Difference between revisions of "Journal:Informatics metrics and measures for a smart public health systems approach: Information science perspective"
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==Factors shaping smart agents and organizations== | ==Factors shaping smart agents and organizations== | ||
The underlying objective of any agent or actor within a given public health system is to maximize the use of data, [[information]], and knowledge as strategic resources. An informatics-biased view of a public health system focuses on the sum of data, information, knowledge systems, people, practices, policies, and cultural factors that operates to support some predefined intelligence strategy, organizational mission, or other event.<ref name="DavenportInfo97">{{cite book |title=Information Ecology: Mastering the Information and Knowledge Environment |author=Davenport, T.H.; Prusak, L. |publisher=Oxford University Press |edition=1st |year=1997 |isbn=9780195111682}}</ref> In these terms, the public health system can be understood as a functional knowledge culture. We have also used related terms such as knowledge environments, information or knowledge ecosystems, and information or knowledge ecologies to represent this idea. Here, we use knowledge culture and knowledge environment interchangeably. We argue that any defined system boundary that contains the formal or informal governance of critical strategic and shared knowledge resources can be called a knowledge environment. The primary purpose of any knowledge environment is best understood in terms of the essential need to leverage data, information, and knowledge in managing individual or collective uncertainty.<ref name="DiezRouxComp11">{{cite journal |title=Complex systems thinking and current impasses in health disparities research |journal=American Journal of Public Health |author=Diez Roux, A.V. |volume=101 |issue=9 |pages=1627–34 |year=2011 |doi=10.2105/AJPH.2011.300149 |pmid=21778505 |pmc=PMC3154209}}</ref><ref name="LichACall13">{{cite journal |title=A call to address complexity in prevention science research |journal=Prevention Science |author=Lich, K.H.; Ginexi, E.M.; Osgood, N.D.; Mabry, P.L. |volume=14 |issue=3 |pages=279-89 |year=2013 |doi=10.1007/s11121-012-0285-2 |pmid=22983746}}</ref><ref name="ArndtComm00">{{cite journal |title=Commentary: the potential of chaos theory and complexity theory for health services management |journal=Health Care Management Review |author=Arndt, M.; Bigelow, B. |volume=25 |issue=1 |pages=35–8 |year=2000 |pmid=10710726}}</ref> | The underlying objective of any agent or actor within a given public health system is to maximize the use of data, [[information]], and knowledge as strategic resources. An informatics-biased view of a public health system focuses on the sum of data, information, knowledge systems, people, practices, policies, and cultural factors that operates to support some predefined intelligence strategy, organizational mission, or other event.<ref name="DavenportInfo97">{{cite book |title=Information Ecology: Mastering the Information and Knowledge Environment |author=Davenport, T.H.; Prusak, L. |publisher=Oxford University Press |edition=1st |year=1997 |isbn=9780195111682}}</ref> In these terms, the public health system can be understood as a functional knowledge culture. We have also used related terms such as knowledge environments, information or knowledge ecosystems, and information or knowledge ecologies to represent this idea. Here, we use knowledge culture and knowledge environment interchangeably. We argue that any defined system boundary that contains the formal or informal governance of critical strategic and shared knowledge resources can be called a knowledge environment. The primary purpose of any knowledge environment is best understood in terms of the essential need to leverage data, information, and knowledge in managing individual or collective uncertainty.<ref name="DiezRouxComp11">{{cite journal |title=Complex systems thinking and current impasses in health disparities research |journal=American Journal of Public Health |author=Diez Roux, A.V. |volume=101 |issue=9 |pages=1627–34 |year=2011 |doi=10.2105/AJPH.2011.300149 |pmid=21778505 |pmc=PMC3154209}}</ref><ref name="LichACall13">{{cite journal |title=A call to address complexity in prevention science research |journal=Prevention Science |author=Lich, K.H.; Ginexi, E.M.; Osgood, N.D.; Mabry, P.L. |volume=14 |issue=3 |pages=279-89 |year=2013 |doi=10.1007/s11121-012-0285-2 |pmid=22983746}}</ref><ref name="ArndtComm00">{{cite journal |title=Commentary: the potential of chaos theory and complexity theory for health services management |journal=Health Care Management Review |author=Arndt, M.; Bigelow, B. |volume=25 |issue=1 |pages=35–8 |year=2000 |pmid=10710726}}</ref> | ||
