Leveraging Data Quality to Better Prepare for Process Mining: An Approach Illustrated Through Analysing Road Trauma Pre-Hospital Retrieval and Transport Processes in Queensland
- discover the range of different care and transport processes undertaken for road trauma patients from roadside to definitive care for cohorts of incidents, patients and transports identified from the guidelines;
- conduct conformance (to guidelines) and comparative performance analyses for the discovered care and transport variants;
- identify key factors influencing deviance from standard care and delivery processes as given in the guidelines (e.g., patient demographics, patient injury types, mechanisms of injury), geospatial factors (of crash location and trauma facilities), responder characteristics (road vs aeromedical, paramedic/clinician attended crashes), etc.
- a contribution to the knowledge of how to establish a process mining study with particular emphasis on systematically identifying data-related issues prior to carrying out a process mining analysis;
- demonstration of how data quality issues manifest in process discovery and conformance analyses;
- a practical demonstration of the approach through a case study resulting in:
- conceptual data models (Object-Role Models (ORM)) of data held by (i) a ground based ambulance service provider (QAS) and (ii) a coordinator of aero-medical retrieval and transport service provider (RSQ);
- an assessment of the quality (fitness for purpose) of the QAS and RSQ data for process mining analysis;
- the derivation of context factors likely to impact on the study from literature regarding multiple aspects of pre-hospital retrieval and transport by both ground and aero-medical transport modes;
- a contribution to the knowledge-base in relation to ground emergency services (GEMS) and helicopter emergency services (HEMS) dispatch processes in an Australian context.
2. Related Literature
2.1. Process Mining and Healthcare
2.2. Process Mining Methodologies
- Step 1
- Log preparation in which the event log is extracted from the organisation’s information systems.
- Step 2
- Log inspection from which a basic understanding of the process is developed.
- Step 3
- Control flow analysis which involves either checking that the event log conforms to an existing process description/model or automatically discovering a process model from the log.
- Step 4
- Performance analysis including discovering bottlenecks and calculating processing times.
- Step 5
- Role analysis which provides information on the division of work within the organisation (as it relates to the process being analysed).
- Step 6
- Transfer of results to process owners in such a way as they understand the outcomes, thus allowing the organisation to implement process changes.
- Step 0
- Justification and planning to clearly outline the reasoning behind the study and to identify resources required for the study.
- Step 1
- Extract domain knowledge (from domain experts and historical data) to (i) develop an understanding of the domain and of the data available for analysis and, (ii) generate artifacts such as hand-made models, objectives and questions.
- Step 2
- Create control-flow model and connect to event log using automated discovery techniques.
- Step 3
- Create integrated process model by extending to other perspectives
- Step 4
- Provide operational support based on insights derived from earlier stages.
- Step 1
- Scoping Identify the process and gather basic knowledge; Determine the objectives of the project; Determine the required tools and techniques.
- Step 2
- Data understanding Locate the required data in the system’s logs; Explore the data in the system’s logs; Verify the data in the system’s logs
- Step 3
- Event log creation Select the dataset in terms of event context, timeframe and aspects; Extract the set of required data; Prepare the extracted dataset, by cleaning, constructing, merging and formatting the data
- Step 4
- Process mining Get familiar with the log by gathering statistics; Make sure that the process contained in the event log is structured enough to apply the required process mining techniques; Apply process mining techniques to answer business questions
- Step 5
- Evaluation Verify the modelled work; Validate the modelled work; Validate the modelled work; Decide on an elaboration of the process mining project
- Step 6
- Deployment Identify if and how the process can be improved by improvement actions; Present the project results to the organization
- Step 1
- Planning sets up the project and establishes research questions.
- Step 2
- Extraction identifies and extracts relevant data and, optionally, process models (if they exist). This stage is informed by the research questions identified earlier.
- Step 3
- Data processing has to do with creating event logs from the data specific to individual research questions.
- Step 4
- Mining and analysis applies process mining techniques to the event logs and aims to answer research questions and gain insights into process performance and compliance.
- Step 5
- Evaluation relates the analysis findings to improvement ideas that achieve the project’s goals.
