COMPUTERIZED RADIOLOGY SCHEDULING SUPPORT USING A RULES ENGINE Simon Rascovsky, MD, MSc, Gabriel Castrillón, Catalina Bustamante, Juan F. Llano, MD, Andres Arango, Jorge A. Delgado, MD Problem statement! Computerized physician order entry (CPOE) systems in radiology have gained a lot of interest in recent years as a way to streamline the process of radiology order entry and aid the referring clinician in requesting the appropriate procedure for the patient’s clinical presentation. However, there has been far less interest in optimizing the next step in the radiology workflow; procedure scheduling.
!
Scheduling radiology procedures is a central process in almost all radiology departments, which is often considered too complex for full automation, and in most cases requires an experienced human operator. The scheduler’s job can be very complex, as this person has to juggle radiology business rules, human and technical resource limitations, and patient preferences in order to efficiently schedule a radiology procedure.
!
It is also common for the scheduler to be the person responsible for informing the patient or caregiver of the appropriate preparations required for the procedure, and these preparations vary widely and are often dependent on the patient’s clinical status.
!
All of this complexity is often amplified for outpatient scheduling via telephone appointments, as the patient requesting the exam expects the scheduling to be performed immediately and during the relatively short timespan of a telephone call.
!
Many rules engine systems are based on the ‘Rete' algorithm (Fig.1), a pattern matching algorithm for implementing production rule systems. Contrary to use several if conditions, they are replaced by using a network, the ‘Rete'. The network begins with the parameters to the rules, and by matching of conditions, the algorithm stops when they reach the rule consequences in the network. this algorithm is highly performant, capable of evaluating hundreds or even thousands of rules in near real time.
Radiology scheduling is a good example of complex business rules that can be efficiently implemented with the aid of rules engines, which are software systems designed to provide decision tree processing in a computerized environment. These systems can aggregate a number of rules and according to simple inputs can output the correct business logic outcome, taking into account conflicts or incompatibilities between the different rules.
!
Proposed solution! At first, the solution to the problem of matching business rules to computational logic structures seems simple; aggregate those business rules as a set of if..then control structures. This works well, and is simple to execute for a few simple rules. The problem arises when there are conflicting rules with multiple conditions and several levels of nesting. Control structure nesting quickly becomes unmanageable and performance falls dramatically with increased number of rules. The common solution to this problem is the use of a ‘Rules Engine’.
!
Rules engines Rules engines are software systems designed to execute business rules in a software environment to produce new knowledge. There are several different kinds of rules engines, but they can generally be divided into inference and event/action engines. Inference engines take predetermined rules commonly in the form of if…then statements. Event/action engines take incoming data streams and generally trigger alerting systems or additional processes. For the scheduling use case, where rules are known beforehand, conditions are well defined and decisions are required for each discrete scheduling episode, an inference engine is an appropriate
Materials and methods! A through evaluation of the scheduling process in our institution was performed, identifying all the rules and constraints required for procedure scheduling, as well as the different preparation informative texts that need to be communicated to the patient prior to the procedure.
!
Overall, 84 preparation ‘scripts’ were identified (Table.1). These scripts were divided into two groups: “Appointment Criteria” and “Patient Instructions”. The required inputs were identified both for the patient’s condition (Table.2) and the requested study (Table.3).
Our implementation of a rules engine for radiology scheduling aims to provide an example of the use of these technologies as applied to the radiology workflow aiding a small and very specific problem, the complexity of scheduling rules, in order to quickly determine both the scheduling constraints and the patient recommendations necessary during a telephone appointment request at our institution. It is expected that this system will help reduce the time needed to schedule a procedure, as well as reduce scheduling and communication errors by presenting both scheduling constraints and patient preparation instructions in an easy to interpret format that does not rely on the human scheduler’s memory or experience.
!
!
Web services In order to expose the rules engine to scheduling systems, an Application Programmer Interface (API) is often preferred, in order to facilitate integration with diverse systems. Modern API’s are increasingly using current web technologies to further enhance their ease of deployment and communication. These web services use HTTP communication protocols with data structures such as Extensible Markup Language (XML) or JavaScript Object Notation (JSON) to consume and send information. The use of a JSON-based web service to expose the rules engine ensures Table 1: Business Rules for study scheduling that the development of interfaces will be facilitated, due to the large number of HTTP and JSON libraries available for almost any programming language.
If patient is claustrophobic and requesting for MRI, schedule sedation
Don't schedule inpatients, studies requiring sedation, or children as last studies for a site
Wait one week to schedule MSK MRI's if patient has had infiltrations on site to be imaged
!
Figure 1: 'Rete' algorithm
system to address the problem. Inference rules engines are particularly well suited for problems where the business-logic changes often and the problem is beyond any algorithmic solution or is not fully understood.
