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An Overview of Clinical Informatics
Chapter 1
An Overview of Clinical Informatics Kathrin M. Cresswell1, David W. Bates2, Adam Wright3 and Aziz Sheikh1,4 1
The University of Edinburgh, Edinburgh, United Kingdom, 2Harvard Medical School; Harvard School of Public Health, Boston, MA, United States, 3Harvard Medical School; Partners HealthCare, Boston, MA, United States, 4Brigham and Women’s Hospital/Harvard Medical School, Boston, MA, United States
INTRODUCTION: THE EVOLVING AND EXPANDING ROLE OF INFORMATION TECHNOLOGY Information technology (IT) is increasingly pervading everything we do. For instance, worldwide statistics show that more people have access to a mobile phone than to working toilets (Times Magazine, 2013), and while the existence of genome editing may have seemed unimaginable just a few years ago, tools have now equipped scientists with the ability to genetically modify human embryos (Liang et al., 2015). Such developments are occurring exponentially with an increasing array of technological features, designs, and data generated from applications impacting on all aspects of human life including food, health, energy, and the environment. The first personal computer was released in 1974, and within a mere seven decades, by 2045 computing power is expected to exceed that of all human brains combined. This “technological singularity” is a widely debated hypothetical moment in time where artificial intelligence (AI) will surpass our cognitive limitations (Kurzweil, 2005). Not surprisingly, most industries have drawn heavily on technological developments to transform their services, and although somewhat lagging behind, healthcare is following suit, driven by increasing pressures on health systems to improve quality, reduce errors, and increase efficiency (Travis et al., 2004). This chapter will provide an overview of past, present, and future developments in the area of clinical informatics. It will introduce the most important concepts and definitions, provide a high-level perspective of the existing empirical evidence base in relation to the effectiveness of health IT (HIT), and provide a contextual overview of the chapters in this book.
Key Advances in Clinical Informatics. DOI: http://dx.doi.org/10.1016/B978-0-12-809523-2.00001-7 © 2017 Elsevier Inc. All rights reserved.
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PART | I An Introduction to Clinical Informatics
The following three sections serve as an overall structure: Section 1 introduces clinical informatics as a discipline, outlining key terms and tensions; Section 2 tackles issues surrounding the impact of clinical informatics applications on quality, safety, and efficiency of care; and Section 3 delves deeper into future developments that are likely to dominate the sector in the foreseeable future, though undoubtedly many other developments will occur that cannot yet be predicted.
A BRIEF HISTORY OF THE FIELD OF CLINICAL INFORMATICS The first use of HIT in a clinical setting can be traced back to 1952, when Dr. Arthur Rappoport reported his experiences with using the McBee Manual Punch Card in a pathology laboratory setting (Porth and Lu¨bke, 1996). This was followed by the emergence of hospital information systems in the 1960s, with the Latter Day Saints Hospital in Utah (USA, now Intermountain) being the first to implement this in 1967. Others, including The COmputer STored Ambulatory Record and the Regenstrief Medical Record System, followed. The Health Evaluation through Logical Programming system had the ability to collect demographic and clinical data with decision support features. It is used to the present day, but may be replaced by Cerner in the near future (Gardner et al., 1999; Healthcare IT News, 2015). The development of clinical specialty systems for laboratory, radiology, pathology, radiotherapy, pharmacy, and primary care followed. Integration of these was not possible until the 1980s, when larger integrated medical information systems emerged, facilitated by the development of high-speed communication networks. In 1985, the first patient scheduling software called “Cadence” was launched by Epic Systems, followed by EpicCare in 1992. Subsequent developments in the 21st century have been characterized by growing clinical uses of technologies drawing on an ever increasing array of data sources (including patients and various care settings), mobile applications that allow patients and providers to gather and view data “on the go”, and the exploitation of digital data generated for reuse (Cresswell and Sheikh, 2016). An overview of key historical developments is provided in Box 1.1.
