Question

1-Contrast three challenges in conducting evaluation in public health informatics?

2-Support your response with at least 2 academic sources outside the required materials for the course?

Answer 1

Challenges

Safeguards are increasingly likely to be challenged as genetic information makes its way into the health care record. The risks of bias, discrimination, and social stigma increase dramatically as genetic data become available to clinicians and investigators. Indeed, genetic information “goes beyond the ordinary varieties of medical information in its predictive value” (Macklin, 1992).

Genetic data also may be valuable to people predicting outcomes, allocating resources, and the like. In addition, genetic data are rarely associated with only a single person; they may provide information about relatives, including relatives who do not want to know about their genetic makeup or maladies as well as relatives who would love dearly to know more about their kin’s genome.

There is still much work to be done in sorting out and addressing the ethical issues related to electronic storage, sharing, and retrieval of genetic data.

Social Challenges and Ethical Obligations

The expansion of evidence-based medicine and, in the United States, of managed care places a high premium on the tools of health informatics. The need for data on clinical outcomes is driven by a number of important social and scientific factors. Perhaps the most important among these factors is the increasing unwillingness of governments and insurers to pay for interventions and therapies that do not work or that do not work well enough to justify their cost. Health informatics helps clinicians, administrators, third-party payers, governments, researchers, and other parties to collect, store, retrieve, analyze, and scrutinize vast amounts of data. Such tasks may be undertaken not for the sake of any individual patient but rather for cost analysis and review, quality assessment, scientific research, and so forth. These functions are important, and if computers can improve their quality or accuracy, then so much the better. Challenges arise when intelligent machines are mistaken for decision making surrogates or when institutional or public policy recommends or demands that computer output stand proxy for human cognition.

Informatics and Managed Care

Consider the extraordinary utility of prognostic scoring systems or machines that use physiologic and mortality data to compare new critical-care patients with thousands of previous patients (Knaus et al., 1991). Such systems allow hospitals to track the performance of their critical-care units by, say, comparing the previous year’s outcomes to this year’s or by comparing one hospital to another. If, for instance, patients with a particular profile tend to survive longer than their predecessors, then it might be inferred that critical care has improved. Such scoring systems can be useful for internal research and for quality management (Figure 10.1). Now suppose that most previous patients with a particular physiologic profile have died in critical-care units; this information might be used to identify ways to improve care of such patients—or it might be used in support of arguments to contain costs by denying care to subsequent patients fitting the profile. An argument in support of such a nonresearch application might be that decisions to withdraw or withhold care are often and customarily made on the basis of subjective and fragmented evidence; so it is preferable to make such decisions on the basis of objective data of the sort that otherwise underlie sound clinical practice. Such outcomes data are precisely what fuels the engines of managed care, wherein health professionals and institutions compete on the basis of cost and outcomes (see Chapter 23). Why, people may argue, should society, or a managed-care organization, or an insurance company pay for critical care when there is objective evidence that such care will not be efficacious? Contrarily, consider the effect of denying care to such patients on the basis of future scientific insights. Scientific progress is often made by noticing that certain patients do better under certain circumstances, and investigation of such phenomena leads to better treatments. If all patients meeting certain criteria were denied therapy on the basis of a predictive tool, it would become a self-fulfilling prophecy for a much longer time that all such patients would not do well. Now consider use of a decision-support system to evaluate, review, or challenge decisions by human clinicians; indeed, imagine an insurance company using a diagnostic expert system to determine whether a physician should be reimbursed for a particular procedure. If the expert system has a track record for accuracy and reliability, and if the system “disagrees” with the human’s diagnosis or treatment plan, then the insurance company can contend that reimbursement for the procedure would be a mistake. After all, why pay a provider for doing a procedure that is not indicated, at least according to the computer?

three reasons why it is problematic to use clinical computer programs to guide policy or practice in these ways:

1. As we saw earlier with the standard view of computational diagnosis (and, by easy extension, prognosis), human cognition is still superior to machine intelligence. The act of rendering a diagnosis or prognosis is not merely a statistical operation performed on uninterpreted data. Rather, identifying a malady and predicting its course requires understanding a complex ensemble of causal relations, interactions among a large number of variables, and having a store of salient background knowledge.