The way in which we engage in information and knowledge seeking, organize ourselves into collectives of varying unit configurations (e.g., workgroups, project teams, taskforces, departments, divisions, networks of organizational coalitions, and consortiums), and/or apply the use of tools or technology indicates the basic need to manage any and all forms of uncertainty.<ref name="KlingOrg93">{{cite journal |title=Organizational analysis in computer science |journal=The Information Society |author=Kling, R. |volume=9 |issue=2 |pages=71–87 |year=1993 |doi=10.1080/01972243.1993.9960134}}</ref> We organize ourselves in response to external and internal drivers/stressors and increasing environmental complexity as a means of reducing or removing any impediments toward fast, reliable, and pertinent data, information, and knowledge resources.<ref name="BanduraSocial76">{{cite book |title=Social Learning Theory |author=Bandura, A. |publisher=Prentice-Hall |edition=1st |year=1976 |isbn=9780138167448}}</ref> This imperative to organize for the sake of becoming smarter is best observed in our introduction of three primary drivers that we argue are interdependent in any knowledge environment. By describing these factors as interdependent, we are stating that as one type of driver category increases or decreases by some set of circumstances or events, corresponding changes can occur in one or both of the other areas. These areas include organizational complexity, problem/issue complexity, and situational awareness (see Figure 2). Essentially, each of these three primary driver categories shapes our overall data, information, and knowledge strategy within any knowledge environment. The primary objective of an informatician in designing and maintaining a smart public health knowledge environment is then to understand the basic predictors of change in any or all of these categories, as well as to account for the corresponding mediation/moderation factors that can shape continued data, information, and knowledge maximization for agents within any public health knowledge environment. | |||
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===Organizational complexity factors shaping public health knowledge environments=== | |||
We use a variety of organizational structures to facilitate interaction, communication, and knowledge representation in our quest to manage changes in our environment. Generally, the levels of organization may vary from a micro- to macrocontinuum that starts with organizational agents/individuals, components/sub-units, a single entity/facility, and a multi-unit of systems/collaborations/coalitions/networks/taskforces/consortiums.<ref name="KlingUnder05">{{cite book |title=Understanding and Communicating Social Informatics: A Framework for Studying and Teaching the Human Contexts of Information and Communication Technologies |author=Kling, R.; Rosenbaum, H.; Sawyer, S. |publisher=Information Today, Inc |year=2005 |pages=241 |isbn=9781573872287}}</ref><ref name="LorenziOrg95">{{cite book |title=Organizational Aspects of Health Informatics |author=Lorenzi, N.M.; Riley, R.T. |publisher=Springer New York |year=1995 |isbn=9781475741841}}</ref> Typically, the level of complexity inherent in the public health challenge or crisis event determines the corresponding level of organizational complexity required in the response.<ref name="LichACall13" /><ref name="ThiétartChaos95">{{cite journal |title=Chaos Theory and Organization |journal=Organization Science |author=Thiétart, R.A.; Forgues, B. |volume=6 |issue=1 |pages=19–31 |year=1995 |doi=10.1287/orsc.6.1.19}}</ref> Challenges or crisis events that are short-term or relatively minor may only require minimal, ad hoc, or temporary organizational responses. Within the modern healthcare environment, these can represent informal partnerships or formal structures appearing as short-term project teams or workgroups. More involved and long-term problems may require increasing levels of complexity within the organizational response. These long-term or complex organizational responses may be represented in the form of permanent departments or divisions within an organization, or they may even extend beyond organizational boundaries to include coalitions, collaborations, taskforces, and interagency network arrangements. | |||
==References== | ==References== | ||
Revision as of 22:59, 10 January 2017
| Full article title | Informatics metrics and measures for a smart public health systems approach: Information science perspective |
|---|---|
| Journal | Computational and Mathematical Methods in Medicine |
| Author(s) | Carney, Timothy J.; Shea, Christopher M. |
| Author affiliation(s) | Gillings School of Global Public Health at University of North Carolina - Chapel Hill |
| Editors | Rodríguez-González, Alejandro |
| Year published | 2017 |
| Volume and issue | 2017 |
| Page(s) | 1452415 |
| DOI | 10.1155/2017/1452415 |
| ISSN | 1748-6718 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.hindawi.com/journals/cmmm/2017/1452415/ |
| Download | http://downloads.hindawi.com/journals/cmmm/2017/1452415.pdf (PDF) |
Abstract
Public health informatics is an evolving domain in which practices constantly change to meet the demands of a highly complex public health and healthcare delivery system. Given the emergence of various concepts, such as learning health systems, smart health systems, and adaptive complex health systems, health informatics professionals would benefit from a common set of measures and capabilities to inform our modeling, measuring, and managing of health system “smartness.” Here, we introduce the concepts of organizational complexity, problem/issue complexity, and situational awareness as three codependent drivers of smart public health systems characteristics. We also propose seven smart public health systems measures and capabilities that are important in a public health informatics professional’s toolkit.