WHAT IS CLINICAL INFORMATICS? Clinical informatics represents a highly interdisciplinary field that involves “analyzing, designing, implementing, and evaluating information and communication systems that enhance individual and population health outcomes, improve patient care, and strengthen the clinician-patient relationship (Gardner et al., 2009).” As such, the field can be positioned at the intersection of clinical care, the health system, and information and communication technology. Its interdisciplinary nature is a core feature, and it includes
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BOX 1.1 Key Historical Developments in Clinical Informatics1,2,3 1949: establishment of the German Society for Medical Documentation, Computer Science and Statistics (first professional informatics organization) 1950s: first time IT applied to the field of medicine in biomedical context 1952: Arthur Rappoport reported on using the McBee Manual Punch Card in a laboratory setting 1960s: first peer-reviewed informatics journals launched 1960s: emergence of hospital information systems that included digital patient information 1967: Latter Day Saints Hospital in Utah first hospital to use an Electronic Health Record (EHR) 1970s: first mention of English term “medical informatics” 1960s/70s: clinical specialty systems were developed for laboratory, radiology, pathology, radiotherapy, pharmacy, and primary care 1970: first Computerized Physician Order Entry system used in El Caminio Hospital, California 1980s: development of local, national, and worldwide high-speed communication networks 1980s: emergence of larger integrated medical information systems 1985: first patient scheduling software launched (Cadence) 1988: creation of the American Medical Informatics Association 1990s: emergence of the internet facilitating exchange of clinical data 1992: EpicCare launched 2000s: clinical users could use IT to view/order tests/medications from various databases 2010s: emergence of cloud networks and integration of data across multiple locations 1. Collen, M.F., 2015. A History of Medical Informatics in the United States. Ball, M.J. (Ed.), Springer, New York. 2. Hayes, G.M., Barnett, D.E. (Eds.), 2008. UK Health Computing: Recollections and Reflections. British Computer Society. 3. http://www.healthworkscollective.com/frankie-xavier/162251/long-road-digitization-historyhealthcare-informatics
clinical providers such as physicians, nurses, and pharmacists, but also medical librarians, information scientists, and communication specialists, to name just a few of the types of professionals involved. Related terms that are sometimes used interchangeably include health informatics, medical informatics, and eHealth. There are a range of published definitions with varying understandings of the field in the published literature—the exponential development of applications and increasing convergence of functionalities complicates navigating the area further (Boogerd et al., 2015; Oh et al., 2005). Various chapters of this book will delve deeper into specific applications and associated concepts. These begin with
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PART | I An Introduction to Clinical Informatics
overviews of inpatient systems, outpatient systems, and clinical documentation in Chapters 2, 3, and 4, respectively. Overall, existing applications can broadly be divided into three categories (Black et al., 2011): (1) systems informing and supporting decisions (see Chapter 10 on medication, laboratory, and radiology testing; and Chapter 12 on knowledge management and computerized guidelines); (2) storage and management of data (Chapter 11 on bioinformatics and precision medicine); and (3) delivery of expertise and care at a distance (see Chapter 13 on mobile health). There has been an increasing emergence of clinical informatics as a discipline (Greenes and Shortliffe, 1990). Associated activity includes the growing demand for organizational capacity in this area, but also the need for academic expertise to develop new educational trajectories and evaluate ongoing implementation, adoption, and optimization activities associated with the increasing range of technologies. Formal accreditation and certification of clinical informatics expertise are closely associated activities that are presently receiving attention (Fridsma, 2015; Gadd et al., 2016; Shortliffe et al., 2016), particularly in the United States (Middleton, 2014), but also many other countries.
EMPIRICAL EVIDENCE SURROUNDING EFFECTIVENESS OF CLINICAL INFORMATICS APPLICATIONS Clinical informatics applications have been shown to result in a number of benefits including, among others, the prevention of life-threatening allergic reactions to medication through systems facilitating clinical decision making (Bates et al., 1999; Kaushal et al., 2003), reductions in prescribing errors (Avery et al., 2012), and the ability to manage diabetes and high blood pressure remotely (Wild et al., 2016). However, it is often difficult to demonstrate the clinical effectiveness and cost-effectiveness of HIT (Black et al., 2011; Chaudhry et al., 2006; Jones et al., 2014), this at least in part reflecting the need for workflow reconfiguration and systems optimization (Cresswell et al., 2017). Chapter 8 will explore HIT and value in more detail, while Chapter 14 will examine the impact of technology on safety. There is increasing understanding of the potential risks associated with the introduction of new technologies in healthcare settings (Black et al., 2011; Buntin et al., 2011). The most commonly examined areas in this respect include privacy, confidentiality, and security (see Chapter 6: Privacy and Security); effects on work practices and interdisciplinary working; and difficulties surrounding accessibility of data (Ash et al., 2004; Barrows and Clayton, 1996; Harrison et al., 2007).
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The underlying reasons for this overall lack of evidence may partly be due to difficulties evaluating technologies, as these are often embedded in wider organizational change initiatives resulting in difficulties attributing effects (Campbell et al., 2000; Lilford et al., 2009). There is also a growing literature reexamining traditional evaluation paradigms advocating randomized controlled trials (RCTs) as the “gold standard”, toward a more flexible use of various evaluation methods including qualitative, mixed methods, human factors, and engineering-based approaches (Cresswell et al., 2017; Klasnja et al., 2011; Yusof et al., 2008). This is because RCTs tend to be costly and time-consuming (resulting in issues surrounding the applicability of results and major challenges associated with changing software/technology) and may not be appropriate for effectively evaluating the range of different rapidly changing existing applications. Conversely, it can be easy and less costly to conduct clinical trials using EHRs, and in industry for example it is now routine to employ “AB” testing, where when it is not clear which of two options is superior, both are tried for half a user base. Whichever is more effective at achieving the desired outcome (e.g., a digital purchase) is then used as the default.
