A Standardized Pre-Hospital Electronic Patient Care System

A Standardized Pre-Hospital Electronic Patient Care System Mark Gaynor, Dan Myung, Amar Gupta, Steve Moulton Biographical notes: Mark Gaynor PhD holds a PhD in Computer Science from Harvard University and is an Assistant Professor in the Graduate School Management at Boston University His research interests include distributed sensor networks for medical applications, innovation with distributed architecture, IT/HealthCare standardization, designing network based-services, IT for healthcare, emergency medical services He has been Co-PI on several grants from NSF, NIH, and the US army He is technical director and network architect at 10Blade He His first book, Network Services Investment Guide: Maximizing ROI in Uncertain Markets, is in press with Wiley (2003) Dr Gaynor has accepted a new position at the School of Public Health at Saint Louis University Dan Myung AB is the Director of Application Development at 10Blade, Inc., where he is the lead architect for iRevive Dan graduated with an AB in computer science from Harvard University Dan's role at 10Blade has since shifted to one of consultation on architectural and technical matters Since 2007, Dan has been Senior Engineer at Dimagi, Inc in Boston, MA where he has continued to build upon his portfolio of critical engineering for medical records system Recently his projects have included: • Core engineering for the smartcard-based national medical records system for the Republic of Zambia funded by the US Centers for Disease Control Global AIDS Program • Project manager and core engineer for a cell-phone based remote screening and consultation system for cervical cancer in Zambia • Core engineer for a system for anonymous text message reminders for HIV patients, an NIHfunded study on ART adherence • Core engineer for an Android phone based SMS monitoring and alert system for asset and crisis management Amar Gupta is Tom Brown Endowed Chair of Management and Technology; Professor of Entrepreneurship, Management Information Systems, Management of Organizations, and Computer Science; all at the University of Arizona In addition, he is Visiting Professor at MIT for part of the year Earlier, he was with the MIT Sloan School of Management (1979-2004); for half of this 25-year period, he served as the founding Co-Director of the Productivity from Information Technology (PROFIT) initiative He has published over 100 papers, and serves as Associate Editor of ACM Transactions on Internet Technology At the University of Arizona, Professor Gupta is the chief architect of new multi-degree graduate programs that involve concurrent study of management, entrepreneurship, and one specific technical or scientific domain He has nurtured the development of several key technologies that are in widespread use today, and is currently focusing on the area of the 24-Hour Knowledge Factory Steve Moulton is Professor of Surgery at University of Colorado, School of Medicine He is board certified in general and pediatric surgery His research is in the areas of trauma and medical informatics His bibliography includes over 50 publications, several active grants, and one patent Dr Moulton is also the Founder and Chairman of 10Blade, Inc (www.10blade.com, March 2009), a startup company developing application software, sensors and sensor network infrastructure for the management of critically ill and injured patients Abstract This paper describes the design, development and testing of a pre-hospital documentation and patient monitoring application called iRevive The application utilizes a sensor gateway and data mediator to enable semantic interoperability with a wide variety of medical devices and applications Initial test results indicate that complete and consistent prehospital Electronic Medical Records (EMR) can be semantically exchanged with two heterogeneous, in-hospital IT applications Keywords: Electronic Medical Records, Interoperability, Clinical Documentation, Emergency Medical Response, Trauma, Standards, Data mediation Introduction We have designed and developed a robust pre-hospital patient care application to improve the quality, distribution and value of pre-hospital patient care information The application, called iRevive, uses wireless sensors to automatically collect and store vital sign data on a timeline, in parallel with manually entered patient care information It adheres to current and emerging health care standards for the storage and transfer of electronic patient data It is, in addition, interoperable, semantically flexible, extensible, and therefore tolerant of changing documentation standards iRevive was developed by 10Blade, Inc., the University of Arizona, and Boston MedFlight (BMF; www.bostonmedflight.org, March 2009) under a grant from the National Institutes of Health BMF is one of America’s largest, non-profit critical care transport services, and as such, plays a central role in our local and regional Emergency Medical Services (EMS) systems Boston MedFlight uses three helicopters, a fixed wing aircraft, and two specially equipped ground vehicles to transport approximately 2700 critically ill and injured patients to the major academic medical centers in Boston each year Maintaining a high quality of service is critical to the success of BMF and an integral part of BMF’s organizational philosophy Boston MedFlight continuously reviews its internal functions and protocols to identify and address all patient care and transport-related problems This cyclical quality management activity demands complete and accurate end-to-end documentation