- Step 6
- Process improvement and support implements the change notions derived in the previous stage and provides support by using process mining techniques to detect problematic running cases, predicting outcomes of running process instances or suggesting change actions for running cases.
2.3. Context Factors Relevant to EMS Operations and Patient Outcomes
3. Our Approach to Project Establishment and Assessing Data Quality
- Step 1
- Process Understanding develop an understanding of the process to be investigated through interviews with stakeholders, from existing process models and standard operating procedures and manuals.
- Output—High level ‘as-is’ process model
- Benefits—The model provides a point of contact to communicate, to the process owners, our understanding of the current process. The model also reveals various control-flow considerations such as event-ordering relationships.
- Step 2
- Data Understanding develop an understanding of the data available from all available sources, e.g., data dictionaries
- Output—Conceptual data model/s
- Benefits—The conceptual data models provide an abstraction/idealisation of the actual data and are thus free from dealing with implementation related issues such as correlation issues across multiple information systems, data entry errors, noise, etc. Conceptual models also include cardinality relationships and constraints between data elements useful in event log construction.
- Step 3
- Data Attribute Quality quantify the quality of the sample data, at the attribute level, over several dimensions
- Output—n x m matrix of n attributes and m quality dimensions where each cell stores a [0..1] value representing the quality of an attribute in the relevant dimension according to the metric used. For instance, the completeness dimension measures the fraction of records for which a given attribute has a value recorded.
- Benefits—Each quality measure can be used to (i) determine the suitability for an attribute’s inclusion/role in an event log, or (ii) anticipate the manifestation of certain structures or behaviours in process models derived form the data. For instance, an attribute that has a high uniqueness value would be unsuitable for inclusion in an event log as an activity label (discovered models would be highly complex). Mixed levels of numeric precision across attributes of datetime data type would lead us to anticipate event ordering issues in discovered process models (at least potentially unnecessary parallelism).
- Step 4
- Event Log Preparation
- Output—Event log for use in pre-study process mining
- Benefits—The inclusion of data attributes and the assignment of same to event log attributes is informed by the quality values.
- Step 5
- Event Quality assess the quality of the event log derived in the previous step from the sample data, at the event level, by checking for the presence of Event Log Imperfection patterns 
- Output—List of Event Log Patterns and whether or not the patterns exist in the data
- Benefits—Identification of the existence of one or more of the Event Log Imperfection patterns points immediately to the associated quality issue/s and impacts on process mining described, for each pattern in .
- Step 6
- Pre-study Process Mining conduct initial process mining analyses (e.g., discovery, conformance)
- Output—At least, discovered process models
- Benefits—Allows checking for the presence of the structures/behaviours anticipated from the discovered quality issues. Conformance checking can reveal the extent of the issues in the log derived in Step 4.
- Step 7
- Evaluation and Feedback use the identified quality issues and quality metrics to:
- inform our understanding of the process being investigated;
- revise, with process owners, questions to be investigated in the process mining study, data to be used in the study; and
- ultimately, guide event log preparation for the study proper.
4. Illustration of Our Approach
- gain domain knowledge and an understanding of Queensland Ambulance Service’s notification-dispatch-retrieval-transport processes including articulation with Retrieval Service Queensland;
- gain an understanding of QAS and RSQ data through development of data models and examination of sample data extracts;
- conduct data quality assessment of each data set;
- prepare event logs relevant to the study aims;
- use the sample data to discover models of the individual QAS and RSQ retrieval/transport processes; and
- evaluate/conformance check the models.
4.1. Process Understanding—High-Level Patients Retrieval/Transport Process in Queensland
- Injuries including (i) all penetrating injuries, (ii) significant blunt injuries to head, neck, chest, abdomen, pelvis, or axilla, (or injuries to multiple regions (iii) limb amputation, (iv) spinal chord injuries, (v) pelvis or lower limb compound fractures
- Mechanisms of Injury including (i) vehicle rollover, (ii) ejection from vehicle, (iii) fatality in same vehicle, (iv) impact >30 kph (motorcyclist) or >60 kph (other), (v) pedestrian impact, (vi) prolonged extrication
- Vital Signs (i) Resp. rate, (ii) Oxygen saturation, (iii) Systolic BP, (iv) Pulse rate, (v) Glascow Coma Score (with threshold values according to patient age
- GEMS only, single/multiple patient, single/multiple response units dispatched, patient/s treated at scene, transport not required.