CLINICAL INFORMATICS IN CONTEXT Having touched upon the challenges inherent in evaluating clinical informatics applications above, it is important to briefly discuss the importance of appreciating the range of contextual dimensions and various stakeholders that are involved in deploying and adopting technologies in healthcare (Fig. 1.1) (Cresswell and Sheikh, 2009). Contextual aspects may include technical features (e.g., usability), social contexts (e.g., changes in work practices), organizational strategies (relating to implementation and optimization), and wider sociopolitical dimensions (such as state and federal approaches to implementation and regulatory environments). Stakeholders within these various contexts include patients, academics, providers, vendors, developers, third-sector organizations, and policy makers. All of these have different interests that need to be aligned for initiatives to be successful. Chapter 9 will discuss organizational issues in more detail, while Chapter 7 will examine policy considerations and associated international strategies to promote clinical informatics implementations. Empirical evaluations that take this range of stakeholders and contextual factors into account are now widely advocated (Catwell and Sheikh, 2009). These should involve a longitudinal component to facilitate tracing developments over time, playing an active role in aligning interests through providing formative feedback to stakeholders in participating healthcare settings, and summative feedback to policy makers (Ammenwerth et al., 2003).
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Widermacroenvironment
Organisational context
Social/human factors
Technical factors
FIGURE 1.1 Overview of contextual factors. Factors important for the successful implementation of EHRs identified in the literature. Adopted from Cresswell, K., Sheikh, A., 2009. The NHS Care Record Service (NHS CRS): recommendations from the literature on successful implementation and adoption. J. Innov. Health Inform. 17 (3), 153 160.
VISIONS SURROUNDING FUTURE DEVELOPMENTS IN CLINICAL INFORMATICS A central theme of this book will be examining state-of-the-art developments in clinical informatics, exploring progress toward realizing the vision of more effective, better quality, and safer care through the application of IT in healthcare settings. Key current developments in this respect are likely to include the creation of integrated health informatics infrastructures where data can be seamlessly shared between settings and applications (see Chapter 5 on interoperability, Chapter 16 on application programming interfaces, and Chapter 17 on cloud-based computing), and the creation of learning health systems that effectively draw on digital data collected in a variety of settings and by a variety of stakeholders to improve performance and services (see Chapter 15 on predictive analytics and population health, Chapter 18 on social/consumer informatics, and Chapter 19 on machine learning and AI). It is important to place this work in the context of a continuously evolving field, where innovations are created at a rapid pace. HIT has, if appropriately conceptualized, developed and implemented, the potential to continue to have major transformative effects on healthcare and can through so doing help deal with one of the most pressing healthcare challenges facing healthcare worldwide, namely to achieve more in terms of health gain for less and to support patient involvement/enablement/empowerment.
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CONCLUSIONS Significant international policy efforts and investments in clinical informatics are taking place to improve adoption and use of healthcare IT, with the underlying aim of improving healthcare safety, quality, and efficiency. We have outlined some of the past, present, and potential future developments in this domain and provided an overview of definitions and core issues in the field. Subsequent chapters in this book aim to share state-of-the-art developments with nonexpert clinical and academic audiences across the globe.
REFERENCES Ammenwerth, E., Gra¨ber, S., Herrmann, G., Bu¨rkle, T., Ko¨nig, J., 2003. Evaluation of health information systems—problems and challenges. Int. J. Med. Inform. 71 (2), 125 135. Ash, J.S., Berg, M., Coiera, E., 2004. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J. Am. Med. Inform. Assoc. 11 (2), 104 112. Barrows, R.C., Clayton, P.D., 1996. Privacy, confidentiality, and electronic medical records. J. Am. Med. Inform. Assoc. 3 (2), 139 148. Bates, D.W., Teich, J.M., Lee, J., Seger, D., Kuperman, G.J., Ma’Luf, N., et al., 1999. The impact of computerized physician order entry on medication error prevention. J. Am. Med. Inform. Assoc. 6 (4), 313 321. Black, A.D., Car, J., Pagliari, C., Anandan, C., Cresswell, K., Bokun, T., et al., 2011. The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS Med. 8 (1), e1000387. Boogerd, E.A., Arts, T., Engelen, L.J., van De Belt, T.H., 2015. “What is eHealth”: time for an update? JMIR Res. Protoc. 4 (1), e29. Buntin, M.B., Burke, M.F., Hoaglin, M.C., Blumenthal, D., 2011. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff. 30 (3), 464 471. Campbell, M., Fitzpatrick, R., Haines, A., Kinmonth, A.L., 2000. Framework for design and evaluation of complex interventions to improve health. Br. Med. J. 321 (7262), 694. Catwell, L., Sheikh, A., 2009. Evaluating eHealth interventions: the need for continuous systemic evaluation. PLoS Med. 6 (8), e1000126. Chaudhry, B., Wang, J., Wu, S., Maglione, M., Mojica, W., Roth, E., et al., 2006. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann. Intern. Med. 144 (10), 742 752. Cresswell, K., Sheikh, A., 2009. The NHS Care Record Service (NHS CRS): recommendations from the literature on successful implementation and adoption. J. Innov. Health Inform. 17 (3), 153 160. Cresswell, K.M., Sheikh, A., 2016. Key global developments in health information technology. J. R. Soc. Med. 109 (8), 299 302. Cresswell, K.M., Bates, D.W., Sheikh, A., 2017. Ten key considerations for the successful optimization of large-scale health information technology. J. Am. Med. Inform. Assoc 24 (1), 182 187. Fridsma, D.B., 2015. Update on informatics-focused certification and accreditation activities. J. Am. Med. Inform. Assoc. 22 (2), 489 490.
