coupled with real-time CDS. In
deriving increased value out of health care, CDS can be
applied to a number of overuse measures in laboratory
testing, radiology, and therapy such as antibiotics, as
outlined by the American Board of Internal Medicine’s
Choosing Wisely campaign.

In 2009, the Institute of Medicine estimated that 30% of
health care spending, approximately $750 billion annually,
was wasteful and unnecessary. Deriving value out of
health care expenditures is important since reimbursement
models are beginning to reward quality over quantity. Blood
transfusion was the most frequently performed procedure in
2009,1 and a significant percentage of transfusions have been
identified to be inappropriate.2-4 Allogeneic blood transfusions
carry inherent risk,5 and studies have increasingly linked
transfusions with adverse clinical patient outcomes, including
morbidity and mortality.6-9 A large Cochrane meta-analysis
of 19 trials and more than 6,000 patients found that restrictive
transfusion strategies were equivalent to liberal transfusion
strategies in most patient

RESEARCH ARTICLE Open Access

A clinical decision support tool to screen
health records for contraindications to
stroke thrombolysis–a pilot study
Mu-Chien Sun* and Jo-Ann Chan

Abstract

Background: The use of intravenous thrombolysis for stroke is limited by contraindications that may be difficult to
identify promptly and accurately. Evidence supports the use of information technology-based clinical decision support
(CDS) tools to achieve improvements in care delivery. The objective of this pilot study was to investigate the efficacy of
a CDS tool to screen health records for contraindications to intravenous stroke thrombolysis.

Methods: A CDS tool was developed to rapidly screen health information in seven affiliated hospitals for
contraindications to stroke thrombolysis. A fixed-sequence, 2-period crossover study was conducted to test the
efficacy of the CDS tool. Four mock patient records derived from the stroke registry that contained a total of
nine contraindication items in two or more of the hospitals were used for testing purposes. The test patients
were preset and balanced between groups with and without the CDS tool appearing six times in each group
before recruiting the participating physicians. Physicians who were responsible for thrombolytic therapy and
willing to sign informed consent were recruited. The participating physicians were asked to check a list of
contraindications for two of the patients by using a shared electronic medical record system among the seven
hospitals with and without the CDS tool. The test time and missed contraindications were recorded and
analyzed statistically.

Results: A total of 14 physicians who were responsible for stroke thrombolysis were approached, and 12 signed
informed consent and took the test. By using the CDS tool, the test time was reduced significantly from 14.6 ± 7.4 to
7.3 ± 5.2 min (P = 0.010). In a total of 54 contraindications, the number of missed contraindications was reduced
significantly from 23 (42.6 %) to seven (13.0 %) (P = 0.001). The difference of missed contraindication number
between the two groups was statistically significant either per physician or per contraindication item.

Conclusions: By screening health records for relevant contraindications, the use of a CDS tool may reduce the
time needed to review medical records and reduce the number of missed contraindications for stroke
thrombolysis.

Keywords: Biomedical technology, Brain infarction, Decision making, Computer-assisted, Decision support
systems, Clinical, Hospital information systems, Medical records systems, Computerized, Medical informatics,
Stroke, Thrombolytic therapy

* Correspondence: [email protected]
Stroke Center and Department of Neurology, Changhua Christian Hospital,
#135, Nanhsiao Street,