To date, this documentation process has been carried out by manually reviewing and abstracting data from every handwritten transport record, maintaining verbal lines of communication with receiving hospitals, and following up on all adverse outcomes This painstaking review process has led to the creation of a large database with disparate tables of information (e.g dispatch, patient care, transport, billing, and quality assessment/quality improvement [QA/QI]) that are not amenable to cross-system querying The current information infrastructure is therefore plagued by two major problems: 1) the standing clinical record is a handwritten piece of paper, and 2) the clinical record is incompletely captured, poorly accessible, and unable to support a rigorous QA/QI process iRevive was designed to address these specific problems The iRevive system consists of several components that work together to create a complete electronic patient care record based on emerging standards These components include a flexible graphical user interface (GUI) to guide data entry, a sensor gateway enabling automatic collection of real-time vital sign information, an expressive and rich Extensible Markup Language (XML) markup of all data fields, and a data mediator to facilitate data exchange between overlapping documentation standards The data mediator promotes interoperability It allows pre-hospital patient care information collected in the iRevive system to be made available to in-hospital providers prior to patient arrival, so that acute patient care needs can be anticipated and planned for It also allows pre-hospital patient care information to become part of each patient’s in-hospital record This will facilitate creating an end-to-end record of each illness event, thus allowing for comprehensive data sharing and quality assurance This is accomplished using linguistic mapping techniques to create mappings into and out of current and emerging nomenclature and communication standards Motivation Our research focus falls within the early resuscitative phase of patient care, when a patient’s physiology is in constant flux due to acute injury or major illness, and clinicians are attempting to intervene and stabilize the patient The pre-hospital phase of patient care is characterized by excitement, high levels of concentration, occasional life and death decisionmaking, and high expectations for performance This phase demands an accurate assessment of physical exam findings, correct interpretation of physiological changes, and an understanding of treatment priorities These actions occur over a relatively short period of time—from minutes to hours—during which a large amount of information must be quickly gathered, accurately interpreted, and meaningfully conveyed to a coordinated group of local and downstream healthcare providers The potential benefits of electronic medical records are numerous and multi-faceted, with direct and indirect advantages to heath care providers, vendors of health care goods and services, insurance companies, medical researchers, and most importantly, those receiving medical and surgical treatment In aggregate, savings from existing EMRs have been estimated to be as high as $77 billion per year [Walker et al 2005] Hospitals benefit from safer, more efficient systems, which reduce medical errors while cutting costs Physicians who have transitioned to electronic medical records cite improved documentation to support higher levels of billing, the convenience of prescription writing, and the seamless integration of laboratory reporting and x-ray viewing [Baron 2005; Davidson, 2004] Vendors and service providers benefit from a standards-based infrastructure to facilitate the exchange of medical information This has translated into a rich eco-system of vendors and service providers, similar to what developed around the Internet and Web standards Insurance companies benefit by requiring more accurate billing, fewer redundant tests and fewer clinical errors Medical researchers benefit from higher quality data capture Patients benefit from fewer errors, less overlap in testing and querying, and the projected lowering of health care costs The overall advantages of electronic medical records are too significant to ignore in today’s environment of rising health care costs Improving electronic data capture leads to better evidence-based treatment protocols, better outcomes and lower healthcare costs [Mackenzie, Hu, et al 2008] This is especially true in the field of trauma, which is complex, data intensive, difficult to study, and traditionally under-funded Trauma is also common: it is the leading cause of death and disability during the first three decades of life Here in the United States, more than 150,000 people die each year as a result of trauma Another to million people suffer disabling injuries, resulting in a staggering $400 billion economic impact [Anonymous 1996 and 2000] Traumatic brain injury (TBI) and exsanguination are the two most common causes of traumatic death, making the management of head injury, hemorrhage and fluid resuscitation an integral part of early trauma care [Anonymous 2000] Evidence-based guidelines for the management of severe traumatic brain injury have been developed, yet a wide spectrum of methods still characterizes most treatment strategies [Bennett et al 1991] [ Bickell et al 1992] [Bickell et al 1994] Fluid resuscitation strategies are also poorly understood, difficult to study, and variably practiced Under-resuscitation poses the risk of hypotension and end organ damage Conversely, aggressive fluid resuscitation