Acute Coronary Syndrome Core Measures and Clinical Information System

This presentation covers the core measures for Acute Coronary Syndrome, including the Joint Commission core measures set and the Clinical Information System (CIS) used for data input and outcomes. The steps involved in the CIS, such as data collection, storage, extraction, transmission, presentation, analysis, and interpretation, are explained. The collected data elements, including specific alerts, demographic and clinical data, research analysis, ICU interventions, complications, hospital stay, and extraction of survival data, are also discussed. The presentation concludes with the outcomes of the CIS, such as providing prognostic information, risk stratifying patients with ACS, patient safety, patient satisfaction, and healthcare quality.

ACUTE CORONARY
SYNDROME
Name of the Student
Name of the University
Author Notes

Acute Coronary Syndrome Core Measures
• Decreased blood flow to coronary arteries
• Inability of heart muscles to function.
• Joint Commission core measures set include –
1. AMI-1 : To be given Aspirin on arrival
2. AMI-2 : Aspirin to be prescribed on discharge
3. AMI-3 : ACEI or ARB for left ventricular systolic dysfunction

Acute Coronary Syndrome Core Measures
continued…………
4. AMI-4 : Nonaccountability measure – Counselling or advice on
cessation of adult smoking
5. AMI-5 : Prescribing of beta blocker at the time of discharge
6. AMI-7 : Fibrinolysis median time
7. AMI-7a : Receiving fibrinolytic therapy 30 minutes on arrival to hospital
8. AMI-8 : PCI median time

Acute Coronary Syndrome Core Measures
continued…………
9. AMI-8a : PCI received following 90 minutes of arrival to hospitals
10. AMI-9 : Mortality of the inpatients (retired effective 12/31/2010)
11. AMI-10 : Prescription of statins during discharge
(Jointcommission.org, 2018)

Clinical Information System (CIS)
Steps in the clinical information system:
Data collection gathering, capture, input and entry
Data storage accumulation and availability
Data extraction retrieval and output
Data transmission submission and retrieval
Data presentation display and view
Data analysis and interpretation use

CIS data input for acute coronary
syndrome
Acute cardiac ischemia time-insensitive predictive instrument
information system (ACI-TIPI-IS)
• Uses numerous information technology applications
• Real time decision support
• Alerting
• Retrospective feedback for performance improvement
• For patients in emergency departments with ACS (Bagchi et al., 2012)

TIPI-IS: Web based relational database
system
• Compiles information about emergency department patients.
• Compiles information from existing operational systems.
• Collects electrocardiogram information for emergency department
patients.
• Supports aspects of patient safety.
• Calculates probability of ACS in patient (0-100%) (Ncbi.nlm.nih.gov,
2018).

Collected data elements
include:
• Specific alerts
• Demographic and
clinical data
(Kawamoto et al.,
2012)
• Research analysis
• ICU interventions,
complications and
hospital stay
• Extraction of
survival data (Clarke
et al., 2013)

Outcomes
• Provides prognostic information
• Risk stratifying patients with ACS (Parhar et al., 2018)
• Patient safety
• Patient satisfaction
• Healthcare quality (Karimi, Poo & Tan, 2015)

Reference List
• Bagchi, S., Ferrucci, D. A., Levas, A. T., & Mueller, E. T. (2012). U.S. Patent Application No. 13/077,480.
• Clarke, M. A., Belden, J. L., Koopman, R. J., Steege, L. M., Moore, J. L., Canfield, S. M., & Kim, M. S.
(2013). Information needs and information‐seeking behaviour analysis of primary care physicians
and nurses: a literature review. Health Information & Libraries Journal, 30(3), 178-190.
• Jointcommission.org. (2018). Acute Myocardial Infarction Core Measure Set. Jointcommission.org. Retrieved
16 February 2018, from https://www.jointcommission.org/assets/1/6/Acute%20Myocardial
%20Infarction.pdf
• Karimi, F., Poo, D. C., & Tan, Y. M. (2015). Clinical information systems end user satisfaction: The
expectations and needs congruencies effects. Journal of biomedical informatics, 53, 342-354.
• Kawamoto, K., Jacobs, J., Welch, B. M., Huser, V., Paterno, M. D., Del Fiol, G., ... & Jenders, R. A. (2012).
Clinical information system services and capabilities desired for scalable, standards-based, service-
oriented decision support: consensus assessment of the Health Level 7 clinical decision support Work Group.
In AMIA Annual Symposium Proceedings (Vol. 2012, p. 446). American Medical Informatics Association.
• Ncbi.nlm.nih.gov. (2018). Using Specialized Information Technology to Reduce Errors in Emergency Cardiac
Care. Ncbi.nlm.nih.gov. Retrieved 16 February 2018, from
https://www.ncbi.nlm.nih.gov/books/NBK20561/
• Parhar, K., Millar, V., Zochios, V., Bruton, E., Jaworksi, C., West, N., & Vuylsteke, A. (2018). Clinical
outcomes of patients undergoing primary percutaneous coronary intervention for acute myocardial