- GEMS only, single/multiple patient, single/multiple response units dispatched, patient/s treated at scene, transport provided by at least one response unit for at least one patient.
- HEMS primary response involving transport of a patient to closest hospital or, a regional trauma centre or, a major trauma centre.
- HEMS IFT (Inter-facility Transport) involving transport of a trauma patient initially delivered to closest hospital or a regional trauma centre who after some period, now requires transport to a major trauma centre.
- Fixed-wing primary response involving transport of a patient to closest hospital or, a regional trauma centre or, a major trauma centre.
- Multi-mode (GEMS + HEMS or Fixed-wing aero-medical) transport of trauma patient to closest hospital or, a regional trauma centre or, a major trauma centre.
4.2. Data Understanding—Conceptual Data Models Relevant to Ground and Aero-medical Retrieval/Transport
- Incident data such as location of the incident, notification datetime the incident was reported to the emergency call centre and the priority of the incident.
- Patient data including patient name, age, gender, pre-existing conditions, allergies, current medications and indigenous status.
- Transport data which includes timestamped way-point data representing key case milestones, details of assessment of the scene, patient and injury by the paramedics, observations of the patient, management activities and procedures carried out by the ground-based paramedics or aircraft medical team, the destination hospital, and the patient outcome.
4.3. Data Attribute Quality Assessment
4.3.1. QAS Sample Data
- the Completeness metric shows that only 4 of the datetime columns are 100% complete which indicates that in any incident, not all patient and vehicle waypoints are completed. In particular, the 50% complete value for OFF_STRETCHER_VACIS indicates that only half the patients involved in incidents required transport to hospital. (We note that this may include missing data for transported patients, i.e., the paramedics did not manually record the timestamps in the record.)
- the Precision metric (for datetime) values gives an indication of mixed granularity among the various timestamps.
- the Uniqueness metric gives an indication of the degree of distinct values found in the column. The FIRST_ASSIGNED_CAD value shows low Uniqueness indicating many repeated values. This reflects the QAS policy of assigning to all vehicles involved in an incident, the timestamp of the first vehicle assigned to attend the incident.
4.3.2. RSQ Sample Data
- the Completeness metric shows that all values are populated for the date time columns, while only 25% of the records in the log have a value for the MECHANISM_OF_INJURY column;
- the Precision metric (for datetime) values shows generally uniform granularity among the timestamps. Only the DATE_RETRIEVAL_REQUESTED column is day-level granularity, while all other other datetime columns are at minute-level granularity.
- the Uniqueness metric gives an indication of the degree of distinct values found in the column. The DATE_RETRIEVAL_REQUESTED value shows low Uniqueness indicating many repeated values. This is not surprising given the narrow range of case dates (many cases on any given day). The SOURCE_ID column shows perfect uniqueness (every value different from all others), while Uniqueness value of 27% for the MECHANISM_OF_INJURY column is reflective of the value being populated from a limited set of allowed values (e.g., a pull-down on a form).
4.4. Pre-Study Process Mining Analysis
4.4.1. QAS Process Discovery and Conformance
4.4.2. RSQ Process Discovery and Conformance
5. Discussion and Lessons Learned
- Identifying event-data quality issues allows for the anticipation of certain observable features in subsequent process mining analysis. For instance, the mixed granularity in timestamps apparent in the quality assessment (different values for the Precision metric) led the analysts to anticipate incorrect event ordering being an issue (subsequently confirmed by the parallelism apparent in the discovered models). Furthermore, the coarse granularity of the (RSQ) DATE_RETRIEVAL_REQUESTED values (all are at day-level granularity) precludes the possibility of properly assessing performance aspects of some phases of aero-medical retrieval (for instance, the time taken to activate a medical team following a retrieval request cannot be assessed as the retrieval request includes only a date with no time information). For the ground-based retrieval/transport data, the quality analysis showed duplication in the FIRST_ASSIGNED_CAD values. After discussion with QAS it emerged that it is QAS practice to include, for all response units dispatched to attend an incident, the same value for FIRST_ASSIGNED_CAD. From a QAS perspective, this allows assessment of time between incident notification and first response to the incident. As such, this is not a data quality issue (as it is done purposefully by the process owner) and can be taken into account by making this a case attribute. Identifying this issue through quality assessment headed-off issues that may have arisen in the process mining analysis had the FIRST_ASSIGNED_CAD milestone been included as an activity for all eARFs and response units involved in the incident.
