Download A practical data mining method to link hospital

A practical data mining method to link
hospital microbiology and an infection
control database
Thanh Kim Dao, MSHS, CIC, Firas Zabaneh, MT, CIC, Judy Holmes, MT, CIC, Laura Disrude, MT, Margaret Price, PhD,
and Layne Gentry, MD
Houston, Texas
Background: Data mining is the process of data exploration, selection, and transformation. Data mining has developed as an important tool in health care for converting immense amounts of data into powerful information to support surveillance, detect and
assist with investigation of microbial clusters, and collaborate data for research. Additionally, data mining supports ICPs’ commitment to providing quality health care.
Methods: Realizing the existing potential of a current infection control database, current software has been transformed into a
system capable of data mining with the addition of a single laboratory information system interface.
Results: Commercial data mining software products are available but at significant cost. With a laboratory interface to a current infection control database, a system capable of data mining was created. Similar to other data mining products, this system achieves
electronic surveillance, generates monthly or more frequent reports as required, provides patient diagnosis and culture history, offers unit-specific rates, and provides rapid identification of patients with multidrug-resistant organisms. Additionally, health care system-wide custom report-writing features are highly effective tools used to assist with outbreak investigations and research projects.
Conclusion: Purchasing new software products can be expensive; however, current resources may be used to meet data mining
needs. Although weekly queries are optimal, data mining software must provide, at a minimum, monthly reports to track infection-related events in a health care facility. The capability to detect a sudden increase in any specific microorganism and the ability
to monitor a known problematic microorganism is a necessity. In addition, data mining software products must include graphing
capabilities and ease of use custom report-writing features. (Am J Infect Control 2008;36:S18-20.)
Data mining is a process of data exploration, selection, and transformation. This process can be concurrently or retrospectively applied to immense amounts
of data stored in databases in an automated fashion
that incorporates pattern recognition and data visualization to obtain knowledge.1 Using analytical techniques and statistical methods to identify significant
patterns and trends, data mining and statistics together
produce synergy.2
Data mining surveillance technology explores microbiology data across the interface to select for significant data and transform data into useful information
for ICPs. Data mining is considered an adjunct to infection control quality and does not replace routine surveillance; ICPs have the obligation to conduct routine
surveillance based on Centers for Disease Control and
From the Department of Infection Control, Saint Luke’s Episcopal
Hospital, Houston, TX.
Address correspondence to Thanh Kim Dao, MT, CIC, Department of
Infection Control, Saint Luke’s Episcopal Hospital, 6720 Bertner Avenue, M/C 1-166, Houston, TX 77030. E-mail: [email protected].
0196-6553/$34.00
Copyright ª 2008 by the Association for Professionals in Infection
Control and Epidemiology, Inc.
doi:10.1016/j.ajic.2007.05.010
Prevention (CDC) definitions of health care–associated
infections.3
Data mining has developed as an important tool in
health care for converting enormous amounts of data
into powerful information to enhance surveillance, detect and assist with investigation of microbial clusters,
and support research.4,5 Because infection control surveillance is based on microbiology culture results and
patient admission information, the addition of a single
interface from the laboratory information system (LIS)
to an existing infection control database has the potential to significantly impact ICPs daily workload by
saving time and improve overall quality.
Whereas the cost of buying commercial data mining
software can be significant, the addition of a single LIS
interface to an existing infection control database provides similar capabilities for a minimal fee. Standard
reports from this automatic LIS data feed automate disease reporting, provide automated alerts for pertinent
microbiological events and early warnings, and allow
for onsite and offsite surveillance capabilities.
METHODS
Infection control database requirements
For effective transformation of an existing infection
control software system into one capable of data
S18
FLA 5.0 DTD ymic1203 11 March 2008 1:33 am ce 23
Dao et al
April 2008
mining, the current infection control database must
include the following: automatic rate calculations;
standard report formats; ease-of-use custom reportwriting capabilities; graphic functions; and an admit,
discharge, and transfer (ADT) interface to receive patient admission information in real time. The addition
of an LIS interface to receive preliminary and final microbiology data in real time is preferable. At minimum,
microbiology information should come across the
interface at least once a day (daily batch.)
Microbiology data requirements
Microbiology data received should be encounterbased and include the following: ordering physician,
specimen source, culture collection date and time, culture type, culture results, organism identification, and
collection location. Susceptibility patterns may be included or omitted if inclusion hinders or limits project
progression. Susceptibility is considered an added
benefit for advance data mining to further assist with
outbreak investigation but not a necessity for basic
data mining purposes.
Effective data transmission
If the sending system is compatible with free text,
data should be received as text to ensure all transmitted
data are received; however, terminology from a predefined dictionary in the receiving system is frequently
used for ease of report-writing for the nonclinical
system administrator. In this case, the sending system
administrator must successfully communicate all dictionary changes to the receiving system administrator.
