Discussion 3 - Healthcare Information Systems

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Compare and contrast these two healthcare delivery system models: the patient-centered medical home (PCMH) and the accountable care organization (ACO). What does each of these models do to improve population health? Evaluate the challenges that each model faces.

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Hindawi Publishing Corporation e Scientific World Journal Volume 2015, Article ID 727694, 9 pages http://dx.doi.org/10.1155/2015/727694 Review Article Information Technology in Critical Care: Review of Monitoring and Data Acquisition Systems for Patient Care and Research Michael A. De Georgia,1 Farhad Kaffashi,2 Frank J. Jacono,1,3 and Kenneth A. Loparo2 1 University Hospitals Case Medical Center, 11100 Euclid Avenue, Cleveland, OH 44106-5040, USA Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106-7078, USA 3 Louis Stokes Cleveland VA Medical Center, 10701 East Boulevard, Cleveland, OH 44106, USA 2 Correspondence should be addressed to Michael A. De Georgia; michael.degeorgia@uhhospitals.org Received 17 October 2014; Accepted 2 January 2015 Academic Editor: Anastasia Kotanidou Copyright © 2015 Michael A. De Georgia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. There is a broad consensus that 21st century health care will require intensive use of information technology to acquire and analyze data and then manage and disseminate information extracted from the data. No area is more data intensive than the intensive care unit. While there have been major improvements in intensive care monitoring, the medical industry, for the most part, has not incorporated many of the advances in computer science, biomedical engineering, signal processing, and mathematics that many other industries have embraced. Acquiring, synchronizing, integrating, and analyzing patient data remain frustratingly difficult because of incompatibilities among monitoring equipment, proprietary limitations from industry, and the absence of standard data formatting. In this paper, we will review the history of computers in the intensive care unit along with commonly used monitoring and data acquisition systems, both those commercially available and those being developed for research purposes. 1. Introduction There is a broad consensus that health care in the 21st century will require the intensive use of information technology and clinical informatics to acquire and manage data, transform the data to actionable information, and then disseminate this information so that it can be effectively used to improve patient care. Nowhere is this more evident and more important to patient outcomes than in the intensive care unit (ICU). Critical care involves highly complex decision making. It is by nature data-intense. Despite the growth of critical care, however, the basic approach of data collection and management has remained largely unchanged over the past 40 years. Large volumes of data are collected from disparate sources and reviewed usually retrospectively; and even that is difficult. Providers must navigate through a jungle of monitors, screens, software applications, and often paper charts that provide supplemental patient data inherent in today’s cacophony of information management systems. Data from patient monitors and medical devices, although available visually at the bedside, is challenging to acquire and store in digital format. There is limited medical device interoperability and integration with the electronic medical record (EMR) remains incomplete at best and cumbersome. In addition (and partly as a result of these limitations), standard analytical approaches provide little insight into a patient’s actual pathophysiologic state. Understanding the dynamics of critical illness requires precisely time-stamped physiologic data (sampled frequently enough to accurately recreate the detail of physiologic waveforms) integrated with clinical context and processed with a wide array of linear and nonlinear analytical tools. This is well beyond the capability of typical commercial monitoring systems. Such an understanding derived from advanced data analytics can aid physicians in making timely and informed decisions and improving patient outcomes. Ultimately, an integrated critical care informatics architecture will be required that includes acquisition, synchronization, integration, and storage of all relevant patient data into a single, searchable database (numeric and waveforms) and data processing to 2 extract clinically relevant features from raw data and translate them into actionable information [1]. Advances in technology are beginning to bring all of this together. In this paper, we will review the history of computers in the ICU along with commonly used monitoring and data acquisition systems, both those commercially available and those being developed for research purposes. 2. Computers in the ICU Clinical information management systems are now common in most hospitals. These systems have evolved along several parallel lines beginning, not surprisingly, in 1946 with the introduction of the Electronic Numerical Integrator and Computer (ENIAC), the first general-purpose computer (see Table 1). The size of a room and weighing in at 27 tons, ENIAC was developed to calculate missile trajectories for the U.S. Army [2]. Five years later, IBM introduced the first commercially available computer, the Engineering Research Associates (ERA) 1103. Because of their exorbitant cost, use of early computers was limited to large corporations to help manage accounting. In the 1960s, academic institutions followed suit and began developing computer systems to streamline their growing business operations. A decade later, hospitals began to develop EMR systems including the Problem Oriented Medical Record (POMR) at the University of Vermont [3], Health Evaluation through Logical Processing (HELP) at the University of Utah [4], and The Medical Record (TMR) at Duke University [5] and the Computer Stored Ambulatory Record (COSTAR) at Harvard [6]. COSTAR was programmed in MUPMS (Massachusetts General Hospital Utility Multi-Programming System), a computer language better formatted for medical data than COBOL and FORTAN, which were routinely used at the time (MUMPS was eventually adopted for use by the Department of Veterans Affairs). While Indiana’s Regenstrief Medical Record System (RMRS) was one of the first systems for both in-patient and outpatient settings [7], these early EMRs were rarely connected to the real-time data-intense environment of the ICU. This was a world unto itself. Shubin and Weil are credited with introducing the computer to the ICU in 1966 for the purpose of automatically collecting vital signs from the bedside monitor [8]. By connecting an IBM 1710 computer through an analog-to-digital converter to bedside devices, they were able to collect arterial and venous pressure, heart rate, temperature, and urinary output. This had actually been done before in the operating room, though not easily. Using a mechanical contraption, McKesson recorded tidal volumes, fraction of inspired oxygen, and blood pressure in 1934 [9]. The development of the computer (and particularly the microprocessor) made this onerous task much easier. Basic analytical tools, such as trend analysis, were soon added to the automated data collection systems [10]. Other early applications of computers in medicine included one of the first clinical decision support systems to aid in the diagnosis of hematologic disorders by Lipkin and colleagues [11], systems for respiratory monitoring by Stacy and Peters [12], and automation of blood transfusion after cardiac surgery by Sheppard and colleagues [13]. The The Scientific World Journal computer-based Clinical Assessment, Research, and Education System (CARE) was a clinical decision support system developed to aid in the treatment of critically ill surgical patients. The system continuously monitored physiologic and metabolic functions of critically ill patients and managed data about fluid and electrolytes as well as cardiac and respiratory functions [14]. This experience with computers in academic institutions inspired Hewlett-Packard to offer a commercial version of these systems. Adoption of their Patient Data Management System (PDMS), however, was slow because the primitive user interfaces and complex menus were not suited to the fast pace of the ICU [15, 16]. In the 1980s, automatic collection of heart rate and blood pressure became more advanced with data being presented in graphical displays that mimicked the familiar bedside flow sheet [17]. The architecture also evolved from the locally contained model to the client/server model in which a workstation in the ICU (the client) interacted with a central computer housing patient data (the server) via a local area network (LAN). Navigational tools became more user friendly though analytical capabilities remained limited [18]. Links to the fledgling hospital EMR systems were also being made beginning with the computer system that handled admissions, discharges, and transfers (ADT) so that patient demographic data could be readily accessed. Physician and nursing notes were soon being entered electronically into a problem-oriented medical record [19]. In parallel to the ICU, computers were also being introduced in the 1980s into the operating room. Picking up where McKesson left off, in 1986 Gravenstein introduced computerized anesthesia records [20], which allowed for more reliable collection, storage, and presentation of data during the perioperative period as well as provide basic record keeping functions (thus in their infancy such systems were called “anesthesia record keepers”). Still, as in the ICU, data from medical devices were rarely integrated with the other physiological data. In the 1990s, ICU systems improved significantly with increased clinical functionality and Internet access. Webbased software used Web browsers to display the user interface and simple queries of cumulative patient data were supported. Vendors migrated the technology that had been developed for the OR and the ability to record and present continuous patient data as well as provide links to physician notes, nursing documentation, and laboratory and imaging data from the evolving EMR systems, thus creating large enterprise systems, now broadly referred to as Clinical Information Systems [21]. 