- Quantifying quality issues means that it is possible to separate systemic from occasional quality ‘breaches’. For instance, the fact that all (QAS) _VACIS timestamps were at a low level of precision (i.e., minute-level granularity) points to a systemic cause.
- Identifying quality issues allows for reasoning about the mechanisms that may have caused the quality issue to be present in the event data. For instance, it is unlikely that all (QAS) _VACIS events happened exactly on the minute, but, it is likely that either the system recording the event had only minute-level precision or that in extracting the data for analysis, seconds and milli-seconds were ‘masked’. The fact that some (RSQ) cases have ARRIVE_AT_RECEIVING_HOSPITAL and DEPART_RECEIVING_HOSPITAL occurring at the same times may indicate a combination of human and system issues, i.e a human omission to record the ARRIVE time when the aircraft arrives (possibly due to patient care needs), and a system requirement that an ARRIVE time needs to be entered before a DEPART time can be entered.
- An understanding of 2 and 3 above facilitates informed engagement with process stakeholders and decisions about data quality remediation actions. For instance, if the _VACIS granularity issues were as a result of incorrect data extraction, this quality issue can be resolved by simply extracting the data at the appropriate granularity.
Conflicts of Interest
- Rojas, E.; Munoz-Gama, J.; Sepúlveda, M.; Capurro, D. Process Mining in Healthcare: A Literature Review. J. Biomed. Inform. 2016, 61, 224–236. [Google Scholar] [CrossRef] [PubMed]
- Yang, W.; Su, Q. Process mining for clinical pathway: Literature review and future directions. In Proceedings of the 11th International Conference on Service Systems and Service Management (ICSSSM), Beijing, China, 25–27 June 2014; pp. 1–5. [Google Scholar]
- Botsis, T.; Hartvigsen, G.; Chen, F.; Weng, C. Secondary use of EHR: Data quality issues and informatics opportunities. Summit Transl. Bioinform. 2010, 2010, 1. [Google Scholar]
- Feder, S.L. Data Quality in Electronic Health Records Research: Quality Domains and Assessment Methods. West. J. Nurs. Res. 2018, 40, 753–766. [Google Scholar] [CrossRef]
- Andrews, R.; Wynn, M.T.; Vallmuur, K.; ter Hofstede, A.H.; Bosley, E.; Elcock, M.; Rashford, S. Pre-hospital Retrieval and Transport of Road Trauma Patients in Queensland: A Process Mining Analysis. In Proceedings of the International Workshop on Process-Oriented Data Science for Healthcare 2018 (PODS4H18), Sydney, Australia, 9–14 September 2018. [Google Scholar]
- Group, T.P.W. A Trauma Plan for Queensland; Queensland Government: Brisbane, Australia, 2006.