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Gadd, C.S., Williamson, J.J., Steen, E.B., Fridsma, D.B., 2016. Creating advanced health informatics certification. J. Am. Med. Inform. Assoc. 23 (4), 848 850. Gardner, R.M., Pryor, T.A., Warner, H.R., 1999. The HELP hospital information system: update 1998. Int. J. Med. Inform. 54 (3), 169 182. Gardner, R.M., Overhage, J.M., Steen, E.B., Munger, B.S., Holmes, J.H., Williamson, J.J., et al., 2009. Core content for the subspecialty of clinical informatics. J. Am. Med. Inform. Assoc. 16 (2), 153 157. Greenes, R.A., Shortliffe, E.H., 1990. Medical informatics: an emerging academic discipline and institutional priority. JAMA 263 (8), 1114 1120. Harrison, M.I., Koppel, R., Bar-Lev, S., 2007. Unintended consequences of information technologies in health care—an interactive sociotechnical analysis. J. Am. Med. Inform. Assoc. 14 (5), 542 549. Healthcare IT News, 2015. Intermountain live with Cerner EHR. Available from: http://www. healthcareitnews.com/news/intermountain-live-cerner-ehr (last accessed 01.01.17). Jones, S.S., Rudin, R.S., Perry, T., Shekelle, P.G., 2014. Health information technology: an updated systematic review with a focus on meaningful use. Ann. Intern. Med. 160 (1), 48 54. Kaushal, R., Shojania, K.G., Bates, D.W., 2003. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch. Intern. Med. 163 (12), 1409 1416. Klasnja, P., Consolvo, S., Pratt, W., 2011. How to evaluate technologies for health behavior change in HCI research. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, pp. 3063 3072. Kurzweil, R., 2005. The Singularity Is Near: When Humans Transcend Biology. Penguin. Liang, P., Xu, Y., Zhang, X., Ding, C., Huang, R., Zhang, Z., et al., 2015. CRISPR/Cas9mediated gene editing in human tripronuclear zygotes. Protein Cell 6, 363 372. Lilford, R.J., Foster, J., Pringle, M., 2009. Evaluating eHealth: how to make evaluation more methodologically robust. PLoS Med. 6 (11), e1000186. Middleton, B., 2014. First diplomates board certified in the subspecialty of clinical informatics. J. Am. Med. Inform. Assoc. 21 (2), 384. Oh, H., Rizo, C., Enkin, M., Jadad, A., 2005. What is eHealth (3): a systematic review of published definitions. J. Med. Internet Res. 7 (1), e1. Porth, A.J., Lu¨bke, B., 1996. History of computer-assisted data processing in the medical laboratory. Eur. J. Clin. Chem. Clin. Biochem. 34 (3), 215 229. Shortliffe, E.H., Detmer, D.E., Munger, B.S., 2016. Clinical informatics: emergence of a new profession. In Clinical Informatics Study Guide. Springer International Publishing, pp. 3 21. Time Magazine, 2013. More people have cell phones than toilets, U.N. Study shows. ,http:// newsfeed.time.com/2013/03/25/more-people-have-cell-phones-than-toilets-u-n-study-shows/. (accessed 12.07.16). Travis, P., Bennett, S., Haines, A., Pang, T., Bhutta, Z., Hyder, A.A., et al., 2004. Overcoming health-systems constraints to achieve the Millennium Development Goals. Lancet 364 (9437), 900 906. Wild, S.H., Hanley, J., Lewis, S.C., McKnight, J.A., McCloughan, L.B., Padfield, P.L., et al., 2016. Supported telemonitoring and glycemic control in people with type 2 diabetes: the telescot diabetes pragmatic multicenter randomized controlled trial. PLoS Med. 13 (7), e1002098. Yusof, M.M., Papazafeiropoulou, A., Paul, R.J., Stergioulas, L.K., 2008. Investigating evaluation frameworks for health information systems. Int. J. Med. Inform. 77 (6), 377 385.
