Journal:Informatics metrics and measures for a smart public health systems approach: Information science perspective

From LIMSWiki
Revision as of 21:28, 11 January 2017 by Shawndouglas (talk | contribs) (Saving and adding more.)
Jump to navigationJump to search
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.


Fig1 Carney CompMathMethMed2017.png

Figure 1. Public health informatics systems intelligence perspectives

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.


Fig2 Carney CompMathMethMed2017.png

Figure 2. Knowledge environment factors of influence

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.

One common public health system problem-solving strategy used throughout the US and worldwide involves formulating networks of individuals and organizations to coordinate global-, national-, state-, regional-, county-, city-, or even community-level responses to health threats to individuals or populations. Such networks (e.g., coalitions, collaborations, consortiums, and taskforces) present opportunities to define common goals, shape strategy, achieve economies-of-scale through the sharing of resources and facilitate the centralized monitoring and measuring of progress toward stated objectives. However, one challenge for the public health informatics professional involves ensuring that the data, information, and knowledge needs of networks of stakeholders — ranging from patient advocates, health organizations, providers, community groups, public health departments, policy makers, and researchers — are all met with efficiency and effectiveness. The issues surrounding timely intelligence were on full display during the recent Ebola virus and Zika virus outbreaks.

Currently, there are no consistent measures or metrics to evaluate the efficiency and effectiveness of the ability of “smart” health networks—of any size or configuration—to leverage data, information, and knowledge to produce actionable intelligence from their efforts.[3][14] In other words, there is no quantifiable set of standardized measures or standard operational definitions of what a smart or learning health network is now or what it should be in the future.[15] Within any public health knowledge environment, a wide variety of network structures can be assumed. The organization is viewed as a dynamic, complex, and adaptive entity whose size, structure, and other organizational determinants must be constantly evaluated to promote its ability to respond to internal and external challenges, threats, and opportunities that will impact individuals and/or the collective leveraging of actionable intelligence to ensure success in health system management.[16][17][18]

Problem/Issue complexity factors shaping knowledge environments

An analogy for problem/issue identification and response within any public health knowledge environment is the human immune response in which the human immune system assesses threats on a constant basis and determines if a foreign agent is a “friend” or “foe.” Once identified in a healthy immune system, the proper immune response is triggered. For a friend response, facilitation/proliferation strategies ensue, and, for a foe response, elimination/mitigation strategies ensue. Two critical components in the overall immune response system are the ability to retain a memory of this encounter and to demonstrate system learning to prepare for future encounters of a relatively similar nature.

The same sort of dynamic occurs within a public health system network among its various organizational components, actors/agents, and events. Once a phenomenon (i.e., circumstance/event/activity/occurrence) is identified as a potential problem, either threatening or nonthreatening, system or collective memory is vetted for familiarity.[18] If sufficient memory of the phenomenon or something similar is found, the ideal response algorithm(s) (set of instructions) is/are identified, outlining the appropriate response mechanism. If no memory exists, a response must be determined on an ad hoc basis. Clinical or public health events/activities that allow favorable health outcomes (e.g., diffusion of best practices, strategic summits, introduction of new technology, disease screening and awareness campaigns, and new funding announcements) may be considered targets for facilitation/proliferation, whereas unfavorable events/activities (e.g., disease outbreaks, health or food recalls, medical errors, deviations from guideline concordant care, risk behaviors linked to disease spread, budget shortfalls, and staff layoffs) may be targets for elimination/mitigation.

In either case, sufficient memory must be generated of the response algorithms (process/workflows, policies/procedures) that contributed to the event(s), pathways toward emergence, and/or remediation strategy to eliminate the threat. Learning in this context presents the ability to circumnavigate potentially harmful events that have the potential for recurrence or the ability to repeat/reinforce positive events that are beneficial.[19] Hence, the ability to extract actionable intelligence from stored memory is essential to overall public health system performance and an effective knowledge environment.[3] Two factors that shape this dynamic of event, memory evaluation, and learning within a knowledge environment are familiarity and preparedness, borrowed from the field of emergency preparedness.[20]

Within any knowledge environment, issues/problem complexity and relative familiarity (stored memory) largely shape the level of “shock” or environmental stress to the public health system, which creates what Burton termed an organizational design misfit.[16] In the presence of an organizational design misfit, the goal is to seek to restore some measure of equilibrium.[17] The level of shock brought by the introduction of a problem/issue into any public health knowledge environment and its corresponding impact on the public health system can be thought of in terms of two factors: (1) the degree to which the event was expected to occur and (2) the degree to which the environment was prepared for its occurrence. Figure 3 highlights the relationships of these two factors, where the green represents a highly desirable state of system and organizational readiness (operationally defined here as the agents’ — within the public health knowledge environment — ability to process the event and determine an appropriate response), yellow represents less desirable states of organizational readiness, and red represents the least desirable state of organizational readiness and the highest level of vulnerability from both internal and external threats.