Ask patient to come for test MRI protocol if he/she has metal retainers and scheduled for Head/Brain MRI
For breast MRI, ask for date of last mesntrual period, schedule MRI at the beginning of the second week of menstrual cycle If study is fMRI, coordinate with research office, assign 1.5 hours and request for all previous studies Request cardiac anesthesia if Cardiac MRI and patient age < 10 yo
If Prostate MRI requested, prostate biopsy must not have been performed < 6 weeks prior
Studies with codes 883101.11, 883101.39, 883102, 883440.1, 883512.23 must only be scheduled in MR3 suite If Prostate MR requested, add 30 min to scheduled time due to spectroscopy Patients over 70 yo that request sedation should be scheduled for anesthesiologist-guided sedation Studies with codes 883101.39 or 883401.8 require additional time for preparation before exam. Must arrive 1:30h before Schedule all functional MR Urographies in coordination with anesthesiologist Brain + CSF dynamic study? Add 15 min to scheduled time
Table 2: Patient criteria name: Patient's Name age: Patient's age
Wait 10 days for new barium based contrast
sex: Patient's sex
If renal protection required, patient must arrive 2 hours prior
weight: Patient's weight
Follow allergy protocol if stated allergies to contrast media Add 15 min to scheduled time for suite disinfection if patient in contagion isolation
height: Patient's height
Abdomen CT patients must arrive 1 hour before exam
hospitalized: Is patient hospitalized? (Inpatient)
Note Viagra intake if study = coronary angioCT If study requires contrast, patient must bring serum creatinine. Note weight and height for GFR calculation
diabetes: Is the patient diabetic?
If direct MR arthrography and patient on anticoagulation, patient must suspend anticoagulation 5 days prior, with approval from physician
allergies: Does the patient have documented allergies to contrast media?
If female patient scheduled for head/brain MR, must not use makeup Enema required for Pelvic MR studies If patient has renal insufficiency, must bring recent serum creatinine result If patient on chemotherapy, must bring recent serum creatinine result If patient on radiotherapy, must bring recent serum creatinine result If patient is older than 60 yo, must bring recent serum creatinine result If patient is diabetic, must bring recent serum creatinine result If abdominal MR study, must not eat food 4 hours prior If study requires sedation, patient must come accompanied
claustrophobia: Is patient claustrophobic? recent_surgery: Has the patient had recent surgery? infiltration: Has the patient had any infiltrations/infusions/injections on body part to be imaged? previous_contrast: Has the patient had recent contrast media administration? renal: does the patient have renal insufficiency/failure? chemo: Is the patient on chemotherapy?
If study requires sedation, patient must not drive to the appointment
radio: Is the patient on radiotherapy?
If study requires contrast, and patient is not diabetic, patient must not eat for 6hrs prior
transplant: Is the patient a transplant recipient?
If study requires sedation, and patient is not diabetic, patient must not eat for 6hrs prior If study requires anesthesia, and patient is not diabetic, patient must not eat for 6hrs prior
contagious: Is the patient under contagion isolation?
If study requires contrast, and patient is diabetic, patient must not eat for hrs prior and eat light foods beforehand
mental_disc: Does the patient suffer from any mental discapacity?
Fetal MRI studies must bring written authorization from OB/GYN Patient must not take metformin 24h prior and 48hrs after exam
metformin: Does the patient take metformin?
If CT with contrast, must not eat 6hrs prios, come accompanied, and must not suspend medication
anticoagulants: Is the patient on anticoagulants?
If patient is diabetic, must apply insulin as scheduled prior to appointment, and bring medication to be applied on site. Must also not eat 4 hrs prior Rule out pregnancy If patient has mental discapacity, must come accompanied by parent or guardian and arrive 30 min earlier for appointment Children must arrive 30 min earlier and accompanied by parent or guardian Children must not eat 4hrs prior to study and sleepy/tired (for better results with sedation/chloral hydrate administration)
viagra: Is the patient taking viagra? metal: Does the patient have any metal in his/her body? brackets: Does the patient wear metal dental retainers?