- FitzGerald, G.; Tippett, V.; Schuetz, M.; Pollard, C. The Queensland Trauma Plan project. ANZ J. Surg. 2008, 78, 780–783. [Google Scholar] [CrossRef] [PubMed]
- Wirth, R.; Hipp, J. CRISP-DM: Towards a Standard Process Model for Data Mining. In Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining (PAKDDM), Manchester, UK, 11–13 April 2000; pp. 29–39. [Google Scholar]
- Mans, R.S.; van der Aalst, W.M.; Vanwersch, R.; Moleman, A. Process Support and Knowledge Representation in Health Care. LNCS 2013, 7738, 140–153. [Google Scholar]
- Andrews, R.; Suriadi, S.; Wynn, M.; ter Hofstede, A.H. Healthcare Process Analysis. In Process Modelling and Management for HealthCare; CRC Press: Boca Raton, FL, USA, 2017. [Google Scholar]
- Bose, R.J.C.; Mans, R.S.; van der Aalst, W.M. Wanna Improve Process Mining Results? In Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Singapore, 16–19 April 2013; pp. 127–134. [Google Scholar]
- Fox, F.; Aggarwal, V.R.; Whelton, H.; Johnson, O. A Data Quality Framework for Process Mining of Electronic Health Record Data. In Proceedings of the IEEE International Conference on Healthcare Informatics (ICHI), New York, NY, USA, 4–7 June 2018; pp. 12–21. [Google Scholar]
- Lamine, E.; Fontanili, F.; Di Mascolo, M.; Pingaud, H. Improving the Management of an Emergency Call Service by Combining Process Mining and Discrete Event Simulation Approaches. In Proceedings of the Working Conference on Virtual Enterprises; Springer: Berlin, Germany, 2015; pp. 535–546. [Google Scholar]
- Badakhshan, P.; Alibabaei, A. Using Process Mining for Process Analysis Improvement in Pre-Hospital Emergency. In Proceedings of the Middle East North Africa Conference for Information Systems, Paris, France, 22–23 March 2018. [Google Scholar]
- Măruşter, L.; van Beest, N.R. Redesigning Business Processes: A Methodology Based on Simulation and Process Mining Techniques. Knowl. Inf. Syst. 2009, 21, 267. [Google Scholar] [CrossRef]
- Bozkaya, M.; Gabriels, J.; van der Werf, J.M. Process diagnostics: A method based on process mining. In Proceedings of the International Conference on Information, Process, and Knowledge Management, (eKNOW’09), Cancun, Mexico, 1–7 February 2009; pp. 22–27. [Google Scholar]
- Rebuge, Á.; Ferreira, D.R. Business process analysis in healthcare environments: A methodology based on process mining. Inf. Syst. 2012, 37, 99–116. [Google Scholar] [CrossRef][Green Version]
- Van der Aalst, W.; Adriansyah, A.; De Medeiros, A.K.A.; Arcieri, F.; Baier, T.; Blickle, T.; Bose, J.C.; Van Den Brand, P.; Brandtjen, R.; Buijs, J.; et al. Process mining manifesto. In Proceedings of the International Conference on Business Process Management; Springer: Berlin, Germany, 2011; pp. 169–194. [Google Scholar]
- Van der Heijden, T. Process Mining Project Methodology: Developing a General Approach to Apply Process Mining in Practice. Master’s Thesis, School of Industrial Engineering, Technische Universiteit Eindhoven, Eindhoven, The Netherlands, 2012. [Google Scholar]
- Van Eck, M.L.; Lu, X.; Leemans, S.J.; van der Aalst, W.M. PM2: A Process Mining Project Methodology. In Proceedings of the International Conference on Advanced Information Systems Engineering; Springer: Berlin, Germany, 2015; pp. 297–313. [Google Scholar]
- Cho, M.; Song, M.; Yoo, S. A Systematic Methodology for Outpatient Process Analysis Based on Process Mining. In Proceedings of the Asia-Pacific Conference on Business Process Management; Springer: Berlin, Germany, 2014; pp. 31–42. [Google Scholar]
- Fernandez-Llatas, C.; Lizondo, A.; Monton, E.; Benedi, J.M.; Traver, V. Process Mining Methodology for Health Process Tracking Using Real-time Indoor Location Systems. Sensors 2015, 15, 29821–29840. [Google Scholar] [CrossRef]