Fig3 Carney CompMathMethMed2017.png

Figure 3. Problem/issue complexity factors

Although most public health systems are prepared to deal with any event, some noticeable changes can occur in the face of uncovered vulnerabilities introduced by shock events. Such adjustments on the organizational side may present as unexpected leadership shifts, sudden changes in organizational command structures, abrupt shifts in policy and procedures, new strata of research funding to investigate and solve problems, or the addition or elimination of staff and key personnel.[16] On the public health knowledge environment side, such adjustments can take the form of wide-scale data integration or health information exchange efforts, the formation of new database solutions, the demand for new technology to monitor and track the problem, surveillance protocols, information systems, knowledge portals, decision-support systems, and changes in information resource-management protocols.[12] The level of complexity in both the problem/issue and the capability of the public health knowledge environment to process the event and mount an appropriate response heavily shapes the level of organization (or in some cases reorganization) required to mediate the threat or exploit the opportunity. Additionally, these changes—and more importantly the rate of changes in the organization in particular and the public health knowledge environment in general—may serve as proxy indicators for overall public health knowledge environment maturity in managing uncertainty. In other words, a health system or public health agency that has undergone frequent leadership changes, high staff turnover, frequent redrafting of strategic plans, and reorganizations in a relatively short span of time serves as a strong indicator of the lack of overall public health knowledge environment maturity.[12] Such a public health knowledge environment characteristically remains in a loop moving from crisis-to-solution to a new or reemerging crisis-to-solution. In contrast, a mature public health knowledge environment will seek to identify and understand the patterns of organizational complexity and problem/issue complexity emergence and response. Properly stored, organized, and readily accessible system memory can greatly aid in achieving a more mature public health knowledge environment.

Situational awareness factors shaping information environments

Previously, we stated that organizational complexity is shaped by external or internal factors in a given public health knowledge environment, requiring different levels of formal or informal organizational structures to manage their environmental challenges. We also mentioned that the level of complexity inherent in problems/issues and the corresponding system memory and preparedness will shape system-level responses to control and mitigate any perceived threats. Here, we formally define the term "situational awareness" (SA) as “the ability to make sense of an ambiguous situation. It is the process of creating [situational awareness] and understanding to support decision-making under uncertainty — an effort to understand connections among people, places, and events in order to anticipate their trajectories and act effectively.”[21] Endsley elaborated on the definition for SA, stating that it's comprised of three sub-domains that shape individual understanding of some phenomena. These include (1) situation perception (defining the current public health condition), (2) situation comprehension (defining the relative public health threat or opportunity), and (3) situation projection (forecasting the public health outcomes of hypothesized trajectories).[22]

References

  1. 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. 
  2. 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. 3.0 3.1 3.2 3.3 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. 
  4. March, J.G.; Simon, H.A. (1993). Organizations (2nd ed.). Wiley-Blackwell. ISBN 9780631186311. 
  5. Davenport, T.H.; Prusak, L. (1997). Information Ecology: Mastering the Information and Knowledge Environment (1st ed.). Oxford University Press. ISBN 9780195111682. 
  6. 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. 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. 
  8. 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. 
  9. Kling, R. (1993). "Organizational analysis in computer science". The Information Society 9 (2): 71–87. doi:10.1080/01972243.1993.9960134. 
  10. Bandura, A. (1976). Social Learning Theory (1st ed.). Prentice-Hall. ISBN 9780138167448. 
  11. 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. 
  12. 12.0 12.1 12.2 Lorenzi, N.M.; Riley, R.T. (1995). Organizational Aspects of Health Informatics. Springer New York. ISBN 9781475741841. 
  13. Thiétart, R.A.; Forgues, B. (1995). "Chaos Theory and Organization". Organization Science 6 (1): 19–31. doi:10.1287/orsc.6.1.19. 
  14. U.S. Government Accountability Office (17 December 2010). "Public Health Information Technology: Additional Strategic Planning Needed to Guide HHS's Efforts to Establish Electronic Situational Awareness Capabilities". pp. 49. http://www.gao.gov/products/GAO-11-99. 
  15. Grossmann, C.; Goolsby, W.A.; Olsen, L.A. (2011). Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. National Academies Press. pp. 340. ISBN 9780309120654. 
  16. 16.0 16.1 16.2 Burton, R.M.; Obel, B. (2004). "The Dynamics of the Change Process". Strategic Organizational Diagnosis and Design. 4. Springer U.S.. pp. 385-420. ISBN 9781441991140. 
  17. 17.0 17.1 Nonaka, I. (1994). "A dynamic theory of organizational knowledge creation". Organizational Science 5 (1): 14–37. doi:10.1287/orsc.5.1.14. 
  18. 18.0 18.1 Popper, M.; Lipshitz, R. (1998). "Organizational learning mechanisms: A structural and cultural approach to organizational learning". Journal of Applied Behavioral Science 34 (2): 161–179. doi:10.1177/0021886398342003. 
  19. Crossan, M.M.; Lane, H.W.; White, R.E. (1999). "An organizational learning framework: From intuition to institution". The Academy of Management Review 24 (3): 522-537. doi:10.5465/AMR.1999.2202135. 
  20. Warren, L.; Fuller, T. (2011). "Contrasting Approaches to Preparedness: A Reflection on Two Case Studies". Managing Adaptability, Intervention, and People in Enterprise Information Systems. IGI Global. pp. 18–34. doi:10.4018/978-1-60960-529-2.ch002. ISBN 9781609605292. 
  21. Klein, G.; Moon, B.; Hoffman, R.R. (2006). "Making sense of Sensemaking 1: Alternative perspectives". IEEE Intelligent Systems 21 (4): 70–73. doi:10.1109/MIS.2006.75. 
  22. Endsley, M.R.; Jones, D.G. (2011). Designing for Situation Awareness: An Approach to User-Centered Design (2nd ed.). CRC Press. pp. 396. ISBN 9781420063554. 

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.