3. Clinical Information Systems Several Clinical Information Systems are commercially available today for the ICU and competition among vendors is intensifying. Frost & Sullivan have estimated that the annual US market for emergency, perioperative, and intensive care software solutions is currently approximately $842.2 million and are expected to reach $1.3 billion in 2015 [22]. Not one company has a dominant share of the market and several have evolved over the last decade, through various acquisitions of smaller participants, to offer broad end-to-end The Scientific World Journal 3 Table 1: Timeline of computers in the ICU. Computer 1946 1951 ENIAC introduced IBM ERA 1103 introduced 1966 HP 2115 introduced Electronic medical record Computer systems for the ICU 1961 Clinical decision support for diagnosis of hematologic disorders (Lipkin) 1964 Marquette Electronics founded 1965 Computerized respiratory monitoring (Stacy and Peters) 1966 Computerized collection of vital signs (Shubin) 1969 IDX System founded for revenue cycle management (utilizing MUMPS) 1973 Computer assisted monitoring with trend analysis (Lauwers) 1975 HP introduces Patient Data Management System (PDMS) 1976 Clinical decision support for management of critically ill surgical patients (Siegel) 1977 The Medical Record (TMR), Duke University Regenstrief Medical Record System (RMRS), Indiana University 1978 Problem Oriented Medical Record (POMR), University of Vermont Computer Stored Ambulatory Record (COSTAR), Harvard University (MUMPS) 1979 Epic Systems founded (MUMPS) 1980 1983 Computerized management system for ICU (Manzano) Health Evaluation through Logical Processing (HELP), University of Utah 1997 Veterans Health Information Systems and Technology Architecture (VistA) founded Computerized system for automating blood transfusion (Sheppard) HP introduces Carevue system Capsule Technologie introduces DataCaptor 1999 Excel Medical Electronics introduces Bedmaster XA 1986 2000 MedicaLogic founded Clinical Information Systems 2003 GE’s Centricity Critical Care system introduced 2007 Philips IntelliVue Clinical Information Portfolio (ICIP) Critical Care introduced platforms [23]. For example, GE’s Centricity Critical Care system from GE HealthCare (Chalfont St. Giles, UK), introduced in 2003, is the culmination of the acquisitions of, among others, Marquette Medical Systems, a leading manufacturer of patient monitors, Instrumentarium, a manufacturer of mechanical ventilators and anesthesia equipment, and iPath, the basis of the Operating Room Management Information System. For the EMR side, GE also acquired MedicaLogic, a leading provider of outpatient digital health records, and IDX Systems, primarily a practice management and billing system. IDX was written using MUMPS, which currently also forms the basis for EpicCare (Epic Systems Corporation, Verona, WI) and Veterans Health Information Systems and Technology Architecture (VistA). GE’s Centricity Critical Care automatically collects data from monitors and ventilators displays it in spreadsheets reminiscent of the typical paper ICU chart. Data are collected from medical devices through device interfaces that connect with GE’s Unity Interface Device (ID) network. Philips Healthcare (Andover, MA) also has a long history in the ICU with the introduction of the Patient Data Management System in the early 1970s under the HewlettPackard brand. In the 1990s this became CareVue [24] and the most recent iteration is the IntelliVue Clinical Information Portfolio (ICIP) Critical Care introduced in 2007. Like GE’s Centricity, Philips’ ICIP Critical Care also evolved 4 through a series of acquisitions [25]. These included Agilent Technologies Healthcare Solutions Group, a leader in patient monitoring and critical care information management, Witt Biomedical Corporation, a leader in hemodynamic monitoring in catheterization laboratories, and Emergin, a developer of alarm management software. In 2008, Philips acquired TOMCAT Systems Ltd., a company that offers software for the collection of cardiac data and that same year, acquired VISICU Inc., a provider of tele-ICU technology. On the EMR side, Philips also partnered in 2004 with Epic in order to provide end-to-end integration with electronic medical records. As with GE’s Centricity, Philips’ ICIP Critical Care supports automatic (or manual) documentation of physiologic data with time resolutions up to every 5 minutes. Philips Information Support Mart interfaces with ICIP and provides a relational database that archives clinical information such as lab results, text notes, medications, and patient demographics that can be queried with special scripts (see MIMIC II below). 4. Limitations of Commercially Available Clinical Information Systems While modern Clinical Information Systems do provide endto-end platforms for the ICU, there are several limitations that remain. First, they remain limited in terms of functionality and the acquisition of high-resolution physiologic data. For example, the actual physiological waveform signals are not acquired or stored by either the GE’s Centricity system or the Philips ICIP system. This is an important limitation of most commercially available enterprise systems today and is the result of a tradeoff between the memory requirements of capturing high-resolution physiological data (including the underlying waveform morphology) versus capturing only data snapshots that may be sufficient for certain clinical decision-making. Currently, no standards have been defined as to where that balance lies. Philips has developed its own proprietary solution for automated acquisition of waveform data for research called the Research Data Exporter (RDE). This solution does not acquire the data at the native sampling rate of the signal and limits the number of waveforms that can be exported. Better real time acquisition of physiological waveform signals is needed along with education of clinicians regarding its value in understanding of complex physiology. Second, there is currently neither processing nor analysis of data. While a few monitors can display raw trends, even basic analyses (mean, median, standard deviations) are difficult to perform at all let alone in real time and higherlevel analyses are impossible. New physiological models are now emerging suggesting that nonlinear changes in dynamics over time may have more predictive value. Understanding this complex physiology can lead to more timely intervention and better outcomes. Techniques for the analysis of nonlinear systems have emerged from the mathematical and engineering sciences but have not been applied to physiological data in the ICU (in part because the acquisition and integration challenges have not been met). The promise of critical care informatics lies in the potential to use these advanced analytical techniques on high-resolution multimodal physiological data in order to have a better understanding of the complex The Scientific World Journal relationships between physiological parameters, improve the ability to predict future events, and thereby provide targets for individualized treatment in real time. In the future, we will use a system that does not simply report streams of raw data to physicians but also synthesizes it to form hypotheses that best explain the observed data, a system that translates multidimensional data into actionable information and provides situational awareness to the clinician [26]. Third, visual displays in the ICU have advanced little since bedside electronic monitors were introduced more than four decades ago despite the increasing volume of data collected. For example, clinicians may be confronted with more than 200 variables [27] when caring for critically ill patients yet most people cannot judge the degree of relatedness between more than two [28]. This greatly contributes to preventable medical errors [28]. Graphical displays must be carefully and thoughtfully designed by applying a human systems integration approach. It is important to understand not only how information should be optimally presented to promote a better understanding of the patient’s pathophysiologic state and support decision-making but also to facilitate collaboration and work-flow among the team [29]. Finally, despite the recent growth of tele-ICUs, networks of audiovisual communication and computer systems that link ICUs to intensivists, most of these same technical limitations remain. That is, whether these systems are used to monitor a patient located 300 miles away or 3 feet away, the underlying principles, equipment interoperability, data acquisition, synchronization, and data analysis, are equally applicable. Investment in this basic information technology architecture is needed for the next generation of tele-ICU care. 5. Medical Device Interoperability and Data Integration Central to the growth of critical care has been the proliferation of monitoring technology and stand-alone medical devices. For example, a typical critically ill patient may undergo frequent or continuous monitoring of dozens of physiological parameters. An enormous amount of data is generated reflecting dynamic and complex physiology, dynamics that can only be understood by data integration and clinical context. Most of these parameters, however, are generated from stand-alone devices that do not easily integrate with one another. Some connect directly into the bedside monitor but many others do not (or do so incompletely meaning that not all the data is captured electronically). A lack of functional medical device interoperability is one of the most significant limitations in health care today. For example, more than 90% of hospitals recently surveyed by HIMSS use six or more types of medical devices and only about a third integrate them with one another or with their EMRs [30]. In contrast to the “plug and play” world of consumer electronics, most acute care medical devices are not designed to interoperate. Most devices have data output ports (analog, serial, USB, and Ethernet) for data acquisition but there is no universally adopted standard that facilitates multimodal data acquisition and synchronization in a clinical setting; each device often has a unique communication protocol for data The Scientific World Journal transfer and often the time base for each device is independently set rather than determined from a standard source. The development and adoption of medical device standards to improve interoperability is ongoing. Although ISO/IEEE 11073 and ASTM F-2761 (Integrated Clinical Environment, ICE) are two applicable standards, the former has not been widely adopted and the latter is still relatively new (2009). Many groups are tackling the problem of interoperability on their own by developing the hardware and software interfaces that enable device connectivity. Connecting with analog data ports requires appropriate hardware interfaces, analog-todigital (A/D) converters, and filters to eliminate aliasing due to a mismatch between sampling rate and the frequency content of the signal being acquired. It also requires that the data be properly scaled to the voltage range of the A/D converter (microvolts to millivolts) to maximize the resolution. Digital data is available from some devices through connection to serial (RS-232 or USB) or Ethernet (802.3) ports, or using wireless (e.g., 802.11b/g or Bluetooth) communications. Although these approaches provide the opportunity to individually interface with a variety of devices in the ICU, a system that provides comprehensive, cross-manufacturer medical device integration for the care of a single critically ill patient at the bedside is not available. Several third party systems have recently emerged specifically to help facilitate this comprehensive data acquisition and integration. For example, Bedmaster XA (Excel Medical Electronics, Jupiter, and Florida) is a product that can be used to collect medical device data with access through the hospital local area network. First introduced in 1999 to assist clinicians by automatically acquiring a patient’s vital signs from a GE/Marquette patient monitor, the current system works with both GE and Philips patient monitors acquiring parameter data (such as vital signs) from every five seconds to every hour. DataCaptor (Capsule Technologie, Paris, France) is another similar product that can be used to collect medical device data. Time synchronization of the data is a critical feature for multimodal data acquisition from different devices and monitors. Without a “master clock” ensuring that all the values and waveforms acquired at the same time “line up” exactly in synch, interpreting