- Rojas, E.; Sepúlveda, M.; Munoz-Gama, J.; Capurro, D.; Traver, V.; Fernandez-Llatas, C. Question-driven Methodology for Analyzing Emergency Room Processes Using Process Mining. Appl. Sci. 2017, 7, 302. [Google Scholar] [CrossRef]
- Johnson, O.A.; Dhafari, T.B.; Kurniati, A.; Fox, F.; Rojas, E. The ClearPath Method for Care Pathway Process Mining and Simulation. In Proceedings of the International Conference on Business Process Management; Springer: Berlin, Germany, 2018; pp. 239–250. [Google Scholar]
- Abe, T.; Takahashi, O.; Saitoh, D.; Tokuda, Y. Association Between Helicopter with Physician Versus Ground Emergency Medical Services and Survival of Adults with Major Trauma in Japan. Crit. Care 2014, 18, R146. [Google Scholar] [CrossRef]
- Leeuwenburg, T.; Hall, J. Tyranny of Distance and Rural Prehospital Care: Is There Potential for a National Rural Responder Network? Emerg. Med. Australas. 2015, 27, 481–484. [Google Scholar] [CrossRef]
- Starnes, A.; Oluborode, B.; Knoles, C.; Burns, B.; McGinnis, H.; Stewart, K. Direct Air Versus Ground Transport Predictors for Rural Pediatric Trauma. Air Med. J. 2018, 37, 165–169. [Google Scholar] [CrossRef] [PubMed]
- McDonell, A.; Veitch, C.; Aitken, P.; Elcock, M. The Organisation of Trauma Services for Rural Australia. Australas. J. Paramed. 2009, 7. [Google Scholar] [CrossRef]
- Fatovich, D.; Phillips, M.; Jacobs, I.; Langford, S. Major Trauma Patients Transferred from Rural and Remote Western Australia by the Royal Flying Doctor Service. J. Trauma Acute Care Surg. 2011, 71, 1816–1820. [Google Scholar] [CrossRef] [PubMed]
- Andrew, E.; De Wit, A.; Meadley, B.; Cox, S.; Bernard, S.; Smith, K. Characteristics of Patients Transported by a Paramedic-staffed Helicopter Emergency Medical Service in Victoria, Australia. Prehospital Emerg. Care 2015, 19, 416–424. [Google Scholar] [CrossRef] [PubMed]
- Suriadi, S.; Andrews, R.; ter Hofstede, A.; Wynn, M. Event Log Imperfection Patterns for Process Mining: Towards a Systematic Approach to Cleaning Event Logs. Inf. Syst. 2017, 64, 132–150. [Google Scholar] [CrossRef]
- Halpin, T.; Morgan, T. Information Modeling and Relational Databases; Morgan Kaufmann: Burlington, MA, USA, 2010. [Google Scholar]
- Strong, D.M.; Lee, Y.W.; Wang, R.Y. Data Quality in Context. Commun. ACM 1997, 40, 103–110. [Google Scholar] [CrossRef]
- ISO/IEC Joint Technical Committee 1—Information Technology. ISO/IEC 25010:2011: Systems and Software Engineering—Systems and Software Product Quality Requirements and Evaluation (SQuaRE)—System and Software Quality Models; International Organization for Standardization: Geneva, Switzerland, 2011. [Google Scholar]
- Wand, Y.; Wang, R. Anchoring Data Quality Dimensions in Ontological Foundations. Commun. ACM 1996, 39, 86–95. [Google Scholar] [CrossRef]
- Batini, C.; Scannapieco, M. Data Quality: Concepts, Methodologies and Techniques; Springer: Berlin, Germany, 2006. [Google Scholar]
- Wang, R.Y.; Strong, D.M. Beyond Accuracy: What Data Quality Means to Data Consumers. J. Manag. Inf. Syst. 1996, 12, 5–33. [Google Scholar] [CrossRef]
- van der Aalst, W. Extracting Event Data From Databases to Unleash Process Mining. In BPM-Driving Innovation in a Digital World; Springer: Berlin, Germany, 2015; pp. 105–128. [Google Scholar]
- Batini, C.; Cappiello, C.; Francalanci, C.; Maurino, A. Methodologies for Data Quality Assessment and Improvement. ACM Comput. Surv. (CSUR) 2009, 41, 16. [Google Scholar] [CrossRef]
- Leemans, S. Robust Process Mining with Guarantees. Ph.D. Thesis, Technische Universiteit Eindhoven, Eindhoven, The Netherlands, 2017. [Google Scholar]
- Mannhardt, F.; De Leoni, M.; Reijers, H.A. The Multi-perspective Process Explorer. BPM (Demos) 2015, 1418, 130–134. [Google Scholar]
|D_RECEIVED_CAD||Date/time the QAS emergency call centre is notified of an incident and|
a request for an ambulance.
