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Clinical Decision Support Systems Paula Coe MSN, RN, NEA-BC NUR 705 Informatics and Technology for Improving Outcomes in Advanced Practice Nursing Dr.

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Presentation on theme: "Clinical Decision Support Systems Paula Coe MSN, RN, NEA-BC NUR 705 Informatics and Technology for Improving Outcomes in Advanced Practice Nursing Dr."— Presentation transcript:

1 Clinical Decision Support Systems Paula Coe MSN, RN, NEA-BC NUR 705 Informatics and Technology for Improving Outcomes in Advanced Practice Nursing Dr. Charlotte Seckman Weber, S. (2007) A qualitative analysis of how advanced practice nurses use clinical decision support systems. Journal of the American Academy of Nurse Practitioners. 19. 652-667

2 Objectives  Explain study design and methods  Discuss grounded theory reflections of APN’s  Describe clinical decision support systems and technology  Explore themes derived from interviews  Implications for advanced practice nurses

3 Background  23 english-speaking, nationally certified APN’s (13-CNS and 10-NP’s)  Urban Medical Centers, Midwest, USA  Qualitative research method with semi structured interview  23 individual (1:1) interviews, approximately 1 hour  19 questions with interview guide  Free time to relate and discuss and relate stories  Pattern and use of clinical decision system in their practice

4 Clinical decision making systems  Interactive computer based system intended to identify and solve problems and make decisions.  Assist physicians and other healthcare professionals with decision making tasks at the point of care:  Diagnose  Treat  Link health observations with health knowledge  To influence health choices by clinicians for improved health care and patient outcomes

5 Can you describe any clinical decision making systems that are currently being used in your area?

6 APACHE III Acute Physiology and Chronic Health Evaluation  Measures the severity of disease for adult patients admitted to intensive care units  Point score is calculated from 12 routine physiological measurements such as:  blood pressure, temperature, heart rate  During the first 24 hours after admission, information about previous health status, and some information obtained at admission  Unit standard of practice: completion on admission and every 24 hours in critical care

7 Purpose of the Study  Grounded theory to reflect the experiences of advanced practice nurses (APN’s) working as critical care (CC) NP’s, CNS using clinical decision making system  How CC APN’s use the APACHE III system?  Degree of system utilization  Factors that enhance/detract from system utilization  Decisions appropriately assisted through technology use  Indicate the utility of system use in healthcare  Enable designers to create systems to be used and benefit all types

8 Core variable of system use  Forecasting decision outcomes  Voluntary process employed to forecast the outcomes of patient care decisions in critical care prior to actual decision making  Four user constructs identified  Categories described  Relationships identified

9 Forecasting Decision Outcomes  System learning experiences  Understanding system technology*  Create inferences from system data  Compare system derived data  Levels of trust in data

10 Subjects understanding of system technology  Positive responses  Clinical signs = system data  Increased clinical experience  Older age  Educated about the technology  Beyond current scope of immediate decisions  Chances of survival  Integrated data into clinical decision making  Negative responses  Less clinical experience  Newer APN’s  Data entry experiences  Patient needs and wants to be considered  Family and support system input  “No need to predict patients response before it occurs”

11 How do you feel about using a computerized decision support tool to assist you in making decisions about patient care?

12 Inferences from system data  Participants derived beliefs and values associated with system use, based on style of learning and understanding  Positive responses associated with: Comparing Applying Forecasting  Negative responses associated with: Decreased use of system Data entry only no use at all

13 Comparing Data  Clinicians observed the correlation to actual vs predicted events  System predicted mortality then clinicians allowed re- evaluation and questioning of decision making  Use data for including in Plan of Care  Collaboration with MD’s  Trust = Use

14 Trusting Data  Verifying data entry before “believing” data and system predictions  Double checking RN data entry  Decision to share system data with patients and family members  RN and MD developed rapport  Understanding patient preferences  Patient/Family request for objective data  Inappropriate use by third party payors

15 Factors influencing decision making satisfaction  Technical knowledge of how and why the system is to be used  Patient acuity level  # of times patient experiences aligned with professional clinical judgment  Lack of computer skills/ease of use  Classroom and hands-on experiences

16 Describe your feelings related to sharing system data with patient and families?

17 Decision Making  Confirm or substantiate APN clinical judgments  Life sustaining treatments or withdrawal  Level of Care aggressiveness  Transfer out/in to ICU  Mortality  Sharing data with family members  As specifically requested  Aids in objective data and decision making for family in difficult situations  Situations of uncertainty  Ethical Decisions

18 Summary  Ability to forecast decision outcomes prior to making decisions  Medical treatment  Family members with objective data  Experiences impacting use of the system  Positive experiences led to increased use  Trust  Education

19 Please share your views about using a clinical decision making system to make decisions about patient care?


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