the information and understanding the interrelationships are difficult, if not impossible. There are two issues. First, when data is being acquired from different devices, each with its own internal clock, the time stamps of data acquired simultaneously can all be different. Time synchronization is therefore necessary when simultaneous analog and digital data streams are acquired in order to align the data. Second, even when acquiring data from a single patient monitor, time drifting from natural degradation, daylight savings time, or incorrect adjustments made by the clinical staff need to be corrected. The Unity Time feature in the Bedmaster XA system manages time synchronization by insuring the accuracy of the time clocks on all GEMS devices connected to the Unity Network. Unity Time functions in conjunction with a NTP (Network Time Protocol) server, as specified by the medical facility. Time clocks on all GEMS patient monitors connected to the unity network are automatically reset to the NTP server at a time interval 5 selected by the hospital. It is primarily these obstacles that have limited wide spread adoption of multimodal monitoring technology in the ICU. 6. Data Acquisition and Integration Systems for Research Commercial off-the-shelf products do not support highresolution physiologic data acquisition, archiving, or annotation with bedside observations for clinical applications. Such systems have been developed in academic settings though mainly for clinical research. Because they are not open source, most of these systems are not readily available. This has resulted in considerable duplication of effort in software development for acquiring and archiving physiological data. There have been a variety of efforts ranging from developing and testing of new mathematical and analytical tools, to hardware/software solutions for patient data acquisition, archiving, and visualization. A complete listing is beyond the scope of this review but several stand out (see Table 2). For example, Tsui and colleagues developed a system for acquiring, modeling, and predicting ICP in the ICU using wavelet analysis for feature extraction and recurrent neural networks to compute dynamic nonlinear models [31]. Smielewski and colleagues from Cambridge University have developed the Intensive Care Monitoring (ICM+) system; configurable software based on MATLAB (The Mathworks, Natlick, MA) that allows real-time acquisition, archiving, and analysis of multimodal data that can then be displayed in several ways including simple trends, cross histograms, correlations, and spectral analysis charts. The software is intended for research so it stores the raw signals acquired from bedside monitors for subsequent reprocessing, thus providing the means of building a data repository for testing novel analytical methods [32]. Others have focused on multimodal data collection linked with clinical annotation. In London, Gade and his colleagues reported the development of the Improved Monitoring for Brain Dysfunction in Intensive Care and Surgery (IBIS) data library that contained continuous EEG signals, multimodal evoked potential recordings, and ECG [33]. The system captured trend data from patient monitors, laboratory data, and some clinical annotations. In 2001, Kropyvnytskyy and colleagues [34] reported a similar system in Boston called SWE for sampling, data display (WAVE), and ECG processing. SWE was initially developed for the MIT-BIH arrhythmia database (and now used for publicly available databases on the National Institutes of Health-sponsored PhysioNet website). Sorani and colleagues from San Francisco General Hospital [35] also developed a system that captured over 20 physiological variables (plus date, time, and annotated clinical information) from Viridia bedside monitors (Philips), Licox tissue oxygen monitors (Integra NeuroSciences, Plainsboro, NJ), and Draeger ventilators (Luebeck, Germany). Data was collected automatically at 1-minute intervals and was output into text files. Monitoring data was integrated by special custom developed middleware (Aristein Bioinformatics). Goldstein and colleagues from Oregon Health Sciences University developed a physiologic data acquisition system that could 6 The Scientific World Journal Table 2: Data acquisition and integration systems for research. 1996 1998 2000 2001 2003 2007 2007 2008 2011 Moody and colleagues [38] Tsui and colleagues [31] Gade and colleagues [33] Kropyvnytskyy and colleagues [34] Goldstein and colleagues [36] Sorani and colleagues [35] Meyer and colleagues [41] Smielewski and colleagues [32] Feng and colleagues [42] Multiparameter Intelligent Monitoring for Intensive Care (MIMIC) Acquisition, modeling, and predicting ICP Improved Monitoring for Brain Dysfunction in Intensive Care and Surgery (IBIS) Sampling, data display (WAVE), and ECG processing (SWE) system Data acquisition system to capture both parametric data and underlying waveforms Aristein Bioinformatics system “OR of the Future” Intensive Care Monitoring (ICM+) system intelligent System for Neuro-Critical-Care (iSyNCC) capture and archive parametric data (such as blood pressure and heart rate) along with the underlying waveforms to assess dynamic changes in the patient’s physiologic state [36]. The system consisted of a laptop computer, a standard PCMCIA serial card (Socket Communications, Newark, CA), RS232 serial interface cables, and custom software. The system acquired analog data from devices incorporating antialiasing filters along with analog-to-digital conversion. Parametric data was sampled at a rate of 0.98 Hz and continuous wave data either at 500 Hz (ECG) or 125 Hz (pressures, arterial saturation, and respiration). Software managed communications with the monitoring devices; the collected signal data were sent to a patient data server and workstation where the files were archived. Standard analytical software packages, such as MATLAB, facilitated advanced mathematical analyses including time and frequency domain methods and linear and nonlinear signal metrics. Annotation of important clinical events, such as changes in a patient’s condition or timing of drug administration, was limited. In 2006, the same group reported the next generation of their system that added event markers and clinical annotation [37]. Moody and Mark from Massachusetts General Hospital initially reported on their initial efforts in developing the MIMIC (Multiparameter Intelligent Monitoring for Intensive Care) database [38]. An updated version, MIMIC II, was reported in 2002 [39]. Each record consisted of four continuously monitored waveforms (two leads of ECG, Arterial Blood Pressure, and Pulmonary Artery Pressure) sampled at 125 Hz, along with other basic parameters (heart rate, oxygen saturation, and cardiac output) collected every minute. The waveforms and parameters were originally acquired from Philips bedside patient monitors using their RDE software tool. Using a customized archiving agent, the waveform and parameter data were later stored onto storage drives and converted from Philips’ proprietary RDE data format into an open-source format (WFDB), thereby making it accessible to others for research. Various tools were used to analyze data. The 1-minute parameter data were processed using wavelet analysis to identify potentially relevant clinical events. Matching waveform records to clinical data was based on unique identifiers such as medical record numbers, dates of admission, and patient names. A text search engine was created to allow users to query the database for key words and patterns of interest. In 2011, this MIMIC II database was made public and is available for research [40]. While MIMIC II represents a major achievement, because physiological data and clinical annotations are collected separately, the two datasets are poorly synchronized. Also, physiological data and clinical annotations have different time “granularity,” making it difficult to retrospectively determine the timing of a clinical event down to seconds. Reconstructing the clinical context with these limitations results in correlations being largely speculative. In parallel to MIMIC II, Meyer and Colleagues at MGH, as part of the “OR of the Future” project, introduced a system for the operating room to perform integration and display of data from a variety of disparate sources, including hospital information systems, patient monitors, surgical equipment, and a location tracking system [41]. At the core of this system is custom integration software (LiveData OR RTI Server, Live-Data, Inc., Cambridge, MA, USA) that is used to capture in realtime all device data (except from infusion pumps), including detailed physiological waveform data and all data elements, without data loss. Custom interfaces are needed for devices with proprietary data formats. Data are maintained in a relational database with an archive of all captured OR data, including trends and full resolution waveforms, information from the location tracking system, and patient and scheduling information (from multiple hospital information systems). This automated database allows time-based playback and analysis of the events of the surgery. Selected data are displayed in real-time on an integrated display created using scalable vector graphics. In 2011, Feng and colleagues developed the intelligent System for Neuro-Critical-Care (iSyNCC) to facilitate multimodal data acquisition and transmission across a local network for storage in a database and computational analysis. The system includes an “artifact removal” module to ensure high quality data and the ability to provide short term data forecasting (for example of ICP elevations). There is also a “Recovery Outcome Prediction” module to estimate patient long-term outcomes based on integration of the clinical records and physiological data [42]. 7. Developing the Integrated Medical Environment At University Hospitals Case Medical Center and Case Western Reserve University, we have focused on overcoming many of the obstacles described and putting everything together: The Scientific World Journal 7 Bedside monitor Device Device Device A/D acquisition card MIB RS232 Integrated, time-synchronized physiologic data and advanced analytics CWRU acquisition software Storage in searchable database Waveform database CWRU analytic software Integration with EMR Lab data Medications Cultures Numeric database Imaging Clinician-centric query interface and display Figure 1: Schematic illustration of the integrated medical environment (tIME). high-resolution physiologic data acquisition, integration, processing, archiving, annotation with bedside observations for clinical applications, and visualization. The Integrated Medical Environment™ (tIME™) (Figure 1), is a new open source architecture that we believe can provide the backbone for the ICU of the future. Specifically tIME™ provides (1) real-time data acquisition, integration, time-synchronization, and data annotation of multimodal physiological waveform data (both analog and digital) from a variety of medical devices and bedside monitors using custom developed parsing algorithms. Both the waveform data and the extracted parametric numeric data are displayed using real-time algorithms developed by our group and simultaneously stored in a local database for easy access, retrieval, and queries. The local database can connect and import into the hospital EMR using a secure HL7 data transfer protocol; (2) a new critical care open middleware informatics architecture that facilitates complex systems analysis methods and data mining capabilities for hypothesis generation and testing; and (3) a clinician-centric visual display and interface, to present an integrated overview of the patient state (past, present, and predicted futures) so that providers can make sensible decisions at the bedside based on all the data. Only when all of these components work in concert will we be able to fully harness the power of information technology to improve patient outcomes in the ICU. computer science, biomedical engineering, signal processing, and mathematics that many other industries have readily embraced. Acquiring, synchronizing, integrating, and analyzing patient data remains frustratingly difficult because of insufficient computational power and a lack of specialized software, incompatibility between monitoring equipment, and limited data storage. All of these technical problems are now surmountable. Today, we are on the verge of the data-intensive science era in which hypotheses will be generated automatically among the enormous amount of data available by using computational science with inductive reasoning. In this new era, information technology enabling the development of an integrated critical care informatics architecture that supports clinical decision-making at the bedside will be essential. 