Introduction
Public health informatics is an evolving domain in which practices constantly change to meet the demands of a highly complex public health and healthcare delivery system. The typical definition for a variety of domains of informatics (e.g., public health, population health, nursing, clinical, medical, health, consumer, and biomedical) centers on the "application of information science and information technology to [a specific domain of] practice, research, and training."[1][2] This definition of informatics relies on a technical view of the health system. A technical view of informatics largely identifies more tangible products such as databases, decision-support tools, information systems, web portals, and mobile devices as the primary means of addressing complex health issues, improving care, and reducing health disparities.
Public health informatics systems expressed as a function of intelligence can be understood in terms of two codependent pathways of (1) generating health information technology (HIT) policies that ensure our ability to govern intelligence as a byproduct and (2) allowing innovations in HIT to shape and inform public health systems policy and practice to ensure that we govern intelligently. In the former case, public health informatics professionals endeavor to generate HIT policy to guide national, state, and local information architecture, information infrastructure, and information integration efforts that ultimately guide how public health meets the needs of stakeholder/agents such as patients/families/health consumers, communities, providers/healthcare organizations, researchers, policymakers, and disease-centric communities of practice through the meaningful supply of intelligence. Such intelligence can inform stakeholder understanding about the burden of disease, spread of an outbreak, health alerts and food recalls, disease clusters, community needs assessments, and health risk assessments. In the latter case, public health informatics professionals seek to find innovative ways to leverage HIT to improve the way we govern by seeking ways to streamline processes that positively impact cost, quality, safety, and overall health outcomes. Figure 1 highlights these relationships in the context of public health practice domains.
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Although useful for fostering greater levels of adoption and use of technical measures, this technical view of public health informatics (1) does not highlight the changing knowledge needs of these system agents over time, (2) fails to capture the full array of interaction among agents in a dynamic environment, and (3) cannot maintain pace in adapting to an ever-increasing complex environment. In other words, the purely technical approach does not effectively highlight the full spectrum of knowledge, communication, and learning that is needed to keep all types of health system stakeholders — including individuals, organizations, or collections of individual and organizational networks (e.g., coalitions, collaborations, consortiums, and taskforces )— informed and able to respond to environmental changes at all stages of the healthcare continuum.
In this era of informatics where the emphasis is on less tangible cognitive capacities (e.g., learning health systems, intelligent and smart systems, and complex adaptive systems), a new public health informatics analytics approach may be required that is less information technology-driven and more knowledge-driven and defines new ways of demonstrating the added value of informatics in shaping health systems performance.[3] Specifically, stakeholders (hereafter referred to as individual- or organizational-level agents) need concise, accurate, and objective analytic measurements of abstract concepts, such as empowerment, which previously has been described as a function of knowledge for the purposes of achieving a quantifiable metric for computational analysis of performance.
Such a view of public health informatics may focus on abstract constructs like actionable intelligence as the primary informatics-centric outcome.[3] Such a strategy should yield objective operational measures and capabilities designed to ensure that individual agents, organizations, and networks have sufficient knowledge to mount an intelligent response to solve complex public health problems. In other words, the strategy should support development and maintenance of smart health systems, that is, a system that "incorporates functions of sensing, actuation, and control in order to describe and analyze a situation, and make decisions based on the available data in a predictive or adaptive manner, thereby performing smart actions. In most cases the ‘smartness’ of the system can be attributed to autonomous operation based on closed loop control, energy efficiency, and networking capabilities."[4]
The purpose of this paper is to propose a set of measures for tracking the development and sustainability of smart public health systems. Specifically, we introduce the concepts of organizational complexity, problem/issue complexity, and situational awareness as three codependent drivers of smart health systems. We then describe seven smart health systems measures. This discussion is important for public health informatics professionals responsible for specifying metrics, overseeing information systems housing data for the metrics, and evaluating the performance of smart public health systems.
Factors shaping smart agents and organizations
The underlying objective of any agent or actor within a given public health system is to maximize the use of data, information, and knowledge as strategic resources. An informatics-biased view of a public health system focuses on the sum of data, information, knowledge systems, people, practices, policies, and cultural factors that operates to support some predefined intelligence strategy, organizational mission, or other event.[5] In these terms, the public health system can be understood as a functional knowledge culture. We have also used related terms such as knowledge environments, information or knowledge ecosystems, and information or knowledge ecologies to represent this idea. Here, we use knowledge culture and knowledge environment interchangeably. We argue that any defined system boundary that contains the formal or informal governance of critical strategic and shared knowledge resources can be called a knowledge environment. The primary purpose of any knowledge environment is best understood in terms of the essential need to leverage data, information, and knowledge in managing individual or collective uncertainty.[6][7][8]
The way in which we engage in information and knowledge seeking, organize ourselves into collectives of varying unit configurations (e.g., workgroups, project teams, taskforces, departments, divisions, networks of organizational coalitions, and consortiums), and/or apply the use of tools or technology indicates the basic need to manage any and all forms of uncertainty.[9] We organize ourselves in response to external and internal drivers/stressors and increasing environmental complexity as a means of reducing or removing any impediments toward fast, reliable, and pertinent data, information, and knowledge resources.[10] This imperative to organize for the sake of becoming smarter is best observed in our introduction of three primary drivers that we argue are interdependent in any knowledge environment. By describing these factors as interdependent, we are stating that as one type of driver category increases or decreases by some set of circumstances or events, corresponding changes can occur in one or both of the other areas. These areas include organizational complexity, problem/issue complexity, and situational awareness (see Figure 2). Essentially, each of these three primary driver categories shapes our overall data, information, and knowledge strategy within any knowledge environment. The primary objective of an informatician in designing and maintaining a smart public health knowledge environment is then to understand the basic predictors of change in any or all of these categories, as well as to account for the corresponding mediation/moderation factors that can shape continued data, information, and knowledge maximization for agents within any public health knowledge environment.