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RECOMMENDED FURTHER READING Avery, A.J., Rodgers, S., Cantrill, J.A., Armstrong, S., Cresswell, K., Eden, M., et al., 2012. A pharmacist-led information technology intervention for medication errors (PINCER): a multicentre, cluster randomised, controlled trial and cost-effectiveness analysis. Lancet 379 (9823), 1310 1319. Black, A.D., Car, J., Pagliari, C., Anandan, C., Cresswell, K., Bokun, T., et al., 2011. The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS Med. 8 (1), e1000387. Coiera, E., 2015. Guide to Health Informatics. CRC Press. Collen, M.F., 2015. A History of Medical Informatics in the United States. Springer, New York. Cresswell, K., Blandford, A., Sheikh, A., Reconsidering paradigms for the evaluation of health information technology. Submitted to JAMIA. Hovenga, E.J., 2010. Health Informatics: An Overview. IOS Press.
An Overview of Clinical Informatics Kathrin M. Cresswell1, David W. Bates2, Adam Wright3 and Aziz Sheikh1,4 1
The University of Edinburgh, Edinburgh, United Kingdom, 2Harvard Medical School; Harvard School of Public Health, Boston, MA, United States, 3Harvard Medical School; Partners HealthCare, Boston, MA, United States, 4Brigham and Women’s Hospital/Harvard Medical School, Boston, MA, United States
INTRODUCTION: THE EVOLVING AND EXPANDING ROLE OF INFORMATION TECHNOLOGY Information technology (IT) is increasingly pervading everything we do. For instance, worldwide statistics show that more people have access to a mobile phone than to working toilets (Times Magazine, 2013), and while the existence of genome editing may have seemed unimaginable just a few years ago, tools have now equipped scientists with the ability to genetically modify human embryos (Liang et al., 2015). Such developments are occurring exponentially with an increasing array of technological features, designs, and data generated from applications impacting on all aspects of human life including food, health, energy, and the environment. The first personal computer was released in 1974, and within a mere seven decades, by 2045 computing power is expected to exceed that of all human brains combined. This “technological singularity” is a widely debated hypothetical moment in time where artificial intelligence (AI) will surpass our cognitive limitations (Kurzweil, 2005). Not surprisingly, most industries have drawn heavily on technological developments to transform their services, and although somewhat lagging behind, healthcare is following suit, driven by increasing pressures on health systems to improve quality, reduce errors, and increase efficiency (Travis et al., 2004). This chapter will provide an overview of past, present, and future developments in the area of clinical informatics. It will introduce the most important concepts and definitions, provide a high-level perspective of the existing empirical evidence base in relation to the effectiveness of health IT (HIT), and provide a contextual overview of the chapters in this book.
Key Advances in Clinical Informatics. DOI: http://dx.doi.org/10.1016/B978-0-12-809523-2.00001-7 © 2017 Elsevier Inc. All rights reserved.
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PART | I An Introduction to Clinical Informatics
The following three sections serve as an overall structure: Section 1 introduces clinical informatics as a discipline, outlining key terms and tensions; Section 2 tackles issues surrounding the impact of clinical informatics applications on quality, safety, and efficiency of care; and Section 3 delves deeper into future developments that are likely to dominate the sector in the foreseeable future, though undoubtedly many other developments will occur that cannot yet be predicted.
A BRIEF HISTORY OF THE FIELD OF CLINICAL INFORMATICS The first use of HIT in a clinical setting can be traced back to 1952, when Dr. Arthur Rappoport reported his experiences with using the McBee Manual Punch Card in a pathology laboratory setting (Porth and Lu¨bke, 1996). This was followed by the emergence of hospital information systems in the 1960s, with the Latter Day Saints Hospital in Utah (USA, now Intermountain) being the first to implement this in 1967. Others, including The COmputer STored Ambulatory Record and the Regenstrief Medical Record System, followed. The Health Evaluation through Logical Programming system had the ability to collect demographic and clinical data with decision support features. It is used to the present day, but may be replaced by Cerner in the near future (Gardner et al., 1999; Healthcare IT News, 2015). The development of clinical specialty systems for laboratory, radiology, pathology, radiotherapy, pharmacy, and primary care followed. Integration of these was not possible until the 1980s, when larger integrated medical information systems emerged, facilitated by the development of high-speed communication networks. In 1985, the first patient scheduling software called “Cadence” was launched by Epic Systems, followed by EpicCare in 1992. Subsequent developments in the 21st century have been characterized by growing clinical uses of technologies drawing on an ever increasing array of data sources (including patients and various care settings), mobile applications that allow patients and providers to gather and view data “on the go”, and the exploitation of digital data generated for reuse (Cresswell and Sheikh, 2016). An overview of key historical developments is provided in Box 1.1.