8. Conclusions [1] T. G. Buchman, “Novel representation of physiologic states during critical illness and recovery,” Critical Care, vol. 14, no. 2, p. 127, 2010. [2] K. Tegtmeyer, L. Ibsen, and B. Goldstein, “Computer-assisted learning in critical care: from ENIAC to HAL,” Critical Care Medicine, vol. 29, no. 8, supplement, pp. N177–N182, 2001. While there have been major improvements in intensive care monitoring, including the development of enterprise Clinical Information Systems, the medical industry, for the most part, has not incorporated many of the advances in Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper. Acknowledgment This work was supported, in part, by the Veterans Administration Research Service (I01BX000873). References 8 [3] S. L. McLeroy and R. V. Klover, “Implementing ProblemOriented Medical Records (POMR) in an out-patient clinic,” Journal of the American Dietetic Association, vol. 72, no. 5, pp. 522–524, 1978. [4] T. A. Pryor, R. M. Gardner, P. D. Clayton, and H. R. Warner, “The HELP system,” Journal of Medical Systems, vol. 7, no. 2, pp. 87–102, 1983. [5] W. W. Stead, R. G. Brame, W. 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Annual Conference, pp. 6426–6429, 2011. 9 MEDIATORS of INFLAMMATION The Scientific World Journal Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 Gastroenterology Research and Practice Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 Journal of Hindawi Publishing Corporation http://www.hindawi.com Diabetes Research Volume 2014 Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 International Journal of Journal of Endocrinology Immunology Research Hindawi Publishing Corporation http://www.hindawi.com Disease Markers Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 Volume 2014 Submit your manuscripts at http://www.hindawi.com BioMed Research International PPAR Research Hindawi Publishing Corporation http://www.hindawi.com Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 Volume 2014 Journal of Obesity Journal of Ophthalmology Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 Evidence-Based Complementary and Alternative Medicine Stem Cells International Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 Journal of Oncology Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 Parkinson’s Disease Computational and Mathematical Methods in Medicine Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 AIDS Behavioural Neurology Hindawi Publishing Corporation http://www.hindawi.com Research and Treatment Volume 2014 Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 Oxidative Medicine and Cellular Longevity Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 Review Article Iran J Public Health, Vol. 45, No.9, Sep 2016, pp.1146-1148 Technological Ecosystems in Health Informatics: A Brief Review Article *Zhongmei WU 1, Xiuxiu ZHANG 2, Ying CHEN 1, Yan ZHANG 1 1. Xu Zhou Institute of Medical Sciences, Xu Zhou Central Hospital, 199# South Jiefang Road, Xuzhou, Jiangsu, China 2. Xuzhou City Children's Hospital, Xuzhou, Jiangsu, China *Corresponding Author: Email: 1187793255@qq.com (Received 15 Feb 2016; accepted 17 Jul 2016) Abstract Background: The existing models of information technology in health sciences have full scope of betterment and extension. The high demand pressures, public expectations, advanced platforms all collectively contribute towards hospital environment, which has to be kept in kind while designing of advanced technological ecosystem for information technology. Moreover, for the smooth conduct and operation of information system advanced management avenues are also essential in hospitals. It is the top priority of every hospital to deal with the essential needs of care for patients within the available resources of human and financial outputs. In these situations of high demand, the technological ecosystems in health informatics come in to play and prove its importance and role. The present review article would enlighten all these aspects of these ecosystems in hospital management and health care informatics. Methods: We searched the electronic database of MEDLINE, EMBASE, and PubMed for clinical controlled trials, preclinical studies reporting utilizaiono of ecosysyem advances in health information technology. Results: The primary outcome of eligible studies included confirmation of importance and role of advances ecosystems in health informatics. It was observed that technological ecosystems are the backbone of health informatics. Conclusion: Advancements in technological ecosystems are essential for proper functioning of health information system in clinical setting. Keywords: Ecosystems, Health informatics, Hospital administration Introduction Ecosystems are the information technology advancements being used worldwide for the efficient exchange of health information among health personal and patients (1, 2). A lot of work is being focused worldwide for improvement of security issues associated with the above technology (3). Benkler refers to economic and technological ecosystem as a dynamic structure, which entails of an interconnected population of organizations (4). On the other hand, Hadzic and Chang (5) group is inclined towards digital ecosystem design methodology for the health domain. Moreover, the same group also suggested that the analogy between information systems and biological systems could be extended into the systems design space. 1146 The present review article is focused on the current views of ecosystem in the health sector. Methods We searched the electronic database of MEDLINE, EMBASE, and PubMed for clinical controlled trials, pre clinical studies, and research articles reporting utilization of ecosystem advances in health information technology. Results Importance of information system (IS) and information technology (IT) Planning in Healthcare The better planning is essential need at present in health sector for examining the potential utility of Available at: http://ijph.tums.ac.ir Wu et al.: Technological Ecosystems in Health Informatics … ecosystems concepts in support of understanding the Hospital Management Information System (HMIS). The main problem predicted in firms involved in production of health information systems has been ruled out to be the prediction of the effects of future technological developments on the value of present technologies (5). Furthermore, a recent study presented another view describing information systems as assets of information advancements in the health sector (6). On the other hand, investigators from financial service industry highlighted the importance of planning with regard to inspiration to practitioners in health sector Furthermore, a case study from the financial services industry specifically studied the issue of information systems planning. (7). Further, the impact of IS and IT planning might be, on planning and decisions in the hospital management environment. Organizations should dedicate towards establishment of a strategic plan in relation to key information systems acquisitions (8). Another explanatory research in the IT systems planning space in healthcare revealed that there is a range of different kinds of IT strategies in healthcare that require diverse decisions, investments and prioritized actions as well as differing implementation approaches (9). IS and IT success and failure in healthcare A key underpinning of this research is a desire to see more effective implementation and usage of information systems in the healthcare environment, and more particularly in the HMIS environment. There is certainly healthcare literature pointing to success and failure in relation to hospital information systems, and the reasons for it, but there is theory and some principles describing the success of IS as well as IT projects. For instance, importance of management support and the role of task interdependence as a moderating factor collectively contributed towards the success of information systems in hospital environment (10, 11). Moreover, end user training as well as moderating factors contributes a lot towards success of IT and IS ecosystems (11). Another study revealed 8 separate models of user accepAvailable at: http://ijph.tums.ac.ir tance of technology (12). Moreover, UTUAT the Unified Theory of Acceptance and Use of Technology is another contribution of above study in the field of IS circles. In terms of the potential for information technology to assist in health care, the possible gains are great. For instance, Gonzalez-Moler et al. studied the implementation of novel approach of telemedicine in the patients affected by diabetes (13). Most of the potential benefits of information systems, in healthcare are collectively grouped in a study (14). The authors undertook a cost benefit analysis in relation to the implementation of an electronic medical record (EMR) system. Successful implementation of planning of IT and IS in health sectors resulted in ease of creation of medical records, decreased full time equivalent (FTE) employees and prevented adverse drug events too. Another, recent study reported potential benefits of IT planning success to the hospital managers of health IT systems (9). This implementation and duration of use of health information technologies resulted in better follow-up of mediation guidelines at US hospitals. Major gains can be achieved through the implementation of multifunctional, interoperable HIT systems. There are evidences supporting the successful IT implementation in healthcare (10-15). The collectively suggested many further insights can be obtained about IT planning and implementation in health care, it is possible that an examination of technology ecosystems could have a beneficial impact in this regard as a new lens through which to examine these issues. Conclusion This is obvious from above discussion that technological eco systems are the key players in the successes of health informatics. Further, improvements in these ecosystems will definitely upshot in better health information management system in near future. The authors declare that there is no conflict of interest. 