Public Health Informatics: What is it? A Definition: Public health informatics is the systematic application of information and computer science and technology to. Public health informatics: improving and transforming public health in the information It requires the application of knowledge tion of information technology. tion level, which includes informatics applied to public health and to the entire health care system (health information infrastructure). Population-level informatics.
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Geraldine S. Johnson, Guthrie S. Birkhead, Rachel Block, Shannon Kelley, James Coates, Robert J. Campbell et al. Pages PDF · State Public Health. Request PDF on ResearchGate | Public Health Informatics and Information Systems | This totally revised edition of a classic textbook covers the context and . Request PDF on ResearchGate | On Jan 1, , Patrick W. O'Carroll and others published Public Health Informatics and Information Systems.
Yasnoff, Patrick W. Linkins, and Edwin M. Kilbourne Development of effective public health Introduction information systems requires Effective public health practice requires timely, understanding public health accurate, and authoritative information from a wide informatics PHI , the systematic variety of sources. PHI is distinguished from oped. William Yasnoff, Mailstop K, context. Telephone: ; Fax: arises from dramatic improvements in ; e-mail: WYasnoff cdc. The authors appreciate the helpful comments and suggestions information technology, new pressures from Edward L.
Bureau of Labor Statistics project sustained growth in e-health jobs over the next decade as information systems continue to be implemented, used, and inter-connected [ 12 , 13 ].
Two prominent disciplines have emerged from the mix of e-health workers now employed in health organizations — health informatics HI and health information management HIM. While these disciplines have distinct ancestors, over time their definitions and scopes of practice have evolved.
Definitions and Evolution of Health Information Professions In evolutionary biology, convergent evolution is the process whereby organisms that are not closely related, independently evolve similar traits as a result of having to adapt to similar environments or ecological niches [ 15 ]. The origins of HIM and HI differ; in this section we define each profession and review their beginnings. ARLNA grew out of efforts to standardize medical education and hospital practice to improve and evaluate the quality of patient care; the ACS recognized the need to establish a knowledgeable workforce to implement and manage standardized methods of collecting, storing, and retrieving patient data and records.
The first annual meeting of ARLNA was held the following year, and initial steps were taken to institute a quarterly journal now two journals, the Journal of AHIMA and Perspectives in Health Information Management , form a committee to develop a standardized course of study for medical record librarians, and set standards for registration and certification of its members.
Current research and practice in HIM address the nature, structure, and translation of data into usable forms of information for advancing the health and health care of both individuals and populations. Health informatics HI Informatics is defined as the science of information, studying the representation, processing and communication of information by computers, humans, and organizations [ 19 ].
BMI has been broadly defined as an interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, driven by efforts to improve human health [ 21 ]. Clinical informatics has been defined as the application and use of health information and technologies in the provision of health services, most often in the context of individual clinical care [ 23 ].
Public health informatics seeks to apply and use health information and technologies to improve population health, including the surveillance and prevention of disease as well as general health promotion [ 24 ]. In this paper, we define HI as the discipline concerned with the study and pursuit of effective uses of information, often aided by the use of technology, to improve health care delivery as well as individual and population health outcomes [ 20 ].
In our review of the research into healthcare information systems as summarized above, we found no discussion of how cognitive processing could be disrupted by clumsy technological functions and no guidance in relation to dealing with those issues in design.
For example, Karsh et al. Karsh et al. Greenhalgh et al. We believe that the appropriate design methods are largely unknown and that these pleas to attend to cognitive issues will either be ignored or will turn designers towards a restricted view of User-Centered Design.
We use this paper as an opportunity to introduce one cognitive design framework, Decision-Centered Design, that can address the concerns expressed by Karsh et al. It is one of several frameworks available within Cognitive Engineering [ 33 ]. Cognitive engineers focus on the design of technological support systems such as interfaces, information-entry systems and communication systems, and on human resource issues such as team design, organizational design, staffing, selection and training.
In contrast to the technology-centric design assumptions of stable, routine and knowable work processes, cognitive engineers assume that healthcare work is demanding, fluid and unpredictable, being distributed and shared across a system of functionally interdependent actors and artefacts.
Within the systems in which they are embedded, information technology artefacts are therefore ideally designed with respect to functional implications at the system level.
The design goal for such an environment is to establish a robust system in which the human capability to perform cognitive work is optimized. We use a case-study approach to emphasize cognitive issues as we develop our argument. Our first two case studies demonstrate the problems that accrue from taking a rational, exclusively rule-based approach to information system design while ignoring the cognitive subleties of healthcare work.
