=Paper=
{{Paper
|id=Vol-1515/poster1
|storemode=property
|title=Development of a discharge ontology to support postanesthesia discharge decision making
|pdfUrl=https://ceur-ws.org/Vol-1515/poster1.pdf
|volume=Vol-1515
|dblpUrl=https://dblp.org/rec/conf/icbo/WangC15
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==Development of a discharge ontology to support postanesthesia discharge decision making==
https://ceur-ws.org/Vol-1515/poster1.pdf
Development of a discharge ontology to support postanesthesia
discharge decision making
Lucy L Wang∗, Yong Choi
Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, 850
Republican St, Seattle, WA 98195, USA
ABSTRACT Many clinics have specified their own modified criteria for
Postanesthesia discharge decision making is a challenging postanesthesia discharge. We begin by assembling resources
process due to the high complexity and variability of care provided to published online by various surgical units. Among these resources,
postoperative patients. We built an ontology-based decision support many are based on the Aldrete scoring system, with additional
system that generates discharge recommendations for patients who modifications tailored to clinic-specific workflow. Criteria from
have undergone surgical procedures. Discharge decisions are made Stanford Hospital and Clinics, Loyola University Medical Center
based on patient vitals, symptoms, medical history and details and others are used to construct a global discharge rule set (Stanford
of the surgical procedure. The output recommendations of our Hospital and Clinics, 2010; Brown et al., 2008). The Phillips et al.,
system can aid healthcare providers in discharge decision-making 2011 systematic review of postanesthesia discharge protocols is also
and potentially reduce readmissions due to improper discharge. This used to determine levels of evidence for various scoring criteria.
project demonstrates the potential uses of ontologies in medical Scoring guidelines present in all or most resources we studied are
decision support systems, especially in areas that use specific included as criteria in our ontology-based decision support system.
scoring guidelines to aid decision-making. Based on the the criteria in these resources, we build a set of
SWRL rules, which in turn guides our development of an OWL
1 INTRODUCTION ontology. We first create a full set of postanesthesia discharge
criteria using information from our source documents. We then
Evidence-based discharge decision making and planning is a
translate these criteria into SWRL rule syntax to facilitate reasoning.
critical care process that can improve patient outcomes and reduce
Afterwards, we use these rules to guide the creation of OWL classes,
readmission rates. Inappropriate discharge can cause additional
as well as the definition of object and data properties.
pain and suffering for patients and their families and consume
The modified Aldrete score, for example, consists of five primary
unnecessary hospital resources (Anderson et al., 2011). For surgical
criteria: consciousness, respiration, circulation, movement and pain.
procedures, the risks associated with early discharge may be even
Some of these, such as respiration and circulation, can be broken
higher. When planning for discharge, healthcare professionals have
down further. For example, respiration consists of breathing quality,
to account for multiple variables such as age, vitals, comorbidities,
breath rate and oxygen saturation.
medications and social issues. A tool like the Aldrete scoring
Aldrete subscores, along with the total score, are used as criteria
system is commonly used to help healthcare professionals determine
for discharge. For each Aldrete subscore, a patient receives a score
when patients can be safely discharged (Aldrete, 1995). However,
on a 2 point scale, where 0 means a low functional level and 2
there are no standardized guidelines routinely used by healthcare
means a normal functional level. We create a data property that
professionals to assist them in making postoperative discharge
corresponds to each primary criteria. The value of this data property
decisions. A knowledge-based decision support tool based on
is determined using SWRL rules and assigned to a patient based
standardized procedures can enhance discharge decision making
on his/her current status in the system. An example data property
and reduce errors. In Bouamrane et al., 2010, the authors built an
hasAldreteScoreConsciousness may take on the values of 0, 1 or 2
ontology to model preoperative domain knowledge. In this paper,
if the patient is unresponsive, responsive but drowsy, and responsive
we use a similar approach to create a postoperative ontology-based
and fully alert respectively.
decision support system to assist discharge decision making.