can dislodge clots from vascular injuries, causing further blood loss, hemodilution and death [Bickell and Waal 1994] How to best proceed when one is dealing with a multiply-injured patient, who has a traumatic brain injury and exsanguinating hemorrhage, can be especially difficult Underresuscitation can harm the already-injured brain, whereas overresuscitation can reinitiate intracranial hemorrhage and exacerbate brain swelling, leading to brain herniation, permanent neurological injury, and oftentimes death At the present time, detailed data regarding pre-hospital resuscitation efforts is poorly captured and rarely integrated with hospital based records, making the study of resuscitation strategies and their end points both complex and time-consuming Compounding this problem is the fact that both pre-hospital and in-hospital EMRs generally function in isolation, with little or no electronic communication with patient-related devices (e.g cardiopulmonary monitors, ventilators, and IV pumps) or downstream systems, This has led to the creation of several competing and proprietary standards, which increase the cost and complexity of automating the collection, analysis, display and electronic transfer of patient care data between different healthcare providers, settings and systems We developed iRevive with the vision of linking physiological, observational and interventional patient data with hospital data, in order to produce an end-to-end EMR for individual illness events Related Work Prior work can be found in many areas, including Human Computer Interaction (HCI), sensor network infrastructure, standards for medical documentation and information transfer, and data mediation between heterogeneous overlapping standards [Gaynor et al 2008] Portable wireless computing devices promise significant benefits for applications that focus on dynamic real-time events, yet their optimization still presents many research challenges HCI Human computer interaction (HCI) research generally encompasses the development of interfaces for dynamic, real-time environments [Burns 1991] These include: 1) task-based methodologies [Lewis and Reiman 1993]; 2) effective use of mobile wireless devices such as tablet PCs/PDAs and wireless sensors [Gaynor et al 2004]; 3) use of HCI to promote safety and efficiency; 4) use of multi-modal data sources; and 5) general HCI metrics [Salman and Karhoca] Application-focused research includes HCIs for emergency response [Turoff et al 2004], emergency medical services, emergency departments, and general medical applications Our research extends this previous work by combining disparate ideas to develop an effective HCI interface for environments that are challenging, dynamic, uncertain, and require mobility Our efforts embrace the task-centered approach pioneered by Lewis and Rieman [Lewis and Reiman 1993], in which the user interface is designed and evaluated within the context of how effective the human computer interaction is when users try to accomplish a particular task The specific tasks that we identified include the creation of a pre-hospital electronic medical record (EMR) via multimodal input, the underlying need for quality assurance and quality improvement process (including approval of each completed EMR by a senior medical officer), and reporting functions These reporting functions include internal reporting, as well as reports to monitoring agencies such as the Centers for Disease Control and Prevention (CDC) For the first task, we selected several existing, paper-based records and created the associated electronic medical record de novo This analysis resulted in a significant improvement in the time required to enter an “average” record into the GUI—from 30 minutes to less than 10 While task-based principles are powerful, they cannot address the inherent uncertainty of dynamic tasks [Boukachour et al 2003] Coskun discusses safety-critical systems and how HCIs need to support users who are in pressure situations [Coskun and Grabowski 2003] Testing HCIs for these types of situations can be challenging, as it is difficult to mimic life and death decision-making under conditions of high uncertainty within a laboratory environment [Bennett et al 2006] Our GUI is designed around a flexible meta-language approach, in order to address and adapt to dynamic and uncertain environments This architecture forces the separation of the GUI from the application code The advent of smaller, more powerful mobile wireless devices such as PDAs, tablet PCs, and small wireless sensors have created new challenges that HCI researchers need to address, including new interaction styles such as small-displays, tilt and touch interfaces, displays of multi-modal input, and voice activation [Ichello and Terrenghi 2005] Research indicates that Health Information Systems are roughly 10 years behind expectations, despite the fact that wireless mobile devices can have a tremendously positive impact on medical applications [Gururjan and Murugesan 2005] Research efforts relating to HCI in healthcare are vast There are many overviews describing the types of applications that work best [Jamar et al 1998], as well as general design guidelines for medical software development [Gosbee et al 1997] In line with the general research demands on the HCI community, emergency medical IT applications require a flexible and adaptable interface for long term evolution, due to emerging technologies, medical concepts, procedures, and new regulations [Amouh et al 2005] (Why the double bracket?) Similar to the ARTHOR [Amouh et al 2005] architecture, we base all of our