Our second two case studies demonstrate the power of supporting cognition with a mix of skill-, rule-, and knowledge-based design strategies. Because we are offering an argument rather than a review or a survey, we deliberately selected case studies that illustrate the significance of cognition in healthcare and how that cognition might be supported with innovative design solutions that are not exclusively rule-based.
The cognitive challenge Decision-Centered Design evolved in response to the neglect by technology-centric design disciplines of the cognitive processes critical to the effective execution of human cognitive work.
Here we illustrate the problems that emerge from designing cognitive support systems based on a technology-centric view of work practice by reference to two research papers that have assessed the efficacy of technological developments in healthcare. Case study: patient evacuation An automated scheduling system was developed for the U.
For large-scale problems, the new system produced better schedules with less effort. However, the scheduling problem was dynamic in that staff could be confronted with an unscheduled evacuation request such as immediate transport of a seriously ill patient who required emergency medical treatment at a specified facility [ 35 ].
With diversion of an aircraft and crew to fill this urgent requirement, evacuation schedules for other patients could be disrupted so that the schedule would have to be adjusted. Although common, schedule adjustment in response to dynamically unfolding needs had always been a challenge.
However, in the process of manual scheduling, staff had implicitly developed an appreciation of potential resource options and conflicts, which they could use to adjust a schedule as consistent with new demands.
In macro-cognitive terms, they had developed a useful level of situation awareness relating to available resources and potential conflicts via an implicit process of sensemaking. However, constraints imposed by the new automated scheduling system blocked staff from building that appreciation of potential options and conflicts so that staff were then ill-prepared to adjust schedules when needed.
Resource scheduling is a ubiquitous challenge in modern hospitals. For example, it can often be difficult in a large hospital to satisfy demands for in intensive care beds [ 36 ].
This is a problem that seems ideal for computerized support, but there is an ever-present concern that those who develop such a system will ignore many of the subtle cognitive processes that are critical to a satisfactory outcome. Case study: anesthesiology A new, highly integrated, microprocessor-based physiological monitoring system for cardiac anesthesia was introduced into a cardiothoracic surgery unit to replace the functions of four single-sensor devices [ 37 ].
By centralizing the sensor data and the patient-monitoring functions in a single computer-based system, designers provided anesthesiologists with options for reorganizing windows on the screen and for viewing different representations of the same information.
The most obvious interface difference from the previous assembly of discrete devices was the multi-layer menu structure that was activated via a touch screen. Cardiac surgical patients are susceptible to rapid and profound hemodynamic changes, many of which can be life-threatening.
This became an issue when the surgeon lifted the heart to feel the coronary blood vessels, an action that could cause blood pressure to fall rapidly. During such an event, the surgeon depends on the anesthesiologist to announce the correct blood pressures.
These could be inferred readily from the default waveform representation of the old system, but the default numeric configuration of the new system encouraged a direct reading of numbers that changed too slowly to track blood pressure accurately. Although anesthesiologists learned to compensate by extrapolating the digital values, inexperienced residents sometimes failed to do so, which resulted in complaints from surgeons.
After considerable thought and experimentation, anesthesiologists developed a fixed-scale analog window that showed all blood pressures.
Although it served the need when visible, this new window configuration had to be set up with a complex series of steps at the beginning of each case and, even then, was not entirely stable. An automatic window-management function could hide this new blood-pressures window as the anesthesiologist performed other tasks. That problem was largely resolved when an anesthesiologist discovered that the preferred screen configuration could be maintained by reserving screen space with modules that contained no useful information.
Once this solution was known, the necessary window management could be completed during the low-workload period of system initialization. Nevertheless, window management continued to be a problem when the anesthesiologist needed to measure cardiac output.
With the new computer system, cardiac output was viewed on a special window brought to the screen by activating a screen label, but activation of this window had the side effect of removing the blood-pressures window, thereby degrading practitioner ability to detect rapid changes in blood pressure.
This did not occur in the old system because the discrete devices displayed the data in parallel. Problematically, the time that measurement of cardiac output was most frequent coincided with the time that rapid changes in blood pressure were of most concern. One consequence of providing multiple functions in a single device is that the control of these functions becomes more complicated. For example, a blood pressure channel was reset on the old system by pressing a physical switch on the front of a panel.
With the new system, that channel reset required a series of screen activations. The most frequently used menu function of the computer system was measurement of cardiac output, a process requiring at least three menu activations.
For the old, discrete device, that same function was activated with a single press of a mechanical button. Furthermore, errors were common.