For example, if the LIS adds ‘‘ventricular assist device’’
to its culture source dictionary, but the receiving system’s culture source dictionary is not updated, any cultures with ‘‘ventricular assist device’’ as a culture
source will not populate or will show as ‘‘null’’ because
that terminology was not recognized by the receiving
system. Traditionally, microbiology departments prefer
to document culture source as precisely as possible;
therefore, standard dictionary terms may be used in
conjunction with free text. In most cases, culture
source should be received as text.
Implementation team member requirements
The project team must include a microbiology data
expert, an infection control practitioner, LIS and infection control system administrators, and interface expert team members. The microbiology team member
must be an internal candidate who is familiar with
the microbiology department documentation processes. Project development may also benefit from having an external informatics consultant who serves as a
clinical–technical liaison.
S19
Allocation of enough resources and time to thoroughly build and test the interface before implementation is a necessity to ensure data accuracy and is critical
to project success. The receiving system administrator
must take an active role in validating interface transactions and periodically monitor for data integrity crossing the interface.
Microbiology culture data are unique and complex.
Unlike other laboratory test results, the combination
of different microorganisms, range of antibiotics and
susceptibilities create a multitude of possible combinations that continue to be a challenge for database designers and for information technology as a whole.
RESULTS
Capabilities of this infection control data base/LIS
data mining system include automatic rate calculation
and early detection of an increased rate through its
graphical functionalities. For example, this system automatically calculates and graphs positive wound,
blood, or urine cultures per 10,000 patient days or
inpatient admissions. These infection rates analyzed
with a statistical software packet such as QI Macro
can facilitate ‘‘early warnings’’ of a process out of control, identify significant variation in pattern of organisms, or signal a ‘‘pertinent’’ microbiological event
warranting further investigation.
Data mining is not just data processing. It is a systematic method to identify excessive rates or unusual
patterns of positive microbiology cultures based on patient location, culture source, and organism identification.6 The availability of culture results in electronic
format allows for onsite and offsite CDC definition–
based surveillance. Furthermore, the capability of a
data mining system to export data to Excel can streamline surveillance and increase turnaround time for ICPs.
Additionally, this system has the potential to produce
unit-specific multidrug-resistant organism rates and
health care–associated infection rates, used by nursing
and administration as a quality indicator marker.
Reportable conditions reporting to local and state
health departments can also be automated. Information from LIS in conjunction with patient information
from ADT is combined to generate this report. From
the ADT interface, patient demographics are merged
with infection-related data to provide records in an
electronic format for infection prevention research
purposes. In addition, the ability of the system to graph
data permits effortless visual interpretation of data.
DISCUSSION
Data mining is a powerful tool for ICPs, but data output is equivalent to data input; therefore, data quality is
FLA 5.0 DTD ymic1203 11 March 2008 1:33 am ce 23
S20
Dao et al
Vol. 36 No. 3 Supplement 1
a key requirement for data mining.1 Monitoring interface transactions is a standard component of any interface-related project. This identifies the number of
records rejected requiring further follow-up and is a
measure of completeness of data transfer. Additional
validation of data crossing the interface should be conducted periodically to ensure accuracy and comprehensiveness of the data transferred.
for busy ICPs. Early recognition is the key to prevention
of outbreaks, and weekly trending reports allow this.
Effective data mining permits microbial pattern recognition and detects the presence of microbial clusters
that warrants early investigation to enhance patient
safety and prevent costly outbreaks.
References
CONCLUSION
A data mining system created by the addition of a
single LIS interface to a standard infection control database has successfully been designed and implemented
at minimal cost to the hospital. This system meets data
mining needs of a health care system infection control
department that includes an 888-bed hospital and its
90-bed community hospital.
Data visualization with graphics is the most useful
form of data exploration. This data mining system is
capable of providing trending reports and detecting
sudden increases in specific pathogens using established queries. Ease of use is the key for data mining
success, and this system is extremely user-friendly
1. Stilou S, Bamidis P, Maglaveras N, Pappa S. Mining association rules from
clinical databases: an intelligent diagnostic process in healthcare.
Medinfo 2001;10:1399-403.
2. Obenshain M. Application of data mining techniques to healthcare data.
Infect Control Hosp Epidemiol 2004;25:690-5.
3. Scheckler W, Brimhall D, Buck AS, Farr BM, Friedman C, Garibaldi RA,
et al. Requirements for infrastructure and essential activities of infection control and epidemiology in hospitals: a consensus panel report.
Available from: http://www.apic.org/AM/Template.cfm?Section5Search
&section5Consensus_Reports&template5/CM/ContentDisplay.cfm&
ContentFileID5284. Accessed March 1, 2007.
4. Koh HC, Tan G. Data mining application in healthcare. J Healthc Inf
Manag 2005;19:64-72.
5. Berger AM, Berger CR. Data mining as a tool for research and knowledge development in nursing. Comput Inform Nurs 2004;22:123-31.
6. Schifman RB, Palmer RA. Surveillance of nosocomial infections by computer analysis of positive culture rates. J Clin Microbiol 1985;21:493-5.
FLA 5.0 DTD ymic1203 11 March 2008 1:33 am ce 23