CT Studies require additional time for billing purposes Must bring a list of concomittant medications if studies 881234.5 or 896100
Remind patients to bring comfortable clothes for studies 881234.1, 881234.2, 881234.5, 881234.6, 895001
Table 3: Study criteria
Reinforce that patients can resume normal diet and activities unless physician says otherwise for studies 881234.5 and 896100
name: Study name (description)
Cardiac MR studies must not eat 4 hrs prior
code: Study code
Remind patients not to bring any jewelry for studies 884103, 894102 or 896100
Don't consume caffeine for study 881234.5 Inpatients scheduled for Transesophageal US must not consume food 2hrs after study
modality: Study modality
Must come accompanied for studies 881235 and 884103
bodypart: Body part to be imaged
For cardica stress test, patient must bring sports attire and not eat 4hrs prior Cardiac monitoring patients must take a bath prior to monitoring, as the may not do so with the monitor in place. They cannot use an electric blanket. For tilt-test, light foods prior to exam, comfortable clothing and no makeup Light foods before abdominal CT For UroCT, must drink plenty of fluids and not void prior to exam For coronary angioCT, must not consume caffeine, cigarretes or chocolate before exam
contrast: Is contrast required? renal_protection: Is renal protection requested? sedation: Is sedation requested? anesthesia: Is anesthesia requested?
Figure 2: Sample Drools DRL rule
JBoss Drools Expert (http://www.jboss.org/drools/droolsexpert.html) was chosen as the inference engine platform for implementing the scheduling support system. Each script was converted to a ‘rule’ using the Drools ‘drl’ declarative language which provides for simple, ‘english-like’ rule declaration in code (Fig.2). Due to identified duplication, only 22 appointment criteria scripts and 38 patient instruction scripts were converted to rules in code.
!
The rules engine was made available to our scheduling system as a web service, that received a JSON structure with the available inputs (Fig.3), and processed them accordingly, generating output as a JSON encoded list of appointment criteria and patient instructions that were shown to the scheduler after all inputs have been filled in (Fig.4). The input interface to this web-service is currently in the process of being integrated with our scheduling system.
!
Discussion! Radiology scheduling is a decision process that involves logistical, technical, workflow and time constraints. It is common for the balancing of these constraints to be left solely to the human performer, who has to rely on (often conflicting) information from many sources to schedule radiology procedures efficiently.
From a technical standpoint, Drools seems to be the most well documented, open source rules engine available, and provides not only the required functionality but also the possibility of extending this project from a specific rules engine to a complete sub-process with BPM tools. Additionally, web services are steadily becoming the preferred way of providing added functionality to existing RIS/HIS/PACS systems due to their modularity and ease of interface development, and the platforms selected support exposing the rules engine in such a way.
!
Conclusion! Many complex decisions take place throughout the radiology workflow that can benefit from decision support systems, which need not be limited to purely clinical applications. Efficient and precise scheduling of radiology procedures requires the scheduler to take into account many clinical and business rules, and this process can be streamlined through the use of rules engines.
!
References! 1. Greenes RA, Sordo M, Zaccagnini D, Meyer M, Kuperman GJ. Design of a standards based external rules engine for decision support in a variety of application contexts: report of a feasibility study at Partners HealthCare System. Medinfo. 2004;2004:611–615.
2. Rosenthal DI, Weilburg JB, Schultza T et al. Radiology Order Entry With Decision Support: Initial Clinical Experience. College of Radiology/Vol. 3 No. 10 October 2006
3. Rosenthal DI. Order Entry in Radiology. In PACS: A Guide to the Digital Revolution. 2006, pp 483493
4. Amaral CS, Rozenfeld H, Costa JM, MdeF Magon, Mascarenhas YM. Improvement of radiology services based on the process management approach. Eur J Radiol 2011;78(3):377–383.
5. Zhengxing H, Xudong L, Huilong D, Mining association rules to support resource allocation in business process management, Expert Systems with Applications, Volume 38, Issue 8, August 2011, Pages 94839490, ISSN 09574174, http://dx.doi.org/10.1016/j.eswa.2011.01.146.
6. Reichert, M (2011) What BPM Technology Can Do for Healthcare Process Support. In: 13th Conf. on Artificial Intelligence in Medicine (AIME'11), July 2011, Bled, Slovenia.
7. Zhang J, Lu X, Nie H, Huang Z, van der Aalst WM. Radiology information system: A workflow based approach. Int J Comput Assist Radiol Surg. 2009;4:509– 16.
!
Although healthcare has had extensive experience with decision support trees and rules engines, it has generally been limited to clinical decision support and electronic guidelines for diagnosis and treatment. The business/workflow applicability of these tools has been addressed only occasionally in general healthcare IT, but far more extensively in other industries, with tools that fall under the category of Business Process Management (BPM). Rules engines are part of an ecosystem of business process management tools that include workflow management, event processing/temporal reasoning and automated planning engines, among others.
!
Figure 4: Sample JSON response
! Acknowledgements: Special thanks to Radesh Mekatukatil Mohan for his invaluable help.
Sample code is available at https://github.com/IATM/indications-api Figure 3: Sample JSON request
Instituto de Alta Tecnología Médica - IATM! Medellín, Colombia! investigació
[email protected]