1147 Iran J Public Health, Vol. 45, No.9, Sep 2016, pp.1146-1148 Ethical considerations Ethical issues (Including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, etc.) have been completely observed by the authors. Acknowledgements The authors declare that there is no conflict of interest. References 1. Tweya H, Feldacker C, Gadabu OJ et al. (2016). Developing a point-of-care electronic medical record system for TB/HIV co-infected patients: experiences from Lighthouse Trust, Lilongwe, Malawi. BMC Res Notes, 9:146. 2. Jergensen C, Sarma A, Wagstrom P (2011). The Onion Patch: Migration in Open Source Ecosystems. ESEC/FSE’11, Szeged, Hungary. 3. Rlsson B, Jacobsson A (2005). On Contamination in Information Ecosystems: A Security Model Applied on Small and Medium Sized Enterprises. 38th Hawaii International Conference on System Sciences, Hawaii. 4. Benkler Y (2001). The Battle over the Institutional Ecosystem in the Digital Environment. Communications of the ACM, 44: 84-90. 5. Hadzic M, Chang E (2010). Application of Digital Ecosystem Design Methodology Within the Health Domain. IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans 40. 6. Besson P and Rowe F (2012). Strategizing information systems-enabled organizational transformation: A transdisciplinary review and new directions. J Strategic Inform Sys, 21:103-24. 1148 7. Teubner RA (2007). Strategic information systems planning: A case study from the financial services industry. J Strategic Inform Sys, 16:105-25. 8. Hosseini J (2005). Strategic Technology Planning for the e-Commerce Enabled Enterprise. International Conference on Information Technology: Coding and Computing (ITCC’05). 9. Iveroth E, Fryk P, Rapp B (2013). Information technology strategy and alignment issues in health care organizations. Health Care Manage Rev, 38, 188-200. 10. Enns H, Huff S, Higgins C (2003). CIO Lateral Influencing Behaviours: Gaining Peers' Committment to Strategic Information Systems MIS Quarterly, 27:155-76. 11. Sharma R and Yetton P (2007). The Contingent Effects of Training, Technical Complexity, and Task Interdependence on Successful Information Systems Implementation. MIS Quarterly, 31:219-38. 12. Venkatesh V, Morris M, Davis G, Davis F (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27:425-478. 13. González-Molero I, Domínguez-López M, Guerrero M, Carreira M, Caballero F, RubioMartín E, Linares F, Cardona I, TeresaAnarte M, De Adana M, Soriguer F (2012). Use of telemedicine in subjects with type 1 diabetes equipped with an insulin pump and real-time continuous glucose monitoring. J Telemed Telecare, 18:328-32. 14. Li K, Naganawa S, Wang K, Li P, Kato K, Li X, Zhang J, Yamauchi K (2012). Study of the cost-benefit analysis of electronic medical record systems in general hospital in China. J Med Syst, 36:3283-91. 15. Appari A, Carian E, Johnson M Anthony D (2012). Medication administration quality and health information technology: a national study of US hospitals. J Am Med Inform Assoc, 19: 360-7. Available at: http://ijph.tums.ac.ir Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Adm Policy Ment Health (2016) 43:471–477 DOI 10.1007/s10488-015-0702-5 COMMENTARY At the Intersection of Health Information Technology and Decision Support: Measurement Feedback Systems…and Beyond Bruce F. Chorpita1 • Eric L. Daleiden2 • Adam D. Bernstein2 Published online: 24 November 2015  Springer Science+Business Media New York 2015 Abstract We select and comment on concepts and examples from the target articles in this special issue on measurement feedback systems, placing them in the context of some of our own insights and ideas about measurement feedback systems, and where those systems lie at the intersection of technology and decision making. We contend that, connected to the many implementation challenges relevant to many new technologies, there are fundamental design challenges that await a more elaborate specification of the clinical information and decision models that underlie these systems. Candidate features of such models are discussed, which include referencing multiple evidence bases, facilitating observed and expected value comparisons, fostering collaboration, and allowing translation across multiple ontological systems. We call for a new metaphor for these technologies that goes beyond measurement feedback and encourages a deeper consideration of the increasingly complex clinical decision models needed to manage the uncertainty of delivering clinical care. Keywords Measurement feedback  Dashboard  Evidence base  Treatment  Clinical decision support & Bruce F. Chorpita chorpita@ucla.edu Eric L. Daleiden e.daleiden@practicewise.com Adam D. Bernstein a.bernstein@practicewise.com 1 UCLA Department of Psychology, University of California, Los Angeles, Franz Hall 3227, Los Angeles, CA 90095, USA 2 PracticeWise, LLC, 340 Lee Avenue, Satellite Beach, FL 32937, USA This issue pulls together a valuable assortment of ideas and observations from research teams working with a variety of measurement feedback systems (MFS) to guide clinical care, while confronting and studying both the human and technological implementation challenges. Their collective insights paint a complicated picture that documents both promises and challenges associated with MFS. In our own efforts to improve service delivery in children’s mental health systems over the past 15 years, we have both used and developed such systems, and have thus encountered both the good and the bad firsthand. In the context of these papers, we would like to offer some general insights for consideration as the field moves forward. Many of these insights concern the basic relation between technology and decision making. Technology generally does what we tell it to do, and thus not surprisingly, early developments in health information technology (HIT) managed the most essential and best-understood processes in health care systems, including utilization, billing, and documentation. These were the questions the field had to ask in order to function: who was seen when for what reason, and how it was paid for? But more recently, there has been a focus on the more complex questions we want to ask to guide clinical care: What treatments approaches are we using? Are they helping? What should we do when they are not? In hindsight, many of these questions were naively ambitious on our part, which may explain why they were often met with such equivocal answers as ‘‘22 sessions of individual therapy’’ or ‘‘30 days of residential,’’ along with the associated billing codes. Thanks to a growing vanguard of thinkers, answering the clinically interesting questions is getting closer to a common reality. But precisely because technology does what we tell it to do, this function of HIT will not fully mature until we have better articulated the complex 123 472 information models that underlie it. We can think of these latter questions as belonging to the domain of clinical decision support, as shown in Fig. 1. Note that there are many strategies to support decision making that don’t involve HIT (e.g., supervisor recommendations, use of a treatment manual), and there are many functions of HIT that don’t involve supporting clinical decision making (e.g., documentation, service authorization, scheduling). At this intersection lies a tremendous set of possibilities and opportunities, within which MFS serves a specific function. As the figure implies, although we believe there is much to explore within the context of MFS development— which entails a human-technology interaction defined by a user receiving measurement information in the form of a report or an alert (e.g., Bickman 2008)—there is even more work to be done within the broader context defined by the intersection of technology and clinical decision making (Fig. 1, shaded region). In this regard, we prefer a metaphor of telecommunication (or an interactive workspace), which suggests collaboration, communication, reasoning, interaction, and even design. Such systems of course can feed back information, but they should also be able to feed forward information to guide action (e.g., setting expectations for what should happen next, exploring ‘‘what if’s’’). As Bickman et al. (2014) suggest in their ‘‘final coda,’’ although the technology may be important, it should operate in the service of decision support. In our metaphor, we know that a telephone conference call must not drop participants and must be free of background noise, but its participants will also benefit from having an agenda, speaking a common language, knowing who else is on the call, having their thoughts organized, and sharing similar goals for their meeting. Thus, better articulation of the work processes to be supported on the ‘‘decision side’’ of the figure is prerequisite to any technology that could ultimately better serve those processes. Fig. 1 Measurement feedback systems in the context of health information technology and clinical decision support. MFS measurement feedback systems 123 Adm Policy Ment Health (2016) 43:471–477 There is another matter, not represented in Fig. 1, involving the human implementation challenges that arise once appropriate technologies are developed. For example, Gleacher et al. (2015) highlighted the key facilitating role of organizational leadership in achieving widespread use of the contextualized feedback system. Without such support, providers may mute the phone calls, or agencies may disconnect the phone service altogether, metaphorically speaking. This is but one of many examples that involve technology implementation issues rather than design issues (of course the two are inevitably connected in the real world; e.g., Higa-McMillan et al. 2011; Lyon et al. 2015; and in treatment contexts, design alone has been associated with implementation success; e.g., Palinkas et al. 2013, Southam-Gerow et al. 2014). Nevertheless, in the measurement feedback context, this issue’s authors provide considerable discussion of implementation, so we focus on a brief list of ideas relevant to clinical decision support and its broad intersection with technology, followed by a simple illustration. These ideas include considerations for sources of the evidence to be displayed, the value of exposing discrepancies between what has happened and what should or could happen, the types and configuration of evidence to be displayed (e.g., beyond progress rating alerts or plots) to allow users to impose a logic model on interpretation, the importance of automated translation of relevant ontologies (e.g., DSM diagnoses vs. elevated scales on a standardized symptom measure), and the creation of structures to facilitate communication and collaboration. Multiple Evidence Bases Daleiden and Chorpita (2005) outlined a model to coordinate and inform service delivery, which among other things described four core evidence bases relevant to decisionmaking: case-specific historical information, local aggregate evidence, general services research, causal mechanism research. These are outlined in Table 1, with reference to corresponding traditions or schools of thought as well as example questions addressed by each. In the current context of MFSs, there is a predominant emphasis on only one of these four sources of evidence—the case-specific history (but see Steinfeld et al. 2015, for examples of using local aggregate evidence in the form of departmental and system level reports). We feel much can be gained from coordinating a fuller set of relevant information from all four of the evidence bases, which gives us the ability to detect, consider, and act on knowledge that might otherwise remain out of view, lost in our decision-making ‘‘blind spots.’’ This task is not possible without a significant amount of ‘‘background complexity,’’ given that each evidence base Adm Policy Ment Health (2016) 43:471–477 473 Table 1 Four evidence bases relevant to clinical decision making (‘‘Evidence-Based Services System Model’’) Evidence base Relevant tradition Example questions Case-specific historical information Individualized case conceptualization What is the primary clinical concern? Is this youth improving? Local aggregate evidence Practice-based evidence What is the most common focus of care? How is the system performing on average for that specific group? General Services Research (evidence-based practice) Evidence-based practice What are the best supported treatment approaches for a defined group? How well have they worked in research trials? Causal Mechanism Research (clinical theory) Individualized case conceptualization What would theory suggest is a promising next step? What is a logical expectation for the outcome? can have multiple indicators, which can even disagree (e.g., two independent randomized trials with discrepant findings; improvement on one measure of depression with deterioration on another). The challenge of determining which knowledge is ‘‘best’’ is likely an impossible pursuit, but fortunately, perhaps not a necessary one. Rather, a sufficient knowledge management function may be for systems to prioritize ‘‘better’’ knowledge from a number of sources through a series of rules or knowledge filters (e.g., psychometric validation for case-specific measures; strength of evidence models for literature review; e.g., Chorpita et al. 2011). This is possible because MFSs inherently create a self-correcting context, in which the real validation of the knowledge used is accomplished by observing whether the desired outcomes were achieved. This notion reflects our fundamental belief that a legitimate function of these systems can be to provide multiple promising contextualized ideas to a decision maker, rather than merely to provide a ‘‘right answer’’ or single prescribed action. In other words, ultimately an agent must prioritize, act, and test results based on the information made available, and the technology should support that process rather than replace it. from the one indicated by strong research support, then there is a discrepancy between the observed value and the expected value. Such discrepancies can motivate and guide action, perhaps in this case to consideration of a different treatment, and we expect that a core function of MFS is to assist with making these discrepancies known. Several papers in this issue provide examples of using expected values. For example, Steinfeld et al. (2015) describe reporting related to expected measure completion at each encounter. Similarly, Bickman et al. (2014) illustrate alerts related to the expectation that feedback reports be viewed by practitioners. Similarly, the model-specific implementations (e.g., Bruns et al. 2015; Nadeem et al. 2015) communicate an expectation that particular service activities occur, simply by incorporating descriptions of those activities into the workflow and visual displays. Although there is evidence that observed-value-only feedback offers advantages over no feedback (e.g., Lambert et al. 2005), it is worth considering the decision support value afforded by contextualizing these with expected values. Multiple Domains Expected and Observed Values In clinical care, expected values (e.g. Chorpita et al. Research Network on Youth Mental Health 2008) refer to information that informs our best guesses about what should happen. For instance, if one needed to select a treatment that might work for a given youth, one might consider research trials involving similar youth to inform that decision (as would be consistent with the evidencebased treatment paradigm); the treatment with the best research support is thus the expected value for the treatment to be delivered. Expected values can be contrasted with observed values, which represent information about what has happened or is happening now. Staying with this example, if the youth is receiving a treatment that differs In the same manner that MFS often prioritize the casespecific evidence base and observed values, to date many systems have also placed a heavy emphasis on the progress domain (e.g., visualizing symptom change over time). However, to facilitate technology’s relevance to clinical decision making, we think MFS platforms should help examine any type of information (not just progress) that fits within the larger decision model used to guide care. For instance, if one believes practice is related to progress, then organizing information about practices delivered over time and configuring that information to be synchronized with progress measurements might be of considerable value. More generally, any events (e.g., change of medication, change of placement, stakeholder participation, session noshows, or end of school year) deemed relevant to progress 123 474 interpretation or strategy selection may be useful for decision support display. This special issue provides two illustrations of strategies for extending beyond progress measurement. For example, Nadeem et al. (2015) show how to track practices delivered directly on the feedback reports. Using a different strategy, Bruns et al. describe integration of service activities into the workflow of the system itself. Multiple Languages In keeping with our telecommunication metaphor, in addition to a shared channel, senders and receivers must share a language and concept system to transfer knowledge. Accordingly, we see a need for translating across the diverse ontologies that are found in mental health research and services (e.g., Diagnostic and Statistical Manual, Research Domain Criteria, Standardized Instrument Scores, Evidence-Based Practices, Practice Elements, etc.; see Chorpita and Daleiden 2014). Constraints that require a single common language (e.g., a fixed set of measures; a single clinical model) are less likely to generalize to diverse contexts and to facilitate communication in the language of the local jurisdiction or system. The papers in this special issue clearly illustrate the underlying dilemma. Several of the systems describe a capacity to support multiple outcome measures (e.g., Bickman et al. 2014; Bruns et al. 2015; Nadeem et al., 2015), whereas Steinfeld et al. (2015) highlight some benefits of committing to a single measurement model even though their electronic medical record could potentially support many. On the practice metric side, Bruns et al. (2015) illustrate the construction and use of a model-specific system whereas Nadeem et al. (2015) illustrate a modelspecific configuration of a generalized platform for progress and event (practice) representation. In our work with systems, we have found tremendous value in the ability to support diversity (of models, measures, display preferences, etc.) within a single platform, but think that diversity is best supported in the context of a strong default configuration that is designed to bias users initially toward ‘‘best practices,’’ while allowing extension and adaptation as user expertise develops. For this to happen, ‘‘translator’’ functions (e.g., is an elevated score on the Children’s Depression Inventory sufficiently similar to a DSM-III-R diagnosis of Major Depression to draw an inference for this youth?) as well as diverse ontological libraries (e.g., configurable lists of practice elements, evidence-based protocols, or other metrics to represent practice delivery) are a necessary infrastructure operation for MFS. 123 Adm Policy Ment Health (2016) 43:471–477 Collaboration Another fundamental premise is that MFS should both foster and structure collaboration. Rather than serving as a substitute for human decision makers, we believe a key role of these systems is to organize and inform those involved in care. Collaboration can be an implicit feature, for example, by requiring treatment team members to select targets and measures as well as selecting benchmarks (i.e., choosing expected values from various evidence bases); or it can be a more explicit feature, for example, by contiguously displaying scores from multiple informants or practices delivered by different members of the treatment team, allowing a full view of team activities and perspectives. Dynamic configuration, such as being able to toggle elements on and off, extends this capability, allowing different views for different users (e.g., sharing progress and practice history with a family member). Bruns et al. (2015) describe features built into the workflow of the TMSWrapLogic system that prompt the type of collaboration that is central to the wraparound service model, and Lyon et al. (2015) found in their contextual assessment that communication, both internal and external, were key functions of service providers. An Illustration Nathan is a 17 year old Asian American male receiving treatment for depression. Figure 2 shows parent and selfreported depression scores on a depression scale over time in days (plots a and b). In terms of the concepts above, this panel represents the case-specific evidence base, using observed values, in a single domain (progress), in a single language (T-scores on a standardized measure). In Fig. 3, we enrich the display in a number of ways. First, the progress panel now displays two additional plots (c and d), corresponding to expected values. Both can therefore be thought of as scores representing goal states. When selecting expected values, it helps to consider all four evidence bases outlined in Table 1, keeping in mind that one or more expected value could be derived from each evidence base. For example, a case-specific expected value might be a discharge score from a previous successful treatment for Nathan. In this example, plot c is derived from the local aggregate evidence base and represents the average post-treatment score for youth in the system who had received treatment for depression. Plot d is taken from the general services research evidence base, and represents pre-post scores from a randomized clinical trial for depression that included youth of the same age and ethnicity as Nathan. Of note is that simply adding expected Adm Policy Ment Health (2016) 43:471–477 475 Progress and Practice Monitoring Tool Case ID: NZ Age (in years): 17.4 Treatment Target: Depression Gender: Male Ethnicity: Asian American 85 Progress Measures 80 Left Scale a RCADS-Depression 75 b RCADS-P-Depression 70 65 60 Right Scale 55 50 180 160 140 120 100 80 60 40 20 0 45 Fig. 2 A progress panel with observed values for depression scores over time. RCADS revised child anxiety and depression scale, RCADS-P revised child anxiety and depression scale-parent version Progress and Practice Monitoring Tool Case ID: NZ Age (in years): 17.4 Treatment Target: Depression Gender: Male Ethnicity: Asian American 85 1 Progress Measures 80 Left Scale a RCADS-Depression 1 75 b RCADS-P-Depression 1 70 c Benchmark 65 d Trend 1 60 Right Scale 0 55 0 50 45 Literature Practices Performed 180 160 140 120 100 80 60 40 20 0 0 Expert ******* FOCUS ******* Relationship/Rapport Building Child Psychoed: Depression Psychoed: Depression (CG) Activity Selection Cognitive: Depression Problem Solving Relaxation Social Skills Communication Skills (CG) Support Networking (CG) Goal Setting (CG) Maintenance (CG) ******* INTERFERENCE ******* Child Psychoed: Anxiety Cognitive: Anxiety Exposure Self-Rewards Fig. 3 A progress and practice panel showing observed and expected