WHAT IS CLINICAL INFORMATICS? Clinical informatics represents a highly interdisciplinary field that involves “analyzing, designing, implementing, and evaluating information and communication systems that enhance individual and population health outcomes, improve patient care, and strengthen the clinician-patient relationship (Gardner et al., 2009).” As such, the field can be positioned at the intersection of clinical care, the health system, and information and communication technology. Its interdisciplinary nature is a core feature, and it includes
An Overview of Clinical Informatics Chapter | 1
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BOX 1.1 Key Historical Developments in Clinical Informatics1,2,3 1949: establishment of the German Society for Medical Documentation, Computer Science and Statistics (first professional informatics organization) 1950s: first time IT applied to the field of medicine in biomedical context 1952: Arthur Rappoport reported on using the McBee Manual Punch Card in a laboratory setting 1960s: first peer-reviewed informatics journals launched 1960s: emergence of hospital information systems that included digital patient information 1967: Latter Day Saints Hospital in Utah first hospital to use an Electronic Health Record (EHR) 1970s: first mention of English term “medical informatics” 1960s/70s: clinical specialty systems were developed for laboratory, radiology, pathology, radiotherapy, pharmacy, and primary care 1970: first Computerized Physician Order Entry system used in El Caminio Hospital, California 1980s: development of local, national, and worldwide high-speed communication networks 1980s: emergence of larger integrated medical information systems 1985: first patient scheduling software launched (Cadence) 1988: creation of the American Medical Informatics Association 1990s: emergence of the internet facilitating exchange of clinical data 1992: EpicCare launched 2000s: clinical users could use IT to view/order tests/medications from various databases 2010s: emergence of cloud networks and integration of data across multiple locations 1. Collen, M.F., 2015. A History of Medical Informatics in the United States. Ball, M.J. (Ed.), Springer, New York. 2. Hayes, G.M., Barnett, D.E. (Eds.), 2008. UK Health Computing: Recollections and Reflections. British Computer Society. 3. http://www.healthworkscollective.com/frankie-xavier/162251/long-road-digitization-historyhealthcare-informatics
clinical providers such as physicians, nurses, and pharmacists, but also medical librarians, information scientists, and communication specialists, to name just a few of the types of professionals involved. Related terms that are sometimes used interchangeably include health informatics, medical informatics, and eHealth. There are a range of published definitions with varying understandings of the field in the published literature—the exponential development of applications and increasing convergence of functionalities complicates navigating the area further (Boogerd et al., 2015; Oh et al., 2005). Various chapters of this book will delve deeper into specific applications and associated concepts. These begin with
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PART | I An Introduction to Clinical Informatics
overviews of inpatient systems, outpatient systems, and clinical documentation in Chapters 2, 3, and 4, respectively. Overall, existing applications can broadly be divided into three categories (Black et al., 2011): (1) systems informing and supporting decisions (see Chapter 10 on medication, laboratory, and radiology testing; and Chapter 12 on knowledge management and computerized guidelines); (2) storage and management of data (Chapter 11 on bioinformatics and precision medicine); and (3) delivery of expertise and care at a distance (see Chapter 13 on mobile health). There has been an increasing emergence of clinical informatics as a discipline (Greenes and Shortliffe, 1990). Associated activity includes the growing demand for organizational capacity in this area, but also the need for academic expertise to develop new educational trajectories and evaluate ongoing implementation, adoption, and optimization activities associated with the increasing range of technologies. Formal accreditation and certification of clinical informatics expertise are closely associated activities that are presently receiving attention (Fridsma, 2015; Gadd et al., 2016; Shortliffe et al., 2016), particularly in the United States (Middleton, 2014), but also many other countries.
EMPIRICAL EVIDENCE SURROUNDING EFFECTIVENESS OF CLINICAL INFORMATICS APPLICATIONS Clinical informatics applications have been shown to result in a number of benefits including, among others, the prevention of life-threatening allergic reactions to medication through systems facilitating clinical decision making (Bates et al., 1999; Kaushal et al., 2003), reductions in prescribing errors (Avery et al., 2012), and the ability to manage diabetes and high blood pressure remotely (Wild et al., 2016). However, it is often difficult to demonstrate the clinical effectiveness and cost-effectiveness of HIT (Black et al., 2011; Chaudhry et al., 2006; Jones et al., 2014), this at least in part reflecting the need for workflow reconfiguration and systems optimization (Cresswell et al., 2017). Chapter 8 will explore HIT and value in more detail, while Chapter 14 will examine the impact of technology on safety. There is increasing understanding of the potential risks associated with the introduction of new technologies in healthcare settings (Black et al., 2011; Buntin et al., 2011). The most commonly examined areas in this respect include privacy, confidentiality, and security (see Chapter 6: Privacy and Security); effects on work practices and interdisciplinary working; and difficulties surrounding accessibility of data (Ash et al., 2004; Barrows and Clayton, 1996; Harrison et al., 2007).
