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How to Improve Patient Outcomes through Harnessing Big Data

Infection preventionists (IPs) collect a large amount of data from the electronic medical record, laboratory, and radiology. But collecting data is just the beginning. It has to be sorted, reviewed, analyzed, reported, and stored. Some basic data processing programs (like Excel), can help you sort and review this information but it cannot help on the front end – the IP has to enter data culled from another source. The National Healthcare Safety Network (NHSN) system can store, retrieve, and analyze your data but cannot help sort through the volumes of information to help the IP determine whether a patient has a healthcare-associated infection (HAI).

IPs are constantly seeking ways toimprove patient outcomes by reducing the patient’s risk of acquiring an HAI. While educating, making rounds, and observing technique is important, it is necessary for the IP to utilize healthcare informatics – the science of using information technology to design, develop, apply, manage, organize, analyze, and optimize healthcare delivery to improve processes. 1,2

As the demand for data from IPs continues to rise, access to information technology is crucial but what is the best solution? Electronic surveillance systems (ESS) can streamline an infection prevention program by assisting with data collection and surveillance activities, but the system may not provide the full capacity for preventing HAIs. Clinical decision support (CDS) systems are algorithm- or rule-based tools designed to provide “computer-generated clinical knowledge and patient-related information intelligently filtered or presented at appropriate times, to enhance patient care.” 3

There is some overlap between ESS and CDS but CDS can provide alerts to IPs in real time by determining whether a patient needs immediate isolation precautions based on organism identification. An excellent article by Wright and Robicsek 4 outlines the benefits and attributes of a CDS for IPs by giving an overview of CDS systems and summarizing key characteristics of successful tools.

Examples of alerts or reminders that can be set up:

  • Bug/drug mismatches
  • Surveillance for high-risk antibiotics for the prevention of C.difficile infection
  • MRSA (Methicillin-Resistant Staph aureus) surveillance culture requests
  • Isolation precautions suggestion due to a positive culture for a multirug-esistant organism (MDRO)
  • Monitoring patients with positive influenza assays

The IP should beware of alert fatigue: where alerts are ignored because there are just too many to address. To avoid alert fatigue, IPs should be involved upfront with their information technology and clinical staff to develop a system that suits the facility.

Are you drowning in data? Would a CDS help you?


  1. Joos IM, Nelson R, Smith M. Introduction to computers for healthcare professionals, 6th ed. Burlington, MA: Jones & Bartlett Learning, 2014.
  2. Bienvenu S, Moody J, Clinical Services Group -HCA Hospital Corporation of America. APIC Text of Infection Control and Epidemiology. Healthcare Informatics and Information Technology Chapter 6.
  3. Sittig DF, Wright A, Osheroff JA, Middleton B, Teich JM, Ash JS, et al. Grand challenges in clinical decision support. J Biomed Inform 2008;41:387-92.
  4. Wright, Marc-Oliver et al. Clinical decision support systems and infection prevention: To know is not enough. American Journal of Infection Control, Volume 43  Issue 6, 554-558.

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