values over time. CG indicates a caregiver directed practice, RCADS revised child anxiety and depression scale, RCADS-P revised child anxiety and depression scale-parent version, Psychoed psychoeducation. The shaded region of the practice panel indicates a hypothetical illustration of practices supported by relevant research trials (i.e., expected values for practice) 123 476 values for treatment progress can have a dramatic effect on interpretation of progress by creating context. That is, it is quite possible to infer satisfactory progress when examining Fig. 1, but less possible when inspecting the top panel of Fig. 2, given the discrepancies between observed and expected values. This discrepancy may effectively indicate when to act, but without additional information, it may say less about how to act. Given a basic logic model that practices affect outcomes, inclusion of a practice panel can help in this regard. The bottom of Fig. 2 uses white circles plotted on the same time axis as the progress ratings to indicate that Nathan has had 15 separate treatment sessions involving 9 different clinical procedures. This panel is one place to explore to determine why he has lagged the expected rate of progress. Once again, expected values can help, and plotting practices coded from evidence-based treatments (which is in essence, a translation exercise, as noted above) indicates that the 12 practices in the ‘‘focus’’ region of the practice panel are part of an evidence-based protocol for depression in adolescents. Comparing these expected practices with those observed, we find that three sessions involved ‘‘off focus’’ practices, targeting anxiety (possible errors of commission), and only one of five caregiver-directed practices has been delivered (communication skills), occurring 150 days into treatment (possible errors of omission). One other notable observation is the increasing latency between sessions starting at about day 60. A treatment team may thus hypothesize that caregiver engagement may be an issue, and begin deeper inquiry, which could include adding additional measures to the progress panel (e.g., a caregiver assessment of barriers to treatment or treatment expectancy) or otherwise enhance caregiver services. The system remains interactive, collaborative, and exploratory. Once the system may have indicated how to act, it should ideally support the treatment team in taking the next steps, including referencing the provider analogue of the case-specific history: does this provider have experience and expertise with promoting caregiver engagement? If not, are there written materials or learning resources that could be launched from the display to facilitate action? Of course, this is but one example, restricted to two domain panels, two of the evidence bases, often with only a single expected value from each. The possibilities, however, can be as varied as conversations, consistent with our telecommunication metaphor. When everything aligns neatly, these conversations can be swift and clear, indicating next steps, but even when information does not align (e.g., the research literature indicates one set of expected values for practice, whereas an expert supervisor recommends a different set of practices), the communication and collaboration is biased toward investigating and resolving 123 Adm Policy Ment Health (2016) 43:471–477 the discrepancy prospectively using evidence. More complex examples and illustrations are available elsewhere in the literature, specifically regarding multiple provider teams (e.g., Bruns et al. 2014); observed and expected values and model integrity (Chorpita et al. 2008; Regan et al. 2013), coordination of multiple evidence bases (e.g. Daleiden et al. 2005), and complex collaboration structures (Chorpita and Daleiden 2014). Conclusion This group of authors is to be commended for their efforts to implement and evaluate MFS in a variety of real-world contexts. The promises of such systems are clear, and the design challenges, although considerable, can and will be resolved. For technology to serve our will, however, our field must continue to wrestle with models for how we wish to select and organize health information. Thus, we disagree with the notion that this burden lies with HIT developers. There is, for the moment, a significant underspecification of the general information and decision models needed to dictate the functional requirements of promising new technologies. Technology will do what we tell it to, and thus, the burden, for now, lies primarily with mental health experts. That said, if we do our jobs right, the complexity of these fully articulated models may soon become a constraint. Our theories of psychopathology have moved to multifactorial risk and protective factor models, and our intervention research has identified a multitude of interventions that, although effective when measured at the group level, involve uncertainty at the case level. To help manage that uncertainty, the current technologies struggle tremendously and with only modest success at displaying a ‘‘human readable’’ form of a ‘‘progress only’’ model, much less a basic practice-yields-progress logic model. Although we encourage practice-progress type models for current applications to help those using the technology of today, we await the truly disruptive technology needed to support the highly elaborated models necessary to help humans contextually detect and respond to abstract phenomena (e.g., behavior, interactions) in the service of their personal pursuits and in their duty to help others. References Bickman, L. (2008). A measurement feedback system (MFS) is necessary to improve mental health outcomes. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 1114–1119. Bickman, L., Douglas, S. R., De Andrade, A. R. V., Tomlinson, M., Gleacher, A., Olin, S., et al. (2014). Implementing a Adm Policy Ment Health (2016) 43:471–477 measurement feedback system: A tale of two sites. Administration and Policy in Mental Health and Mental Health Services Research, 1–16. doi:10.1007/s10488-015-0647-8. Bruns, E. J., Hyde, K. L., Sather, A., Hook, A. N., & Lyon, A. R. (2015). Applying user input to the design and testing of an electronic behavioral health information system for wraparound care coordination. Administration and Policy in Mental Health and Mental Health Services Research, 1–19. doi:10.1007/ s10488-015-0658-5. Bruns, E. J., Walker, J. S., Bernstein, A. D., Daleiden, E. L., Pullmann, M. D., & Chorpita, B. F. (2014). Family voice with informed choice: Coordinating wraparound with research-based treatment for children and adolescents. Journal of Clinical Child and Adolescent Psychology, 43, 256–269. Chorpita, B. F., Bernstein, A., Daleiden, E. L., & Research Network on Youth Mental Health. (2008). Driving with roadmaps and dashboards: Using information resources to structure the decision models in service organizations. Administration and Policy in Mental Health and Mental Health Services Research, 35(1–2), 114–123. Chorpita, B. F., & Daleiden, E. L. (2014). Structuring the collaboration of science and service in pursuit of a shared vision. Journal of Clinical Child and Adolescent Psychology, 43, 323–338. Chorpita, B. F., Daleiden, E. L., Ebesutani, C., Young, J., Becker, K. D., Nakamura, B. J., et al. (2011). Evidence-based treatments for children and adolescents: An updated review of indicators of efficacy and effectiveness. Clinical Psychology: Science and Practice, 18, 153–171. Daleiden, E., & Chorpita, B. F. (2005). From data to wisdom: Quality improvement strategies supporting large-scale implementation of evidence-based services. Child and Adolescent Psychiatric Clinics of North America, 14, 329–349. Gleacher, A. A., Olin, S. S., Nadeem, E., Pollock, M., Ringle, V., Bickman, L., et al. (2015). Implementing a measurement feedback system in community mental health clinics: A case study of multilevel barriers and facilitators. Administration and Policy in Mental Health and Mental Health Services Research, 1–15. doi:10.1007/s10488-015-0642-0. 477 Higa-McMillan, C. K., Powell, C., Daleiden, E., & Mueller, C. W. (2011). Pursuing an evidence-based culture through contextualized feedback: Aligning youth outcomes and practices. Professional Psychology: Research and Practice,. doi:10.1037/a0022139. Lambert, M. J., Harmon, C., Slade, K., Whipple, J. L., & Hawkins, E. J. (2005). Providing feedback to psychotherapists on their patients’ progress: Clinical results and practice suggestions. Journal of Clinical Psychology, 61, 165–174. doi:10.1002/jclp. 20113. Lyon, A. R., Wasse, J. K., Ludwig, K., Zachry, M., Bruns, E. J., Unützer, J., et al. (2015). The Contextualized Technology Adaptation Process (CTAP): Optimizing health information technology to improve mental health systems. Administration and Policy in Mental Health and Mental Health Services Research, 1–16. doi:10.1007/s10488-015-0637-x. Nadeem, E., Cappella, E., Holland, S., Coccaro, C., & Crisonino, G. (2015). Development and piloting of a classroom-focused measurement feedback system. Administration and Policy in Mental Health and Mental Health Services Research, 1–15. doi:10.1007/s10488-015-0651-z. Palinkas, L. A., Weisz, J. R., Chorpita, B. F., Levine, B., Garland, A., Hoagwood, K. E., & Landsverk, J. (2013). Continued use of evidence-based treatments after a randomized controlled effectiveness trial: A qualitative study. Psychiatric Services, 64, 1110–1118. Regan, J., Daleiden, E. L., & Chorpita, B. F. (2013). Integrity in mental health systems: An expanded framework for managing uncertainty in clinical care. Clinical Psychology: Science and Practice, 20, 78–98. Southam-Gerow, M. A., Daleiden, E. L., Chorpita, B. F., Bae, C., Mitchell, C., Faye, M., & Alba, M. (2014). MAPping Los Angeles County: Taking an evidence-informed model of mental health care to scale. Journal of Clinical Child and Adolescent Psychology, 43, 190–200. Steinfeld, B., Fraynt, R., & Simon, G. (2015). Progress monitoring in an integrated health care system: Tracking behavioral health vital signs. Administration and Policy in Mental Health and Mental Health Services Research, 1–10. doi:10.1007/s10488015-0648-7. 