|FIRST_ASSIGNED_CAD||Date/time when the first ambulance unit is dispatched to attend the incident.|
|CAD (Vehicle) Waypoints|
|ON_SCENE_CAD||Date/time when a unit arrives at the incident scene.|
|DEPART_SCENE_CAD||Date/time when a unit departs the incident scene.|
|AT_DEST_CAD||Date/time when a unit arrives at destination. This is usually a hospital|
|CLEAR_CAD||Date/time when a unit indicates its involvement in the incident is finished|
(and is available for re-tasking).
|eARF (Patient) Waypoints|
|EN_ROUTE_VACIS||Date/time recorded by a unit indicating it has commenced travelling|
to the incident scene.
|AT_SCENE_VACIS||Date/time recorded by a unit when it arrives at the incident scene.|
|AT_PAT_VACIS||Date/time recorded by a unit when paramedics arrive at a patient. May|
be different from arriving at the incident scene as the patient location may
be inaccessible by vehicle necessitating the paramedics walk to the patient.
|LOADED_VACIS||Date/time recorded by a unit when a patient is loaded in the unit ready|
|NOTIFY_VACIS||Date/time recorded by a unit on leaving incident scene.|
|OFF_STRETCHER_VACIS||Date/time recorded by a unit when a patient is unloaded from the unit|
|Column Name||Data Type||Completeness||Precision||Uniqueness|
|CAD (Vehicle) Waypoints|
|eARF (Patient) Waypoints|
|DATE_RETRIEVAL_REQUESTED||Date the RSQ is notified of an incident and of a request for|
|TEAM_ACTIVATED||Date/time when the medical team crewing is alerted to fly.|
|READY_TO_DEPART||Date/time when the unit is ready to depart base.|
|DEPART_WITH_MEDICAL_TEAM||Date/time when a unit actually departs base.|
|LAND_AT_DESTINATION||Date/time when a unit arrives at the incident scene.|
|AT_SCENE_PATIENT||Date/time when the medical team arrives at a patient. May|
be different from arriving at the incident scene as the patient
location may be inaccessible by vehicle necessitating the
paramedics walk to the patient.
|DEPARTURE_READY||Date/time when a unit is ready to depart from the incident scene.|
|ACTUAL_TIME_DEPART||Date/time when a unit actually departs the incident scene.|
|ARRIVE_AT_RECIEVING_HOSPITAL||Date/time when a unit arrives at the receiving hospital.|
|DEPART_RECIEVING_HOSPITAL||Date/time when a unit departs from the receiving hospital|
(after handing over patient to hospital medical team.
|ARRIVE_BACK_AT_BASE||Date/time when a unit returns to base.|
|AVAILABLE_FOR_NEXT_TASKING||Date/time when a unit is refitted and ready for re-tasking.|
|MECHANISM_OF_INJURY||mode of injury necessitating aero-medical rather then|
ground-based ambulance attendance.
|Column Name||Data Type||Completeness||Precision||Uniqueness|
|Milestone Activities||a before b||a after b|
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Andrews, R.; Wynn, M.T.; Vallmuur, K.; ter Hofstede, A.H.M.; Bosley, E.; Elcock, M.; Rashford, S. Leveraging Data Quality to Better Prepare for Process Mining: An Approach Illustrated Through Analysing Road Trauma Pre-Hospital Retrieval and Transport Processes in Queensland. Int. J. Environ. Res. Public Health 2019, 16, 1138. https://doi.org/10.3390/ijerph16071138
Andrews R, Wynn MT, Vallmuur K, ter Hofstede AHM, Bosley E, Elcock M, Rashford S. Leveraging Data Quality to Better Prepare for Process Mining: An Approach Illustrated Through Analysing Road Trauma Pre-Hospital Retrieval and Transport Processes in Queensland. International Journal of Environmental Research and Public Health. 2019; 16(7):1138. https://doi.org/10.3390/ijerph16071138Chicago/Turabian Style
Andrews, Robert, Moe T. Wynn, Kirsten Vallmuur, Arthur H. M. ter Hofstede, Emma Bosley, Mark Elcock, and Stephen Rashford. 2019. "Leveraging Data Quality to Better Prepare for Process Mining: An Approach Illustrated Through Analysing Road Trauma Pre-Hospital Retrieval and Transport Processes in Queensland" International Journal of Environmental Research and Public Health 16, no. 7: 1138. https://doi.org/10.3390/ijerph16071138