1. Information Systems Group, is.tm.tue.nl
A data driven approach to evaluate
guidelines for non-melanoma skin can-
cers (NMSCs)
T.C.P. (Tim) Kleinloog
Data driven evaluation of clinical guidelines
A wealth of data is registered by hospitals information systems (HISs)
nowadays. In this study a selection of the data is used to create
insights that could be used to support the clinical process and optimize
the guidelines studied. An approach is developed and tested. This
approach is displayed in figure 1. In the process modeling phase the
BPMN 2.0 language is used. To prepare the data, the statistical
programming language R is used. For compliance checking an
alignment based technique is used as described in [2]
Figure 1: Approach to perform data driven evaluation of clinical guidelines
Non-melanonma skin cancers (NMSCs)
NMSCs are carcinomas and represent the majority of skin cancers,
compromising basal cell carcinoma (BCC) and squamous cell
carcinoma (SCC). The worldwide incidence of NMSC has increased
markedly during the last decades [1]. Clinical guidelines have been
developed for NMSC treatment to assure the care quality and improve
patient safety. In this study we investigate whether it is possible to
improve these guidelines with the use of data currently available in the
Bravis hospital.
An application for NMSCs
After the clinical context is understood, the paper-based guidelines for
BCC and SCC are modeled in BPMN 2.0 process models:
Figure 2: Abstract BPMN 2.0 process models for the BCC en SCC guidelines
As presented by the “+” sign in the models, it is possible to “zoom in” at
different sub-processes.
Moreover, data behind the HIS and EPR is studied, extracted and
transformed in order to create event logs that describe patient behavior
relevant for the guidelines studied.
Figure 3: Log and patient data as required after data preparation
Relevant statistics about patients diagnosed with BCC or SCC:
Figure 4: Histograms for the age and recurrency of tumours for patients diagnoses BCC
and SCC
One major problem that could not be avoided during the application of
this approach is that patient behavior for one diagnosis (guideline)
could not be isolated in an automated way. Many patients are treated
for more diseases at the same time. Activities are not always unique
for one diagnosis. When compliance checking with the current dataset
was applied: for BCC, 42% of all events that are expected to happen
according to the guidelines are skipped during diagnosis and 96% of
all events that are expected to happen according to the guidelines are
skipped during treatment/follow-up. For SCC, respectively 47% and
44% are skipped.
One should be careful while interpreting these numbers. The results
do give an indication that and where patient behavior is often different
than described in guidelines.
References
[1] S. van der Geer-Rutten. Disease Management for Chronic Skin
Cancer. PhD thesis, Erasmus University Rotterdam, April 2012.
[2] H. Yan, P.M.E van Gorp, U. Kaymak, X. Lu, R. Vdovjak, H.H.M.
Korsten, and H. Duan. Analyzing conformance to clinical
protocols involving advanced synchronizations. In Bioinformatics
and Biomedicine (BIBM), 2013 IEEE International Conference on
Bioinformatics and Biomedicine, pages 61–68, 2013.
/ department of Industrial Engineering & Innovation Sciences