123 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Aller et al. BMC Health Services Research (2015) 15:323 DOI 10.1186/s12913-015-0968-z RESEARCH ARTICLE Open Access Development and testing of indicators to measure coordination of clinical information and management across levels of care Marta-Beatriz Aller1*, Ingrid Vargas1, Jordi Coderch2, Sebastià Calero3, Francesc Cots4, Mercè Abizanda5, Joan Farré6, Josep Ramon Llopart7, Lluís Colomés8 and María Luisa Vázquez1 Abstract Background: Coordination across levels of care is becoming increasingly important due to rapid advances in technology, high specialisation and changes in the organization of healthcare services; to date, however, the development of indicators to evaluate coordination has been limited. The aim of this study is to develop and test a set of indicators to comprehensively evaluate clinical coordination across levels of care. Methods: A systematic review of literature was conducted to identify indicators of clinical coordination across levels of care. These indicators were analysed to identify attributes of coordination and classified accordingly. They were then discussed within an expert team and adapted or newly developed, and their relevance, scientific soundness and feasibility were examined. The indicators were tested in three healthcare areas of the Catalan health system. Results: 52 indicators were identified addressing 11 attributes of clinical coordination across levels of care. The final set consisted of 21 output indicators. Clinical information transfer is evaluated based on information flow (4) and the adequacy of shared information (3). Clinical management coordination indicators evaluate care coherence through diagnostic testing (2) and medication (1), provision of care at the most appropriate level (2), completion of diagnostic process (1), follow-up after hospital discharge (4) and accessibility across levels of care (4). The application of indicators showed differences in the degree of clinical coordination depending on the attribute and area. Conclusion: A set of rigorous and scientifically sound measures of clinical coordination across levels of care were developed based on a literature review and discussion with experts. This set of indicators comprehensively address the different attributes of clinical coordination in main transitions across levels of care. It could be employed to identify areas in which health services can be improved, as well as to measure the effect of efforts to improve clinical coordination in healthcare organizations. Keywords: Quality indicators, Coordination across levels of care, Clinical management coordination, Clinical information coordination, Health services research Background Healthcare systems are in a constant process of adaptation due to rapid advances in technology, new treatments, high specialisation and changes in the organization of health services [1]. As a consequence, patients are seen by an everexpanding array of different providers in a variety of * Correspondence: maller@consorci.org 1 Health Policy and Health Services Research Group, Health Policy Research Unit, Consortium for Health Care and Social Services of Catalonia, Avenida Tibidabo, 21, 08022 Barcelona, Spain Full list of author information is available at the end of the article locations, making coordination difficult [1, 2]. This is particularly challenging in the care of patients with chronic and multiple conditions, who tend to use healthcare services more frequently and use a greater array of services than other patients [3, 4]. Clinical coordination across levels of care should prevent wasteful duplication of diagnostic testing, perilous polypharmacy and conflicting care plans [5, 6]; thus the effects of clinical coordination extend beyond cost reduction through improving quality of care [7–9]. This study is set within a conceptual framework for analysing the performance of integrated healthcare © 2015 Aller et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Aller et al. BMC Health Services Research (2015) 15:323 networks, which is based on an extensive literature review [6, 10] and could be applied in any healthcare area that arranges to provide a coordinated continuum of services to a defined population. In this framework, clinical coordination, together with continuity of care and access to health services, is considered an intermediate objective of integrated healthcare networks and is regarded as a means by which to reach the ultimate objectives of quality of care, efficiency and equity of access [6, 10, 11]. To analyse the achievement of these objectives, both external and internal processes and contextual factors are taken into account, as well as the different perspectives (services, professionals and users) and approaches. In this conceptual framework, clinical coordination is defined as the harmonious connection of the different health services needed to provide care to a patient throughout the care continuum in order to achieve a common objective without conflicts [10, 12]. Continuity of care refers to how individual patients experience coordination of services, and it is defined as the degree to which patients experience care over time as coherent and linked [1]. Clinical coordination across levels of care consists of the coordination of both clinical information and clinical management [6, 10]. Clinical information coordination is the transfer and use of patients’ clinical information in order to harmonize activities between providers, and consists of two dimensions: transfer of clinical information and the use of this information [13]. Clinical management coordination is the provision of care in a sequential and complementary way according to a healthcare plan shared by the different services and healthcare levels involved, and consists of three dimensions: care coherence (i.e., the existence of similar approaches and treatment objectives among professionals from different levels of care), follow-up across care levels (i.e., the adequate monitoring of the patient when there are transitions from one care setting to another) and accessibility across levels (provision of care without interruption across levels of care throughout the clinical episode of the patient) [13]. The results of clinical coordination can be assessed by analysing processes aimed at coordination or their outputs (immediate results of activities related to clinical coordination) or outcomes (final expected middle-long term results of clinical coordination, such as hospital readmissions or avoidable hospital admissions), and using different perspectives (services, professionals, users (continuity)). The focus of this study relies on measures to assess the outputs of clinical coordination across levels of care (primary and secondary) by using service-based indicators. Despite the interest this subject has generated, there are still important gaps in terms of measures to assess clinical coordination across levels of care and the development of new indicators continues to be considered a priority in health policy and health services research [14, 15]. Many of the attempts to address this to date have Page 2 of 16 focused on developing indicators to measure healthcare outcomes which are attributed to improvements in clinical coordination [16]. However, the development of output indicators has been limited, and without this type of indicators it is not possible to conclude that outcomes in health care can be attributed to improvements in clinical coordination across levels of care [15]. Existing sets of indicators are usually designed to analyse a single dimension (e.g. transfer of information) or attribute (e.g. due completion of referral forms and discharge reports) of clinical coordination [17–20]. Those which address more than one dimension of clinical coordination are not exhaustive in their approach to clinical coordination and are often insufficiently operative or are not directed at the assessment of clinical coordination across levels of care [21–25]. Furthermore, the conceptual framework used to develop these measures is not generally explained in detail, so it is not obvious exactly which aspects of clinical coordination are being analysed or how measures relate to clinical coordination. As a result of these issues, there is an overrepresentation of some dimensions of clinical coordination addressed by indicators, whilst other dimensions have scarcely been investigated [26]. Studies have concentrated in particular on the transfer of clinical information [22–24, 27–30], especially in terms of completeness of information in discharge reports [22, 30–34] and to a lesser degree in emergency reports [30] and referral forms [20, 35], and on the followup of patients and accessibility across care levels [22, 24, 29, 30, 36]. Only a few studies have used indicators to measure clinical coherence between care levels [30, 37]. The aim of this study is to develop and test a set of output indicators to comprehensively evaluate clinical coordination across care levels of care, i.e. addressing both types of clinical coordination, information and management, and their dimensions and attributes. Methods The study consisted of two phases: in the first phase, a set of indicators to measure clinical coordination across levels of care was developed based on the literature review and expert discussions, and in the second phase, the set was tested in three different healthcare areas. 1. Development of a set of indicators to measure clinical coordination across levels of care Identification of indicators: literature review The study was based on the conceptual framework for analysing the performance of integrated healthcare networks [6, 10], which identifies two types of clinical coordination across levels of care (clinical information and clinical management) and five dimensions (transfer of information, use of information, care coherence, follow-up across levels and accessibility across levels). A systematic Aller et al. BMC Health Services Research (2015) 15:323 review of literature was undertaken to identify previously developed indicators. A computerised search of the following bibliographic databases was conducted: Pubmed, Social Science Citation Index, Science Citation Index, ECONLIT, CINAHL and LILACS, in addition to standard internet search engines such as Google. The search strategy included a combination of descriptors and keywords relating to clinical coordination (‘coordination of care’ or associated key terms with similar meaning), levels of care (‘primary care’, ‘secondary care’, ‘hospitalization’, ‘interface’, ‘cross-level’ or associated terms) and measurement tools (‘measure’, ‘indicator’ or associated key terms), making use of the Boolean operator ‘AND’. References from retrieved studies were also screened for possible omissions....
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Running head: HEALTHCARE INFORMATION SYSTEM

Healthcare Information System
Student’s Name
Institutional Affiliation

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HEALTHCARE INFORMATION SYSTEM

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Healthcare Information System
Healthcare information system (HCIS) is a computer network that record, govern, store
or relay information regarding one's health and activities of a health institution such as disease
surveillance. This system are a disposition of data, processes, persons, and computer technology
that work together to acquire, process, and generate essential information to support a health care
institution as an output (Wager, Lee, & Glaser, 2017). HCIS is divided into two primary
categories namely clinical and administrative information system, which are distinguished by the
type and the purpose of the data contained. An administrative data network primarily holds
financial or managerial data such as personnel, equipment, finances, and supplies, which is
generally utilized to support organizational functions and general undertakings of a health care
institution (Wager, Lee, & Glaser, 2017). On the other hand, a clinical information system (CIS)
consist health-related or clinical information utilized by health caregivers in diagnosis, treatment,
and monitoring patient's care. Examples of CIS are departmental systems, electronic health
record networks, computerized provider order entry, and medication administration systems.
Health Care Delivery System Models
Traditionally, healthcare delivery was based on a fee-for-service payment model where
payment was largely dependent on quantity instead of the quality of care (Adler, Cutler,
Jonathan, Galea, Glymour, Koh, & Satcher, 2016). This has led to redesigning of the healthcare
system to boost on the standard of care, which has resulted to the evolution of a number of
models including patient-centered medical homes (PCMH), dual eligible, bundledreimburseme...


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