An Overview of Clinical Informatics Chapter | 1
7
The underlying reasons for this overall lack of evidence may partly be due to difficulties evaluating technologies, as these are often embedded in wider organizational change initiatives resulting in difficulties attributing effects (Campbell et al., 2000; Lilford et al., 2009). There is also a growing literature reexamining traditional evaluation paradigms advocating randomized controlled trials (RCTs) as the “gold standard”, toward a more flexible use of various evaluation methods including qualitative, mixed methods, human factors, and engineering-based approaches (Cresswell et al., 2017; Klasnja et al., 2011; Yusof et al., 2008). This is because RCTs tend to be costly and time-consuming (resulting in issues surrounding the applicability of results and major challenges associated with changing software/technology) and may not be appropriate for effectively evaluating the range of different rapidly changing existing applications. Conversely, it can be easy and less costly to conduct clinical trials using EHRs, and in industry for example it is now routine to employ “AB” testing, where when it is not clear which of two options is superior, both are tried for half a user base. Whichever is more effective at achieving the desired outcome (e.g., a digital purchase) is then used as the default.
CLINICAL INFORMATICS IN CONTEXT Having touched upon the challenges inherent in evaluating clinical informatics applications above, it is important to briefly discuss the importance of appreciating the range of contextual dimensions and various stakeholders that are involved in deploying and adopting technologies in healthcare (Fig. 1.1) (Cresswell and Sheikh, 2009). Contextual aspects may include technical features (e.g., usability), social contexts (e.g., changes in work practices), organizational strategies (relating to implementation and optimization), and wider sociopolitical dimensions (such as state and federal approaches to implementation and regulatory environments). Stakeholders within these various contexts include patients, academics, providers, vendors, developers, third-sector organizations, and policy makers. All of these have different interests that need to be aligned for initiatives to be successful. Chapter 9 will discuss organizational issues in more detail, while Chapter 7 will examine policy considerations and associated international strategies to promote clinical informatics implementations. Empirical evaluations that take this range of stakeholders and contextual factors into account are now widely advocated (Catwell and Sheikh, 2009). These should involve a longitudinal component to facilitate tracing developments over time, playing an active role in aligning interests through providing formative feedback to stakeholders in participating healthcare settings, and summative feedback to policy makers (Ammenwerth et al., 2003).
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PART | I An Introduction to Clinical Informatics
Widermacroenvironment
Organisational context
Social/human factors
Technical factors
FIGURE 1.1 Overview of contextual factors. Factors important for the successful implementation of EHRs identified in the literature. Adopted from Cresswell, K., Sheikh, A., 2009. The NHS Care Record Service (NHS CRS): recommendations from the literature on successful implementation and adoption. J. Innov. Health Inform. 17 (3), 153 160.
VISIONS SURROUNDING FUTURE DEVELOPMENTS IN CLINICAL INFORMATICS A central theme of this book will be examining state-of-the-art developments in clinical informatics, exploring progress toward realizing the vision of more effective, better quality, and safer care through the application of IT in healthcare settings. Key current developments in this respect are likely to include the creation of integrated health informatics infrastructures where data can be seamlessly shared between settings and applications (see Chapter 5 on interoperability, Chapter 16 on application programming interfaces, and Chapter 17 on cloud-based computing), and the creation of learning health systems that effectively draw on digital data collected in a variety of settings and by a variety of stakeholders to improve performance and services (see Chapter 15 on predictive analytics and population health, Chapter 18 on social/consumer informatics, and Chapter 19 on machine learning and AI). It is important to place this work in the context of a continuously evolving field, where innovations are created at a rapid pace. HIT has, if appropriately conceptualized, developed and implemented, the potential to continue to have major transformative effects on healthcare and can through so doing help deal with one of the most pressing healthcare challenges facing healthcare worldwide, namely to achieve more in terms of health gain for less and to support patient involvement/enablement/empowerment.
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CONCLUSIONS Significant international policy efforts and investments in clinical informatics are taking place to improve adoption and use of healthcare IT, with the underlying aim of improving healthcare safety, quality, and efficiency. We have outlined some of the past, present, and potential future developments in this domain and provided an overview of definitions and core issues in the field. Subsequent chapters in this book aim to share state-of-the-art developments with nonexpert clinical and academic audiences across the globe.