|
Organizational complexity factors shaping public health knowledge environments
We use a variety of organizational structures to facilitate interaction, communication, and knowledge representation in our quest to manage changes in our environment. Generally, the levels of organization may vary from a micro- to macrocontinuum that starts with organizational agents/individuals, components/sub-units, a single entity/facility, and a multi-unit of systems/collaborations/coalitions/networks/taskforces/consortiums.[11][12] Typically, the level of complexity inherent in the public health challenge or crisis event determines the corresponding level of organizational complexity required in the response.[7][13] Challenges or crisis events that are short-term or relatively minor may only require minimal, ad hoc, or temporary organizational responses. Within the modern healthcare environment, these can represent informal partnerships or formal structures appearing as short-term project teams or workgroups. More involved and long-term problems may require increasing levels of complexity within the organizational response. These long-term or complex organizational responses may be represented in the form of permanent departments or divisions within an organization, or they may even extend beyond organizational boundaries to include coalitions, collaborations, taskforces, and interagency network arrangements.
References
- ↑ Kukafka, A.; Yasnoff, W.A. (2007). "Public health informatics". Journal of Biomedical Informatics 40 (4): 365–369. doi:10.1016/j.jbi.2007.07.005. PMID 17656158.
- ↑ Yasnoff, W.A.; O'Carroll, P.W.; Koo, D. et al. (2000). "Public health informatics: Improving and transforming public health in the information age". Journal of Public Health Management and Practice 6 (6): 67–75. PMID 18019962.
- ↑ 3.0 3.1 Hsu, C.E.; Chambers, W.C.; Herbold, J.R. et al. (2010). "Towards shared situational awareness and actionable knowledge — an enhanced, human-centered paradigm for public health information system design". Journal of Homeland Security and Emergency Management 7 (1). doi:10.2202/1547-7355.1727.
- ↑ March, J.G.; Simon, H.A. (1993). Organizations (2nd ed.). Wiley-Blackwell. ISBN 9780631186311.
- ↑ Davenport, T.H.; Prusak, L. (1997). Information Ecology: Mastering the Information and Knowledge Environment (1st ed.). Oxford University Press. ISBN 9780195111682.
- ↑ Diez Roux, A.V. (2011). "Complex systems thinking and current impasses in health disparities research". American Journal of Public Health 101 (9): 1627–34. doi:10.2105/AJPH.2011.300149. PMC PMC3154209. PMID 21778505. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3154209.
- ↑ 7.0 7.1 Lich, K.H.; Ginexi, E.M.; Osgood, N.D.; Mabry, P.L. (2013). "A call to address complexity in prevention science research". Prevention Science 14 (3): 279-89. doi:10.1007/s11121-012-0285-2. PMID 22983746.
- ↑ Arndt, M.; Bigelow, B. (2000). "Commentary: the potential of chaos theory and complexity theory for health services management". Health Care Management Review 25 (1): 35–8. PMID 10710726.
- ↑ Kling, R. (1993). "Organizational analysis in computer science". The Information Society 9 (2): 71–87. doi:10.1080/01972243.1993.9960134.
- ↑ Bandura, A. (1976). Social Learning Theory (1st ed.). Prentice-Hall. ISBN 9780138167448.
- ↑ Kling, R.; Rosenbaum, H.; Sawyer, S. (2005). Understanding and Communicating Social Informatics: A Framework for Studying and Teaching the Human Contexts of Information and Communication Technologies. Information Today, Inc. pp. 241. ISBN 9781573872287.
- ↑ Lorenzi, N.M.; Riley, R.T. (1995). Organizational Aspects of Health Informatics. Springer New York. ISBN 9781475741841.
- ↑ Thiétart, R.A.; Forgues, B. (1995). "Chaos Theory and Organization". Organization Science 6 (1): 19–31. doi:10.1287/orsc.6.1.19.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.