Another example is the circulation subscore, where the patient’s
blood pressure must fall within a pre-specified range from the
2 METHODS baseline blood pressure. Within ± 20 mmHg yields a score of 2,
Our goal is to create an ontology to aid in post-surgery discharge within ± 20-50 mmHg yields a score of 1, and anything outside
decision-making. Following surgery, patients generally go from of that range yields a score of 0. These specific differences can
phase I postanesthesia care to phase II before being discharged be automatically calculated by our reasoner, which then assigns a
to home. Phase I care immediately follows surgery and involves score to the patient. This reduces the need for healthcare workers to
intensive monitoring of patient status. Phase II care is less intensive perform time-consuming numerical calculations.
and sees the patient recovering well from anesthesia. The goals An example SWRL rule for oxygen saturation is:
of our system are (i) to detect patients who may be suitable for
P atient(?pt), hasSpO2(?pt, ?SpO2), greaterT han(?SpO2, 95)
discharge, (ii) to determine the appropriate discharge workflow, and
(iii) to generate a list of additional recommendations for physicians. → hasAldreteScoreOxygen(?pt, 2)
which assigns 2 points to the Aldrete oxygen subscore if a
∗ To whom correspondence should be addressed: lucylw@uw.edu patient’s oxygen saturation is greater than 95%. The sum of points
Copyright c 2015 for this paper by its authors. Copying permitted for private and academic purposes 1
Choi and Wang
assigned to all Aldrete criteria is then calculated and used to work are the semantic plurality of clinical data representation and
determine whether a patient fits the basic criteria for discharge. If non-standard data exchange protocols between platforms.
a patient satisfies this condition, she/he is assigned into the class
DISCHARGE FROM PHASE I POSTANESTHETIC CARE.
Our ontology classifies patients for discharge from phase I to
phase II care, as well as from phase II care to home. Additionally,
our discharge ontology makes recommendations for healthcare
provider actions. For example, a patient receiving a sciatic block
may require clutches at discharge (Figure 1), or a patient with
high pain levels may require additional pain management. These
recommendations can be used by healthcare providers to prioritize
patient care and to generate discharge notes.
Fig. 2. Integrating information from electronic health records and patient
monitoring devices into the decision support ontology.
Another future direction is to expand our system to create
automated discharge summary notes to assist in the transition of
care. The discharge summary notes can be generated for two
groups of users: (1) healthcare professionals, and (2) patients and
caregivers. Discharge notes generated for healthcare professionals
can be used to facilitate continuity of care. Notes generated for
patients and caregivers can contain care instructions specifically
tailored to the patient to help guide them through the complex
post-discharge care process.
4 CONCLUSION
The discharge decision-making process relies on a set of predefined
Fig. 1. Example patient who is 30 minutes post surgery with stable vitals.
Output classifications based on SWRL rules are given.
clinical criteria that must be interpreted correctly to reach the
appropriate discharge decision. Our ontology integrates patient
3 RESULTS & DISCUSSION vitals, symptoms, and surgical and medical information, and outputs
recommendations for discharge and healthcare provider actions.
In this project, we demonstrated our work in building a knowledge-
This decision support tool could simplify postanesthesia discharge
based decision support system that generates decision support
procedures and may help reduce adverse events based on improper
recommendations to determine patient discharge eligibility after
or early discharge.
surgical procedures. We were able to model appropriate discharge
decision making in several example patients (Figure 1). In addition
to making the correct discharge decision, our system also generates
ACKNOWLEDGEMENTS
a list of recommendations for clinicians which should be followed This study was supported in part by National Library of Medicine
before the actual discharge. (NLM) Training Grant T15LM007442.
Our decision support system operated with a number of
limitations. First of all, the recommendations and guidelines issued REFERENCES
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2 Copyright c 2015 for this paper by its authors. Copying permitted for private and academic purposes