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PART | I An Introduction to Clinical Informatics
Gadd, C.S., Williamson, J.J., Steen, E.B., Fridsma, D.B., 2016. Creating advanced health informatics certification. J. Am. Med. Inform. Assoc. 23 (4), 848 850. Gardner, R.M., Pryor, T.A., Warner, H.R., 1999. The HELP hospital information system: update 1998. Int. J. Med. Inform. 54 (3), 169 182. Gardner, R.M., Overhage, J.M., Steen, E.B., Munger, B.S., Holmes, J.H., Williamson, J.J., et al., 2009. Core content for the subspecialty of clinical informatics. J. Am. Med. Inform. Assoc. 16 (2), 153 157. Greenes, R.A., Shortliffe, E.H., 1990. Medical informatics: an emerging academic discipline and institutional priority. JAMA 263 (8), 1114 1120. Harrison, M.I., Koppel, R., Bar-Lev, S., 2007. Unintended consequences of information technologies in health care—an interactive sociotechnical analysis. J. Am. Med. Inform. Assoc. 14 (5), 542 549. Healthcare IT News, 2015. Intermountain live with Cerner EHR. Available from: http://www. healthcareitnews.com/news/intermountain-live-cerner-ehr (last accessed 01.01.17). Jones, S.S., Rudin, R.S., Perry, T., Shekelle, P.G., 2014. Health information technology: an updated systematic review with a focus on meaningful use. Ann. Intern. Med. 160 (1), 48 54. Kaushal, R., Shojania, K.G., Bates, D.W., 2003. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch. Intern. Med. 163 (12), 1409 1416. Klasnja, P., Consolvo, S., Pratt, W., 2011. How to evaluate technologies for health behavior change in HCI research. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, pp. 3063 3072. Kurzweil, R., 2005. The Singularity Is Near: When Humans Transcend Biology. Penguin. Liang, P., Xu, Y., Zhang, X., Ding, C., Huang, R., Zhang, Z., et al., 2015. CRISPR/Cas9mediated gene editing in human tripronuclear zygotes. Protein Cell 6, 363 372. Lilford, R.J., Foster, J., Pringle, M., 2009. Evaluating eHealth: how to make evaluation more methodologically robust. PLoS Med. 6 (11), e1000186. Middleton, B., 2014. First diplomates board certified in the subspecialty of clinical informatics. J. Am. Med. Inform. Assoc. 21 (2), 384. Oh, H., Rizo, C., Enkin, M., Jadad, A., 2005. What is eHealth (3): a systematic review of published definitions. J. Med. Internet Res. 7 (1), e1. Porth, A.J., Lu¨bke, B., 1996. History of computer-assisted data processing in the medical laboratory. Eur. J. Clin. Chem. Clin. Biochem. 34 (3), 215 229. Shortliffe, E.H., Detmer, D.E., Munger, B.S., 2016. Clinical informatics: emergence of a new profession. In Clinical Informatics Study Guide. Springer International Publishing, pp. 3 21. Time Magazine, 2013. More people have cell phones than toilets, U.N. Study shows. ,http:// newsfeed.time.com/2013/03/25/more-people-have-cell-phones-than-toilets-u-n-study-shows/. (accessed 12.07.16). Travis, P., Bennett, S., Haines, A., Pang, T., Bhutta, Z., Hyder, A.A., et al., 2004. Overcoming health-systems constraints to achieve the Millennium Development Goals. Lancet 364 (9437), 900 906. Wild, S.H., Hanley, J., Lewis, S.C., McKnight, J.A., McCloughan, L.B., Padfield, P.L., et al., 2016. Supported telemonitoring and glycemic control in people with type 2 diabetes: the telescot diabetes pragmatic multicenter randomized controlled trial. PLoS Med. 13 (7), e1002098. Yusof, M.M., Papazafeiropoulou, A., Paul, R.J., Stergioulas, L.K., 2008. Investigating evaluation frameworks for health information systems. Int. J. Med. Inform. 77 (6), 377 385.
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RECOMMENDED FURTHER READING Avery, A.J., Rodgers, S., Cantrill, J.A., Armstrong, S., Cresswell, K., Eden, M., et al., 2012. A pharmacist-led information technology intervention for medication errors (PINCER): a multicentre, cluster randomised, controlled trial and cost-effectiveness analysis. Lancet 379 (9823), 1310 1319. Black, A.D., Car, J., Pagliari, C., Anandan, C., Cresswell, K., Bokun, T., et al., 2011. The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS Med. 8 (1), e1000387. Coiera, E., 2015. Guide to Health Informatics. CRC Press. Collen, M.F., 2015. A History of Medical Informatics in the United States. Springer, New York. Cresswell, K., Blandford, A., Sheikh, A., Reconsidering paradigms for the evaluation of health information technology. Submitted to JAMIA. Hovenga, E.J., 2010. Health Informatics: An Overview. IOS Press.