meta-data on XML representation to ensure future flexibility Previous studies in Human Computer Interaction (HCI) for medical applications and emergency response have focused on: 1) effective information capture in dynamic real-time environments [Boukachour et al 2003; Iachello and Terrenghi 2005]); 2) HCIs with mobile wireless devices [Gururajan and Murugesan] [Kuhn 2001] [Gururajan and Murugesan 2005; Kuhn 2001]?; and 3) decision support [Kuhn and Giuse 2001] What has not been addressed is an overall scheme to combine and enhance these ideas to build an effective application for the creation of electronic pre-hospital medical records Furthermore, to be an effective tool for first responders, information systems must function well in chaotic environments and be supported by complex computer interactions that meet dynamic and uncertain needs By combining emerging technology with new developments in human-computer interfaces, iRevive contributes to the longstanding challenge of improving the quality of pre-hospital documentation [Clayton 2001; Kuhn and Guise 2001] Schema Matching We based our mediation infrastructure [Sarnikar et al.] on two matching techniques that can be broadly classified into linguistic instance and schema-based matching techniques [Rahm and Bernstein 2001] Linguistic techniques are based on identifying linguistic similarities 10 Information Matching Between iRevive, iBEX and nTRACS In this experiment we compared diagnostic ICD-9 patient information between Boston MedFlight and two different patient care information applications at Boston Medical Center: NTRACS and ibex The former is the trauma database that is used at BMC; the latter is a proprietary system that is actively used by emergency department nurses and physicians to document ED care and assessment There were 231 true patient matches between Boston MedFlight, ibex and NTRACS Exact digit ICD-9 code matches were uncommon between BMF and both NTRACS (4%) and ibex (8%) patient information systems (see Table 5) Table – Data Matching BMF to nTRACS BMF to ibex ibex to nTRACS Exact Digit Match 11/231 (4%) Digit Match Digit Match 83/231 (36%) 14/231 (6%) (5 + + 3) Digit Matches 108/231 (47%) 19/231 (8%) 44/231 (19%) 23/231 (10%) 86/231 (37%) 77/231 (33%) 83/231(36%) 14/231 (6%) 174/231 (75%) One interesting result is a less than 50% correlation between the primary diagnoses in the iRevive (pre-hospital) data set, versus the two in-hospital patient information systems From a knowledge development standpoint, poor diagnostic correlation for the same patient, evaluated for the same injury event, using three different information systems, is not ideal Also surprising is that the ibex (emergency department) primary diagnosis had a poorer correlation with the prehospital data than did the nTRACS primary diagnosis We believe this is because the ibex diagnostic data is collected within minutes to hours of the pre-hospital data, whereas nTRACS data is collected during hospitalization and after patient discharge 37 The findings in these two experiments validate the need for improved data mediation and data sharing between pre-hospital and hospital-based data systems This is especially true if we plan to use these combined data sets for outcomes studies and the development of new knowledge bases Conclusion We have designed and developed a robust pre-hospital patient care system called iRevive with Boston MedFlight, 10Blade, and researchers at the University of Arizona, under a Phase SBIR/STTR award The iRevive EMR is composed of automatically-collected physiological patient data and manually-entered patient information, including dispatch information, history, physical exam findings, procedural information, and response to treatment Patient information can be captured at the point of care and wirelessly transferred to a central server using the emerging standards of web services and HL7 v3 compliant messaging The application includes a data mediator, which allows the application to safely match and exchange electronic pre-hospital patient care information with heterogeneous healthcare information systems The entire system is built upon open standards It is robust, semantically flexible, web service enabled and forward compatible Acknowledgements This work was supported by NIH R41 RR018698-01A1, NSF PFI-0227879, NSF ACI0330244, and NSF IIS-0529798) We would like to thank the staff at BMF for guidance 38 References [1] Abiteboul, S., Cluet, S and Milo, T (2002) “Correspondence and translation for heterogeneous data” Theoretical Computer Science, 275, pp 179–213 [2] Amouh, Teh, Gemo, Monica, Macq, Benoit, Venderdonckt, Jean, Wahed, Abdul, Reynaert, Marc, Stamatakis, Lambert, and Thys, Frederic Versatile Clinical Information System Design for Emergency Departments, IEEE Transactions on Information Technology in medicine, Vol 9, No 2, June 2005 [3] Anderson, C.W Patient Care Documentation Emergency Medical Services Magazine, 28(3): 59-62, 1999 [4] Anonymous, Guidelines for the management of severe head injury Brain Trauma Foundation, American Association of Neurological Surgeons, Joint Section on Neurotrauma and Critical Care J Neurotrauma 1996; 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SOA architecture to achieve interoperability in health care has been shown to have theoretical value in an empirical study by Daskalakis [Daskalakis and Mantas 2009] as well as practical value... fields, and a data mediator to facilitate data exchange between overlapping documentation standards The data mediator promotes interoperability It allows pre-hospital patient care information collected