2. Decisions about whether to treat a given patient are often value laden and must be made relative to treatment goals. In other words, it might be that a treatment will improve the quality of life but not extend life, or vice versa (Youngner, 1988). Whether such treatment is appropriate cannot be determined scientifically or statistically (Brody, 1989).

3. Applying computational operations on aggregate data to individual patients runs the risk of including individuals in groups they resemble but to which they do not actually belong. Of course, human clinicians run this risk all the time the challenge of inferring correctly that an individual is a member of a set, group, or class is one of the oldest problems in logic and in the philosophy of science. The point is that computers have not solved this problem, yet, and allowing policy to be guided by simple or unanalyzed correlations constitutes a conceptual error.

Grand Challenges of Public Health Informatics

As might be expected from the public health principles described above and illustrated by the case study, the nature of public health also defines a special set of informatics application challenges. For example, to assess the health and risk status of a a population, data must be obtained from multiple disparate sources (e.g., hospitals, social service agencies, police, departments of labor and industry, population surveys, and on-site inspections). Data about particular individuals from these sources must be accurately combined, then individual-level data must be compiled into usable, aggregate forms at the population level. This information must be presented in clear and compelling ways to legislators and other policymakers, scientists, advocacy groups, and the public while ensuring the confidentiality of the health information of specific individuals.

Although information science and technology can improve public health practice in various ways, three areas represent grand challenges for public health informatics developing coherent, integrated national public health information systems, developing closer integration of public health and clinical care, and addressing pervasive concerns about the effects of information technology on confidentiality and privacy.

One goal of public health informatics is ensuring the capacity to assess community problems in a comprehensive manner through the development of integrated nationwide public health data systems. This will require a clear definition of public health data needs and the sources of these data, consensus on data and communication standards to facilitate data quality, comparability, and exchange with policies to support data sharing and mechanisms and tools for accessing and disseminating data and information in a useful manner. Because electronic reporting will increasingly form the basis for surveillance systems, developmental efforts must also address such concerns as unambiguously defining the specific medical conditions that trigger automated data transmissions, working with reporting organizations to ensure that they have appropriate software and electronic communication capabilities, and ensuring that adequate capacity exists for analysis of the increased volumes of public health data that are anticipated.

A second challenge for public health informatics is facilitating the improved exchange of information between public health and clinical care. Much of the data in public health information systems comes from forms that are filled out by hand and later computer-coded. Even where reporting is electronic, initial data entry is typically manual. This results in serious under-reporting of many reportable diseases and conditions.

Data should flow automatically to public health from clinical and laboratory information systems. When these data are appropriately compiled by public health information systems, they should allow rapid and accurate assessments and disease control responses, as well as the formulation of improved clinical guidelines and interventions. Conversely, automated presentation to clinicians of prevention guidelines has been shown to improve clinical care, and there are other ways in which the skills and activities of the public health community (e.g., community outreach) could benefit clinical care. Electronic information sharing and data exchange provide the means by which we can better integrate public health and clinical care activities, but creativity and hard work are needed to take full advantage of these opportunities.

Finally, privacy, confidentiality, and security are pervasive and persistent challenges to progress in public health informatics. Information systems are correctly perceived by the public as being a double-edged sword: Whatever is done to make integrated, comprehensive information more easily available for laudable and worthwhile purposes must of necessity create new opportunities for misuse. Public health often collects extremely sensitive personal medical information that has the potential for tremendous harm if improperly disclosed. Federal legislation that provides a fair and workable balance between individual privacy and the common good is needed to reassure the public and establish legal guidelines for handling sensitive information.

The Health Insurance Portability and Accountability Act (HIPAA) of 1996 will result in both privacy and security standards for all health plans (including Medicare and Medicaid), clearinghouses, and providers who use electronic data. Public health has had an excellent record of information protection in the past; the recently published HIPAA Privacy Rule continues to permit disclosure of protected health information to public health authorities for public health activities.⁵³ Public health agencies should adopt and enforce confidentiality policies that incorporate fair information practices and use state-of-the-art security measures to implement those policies.

To these three specific challenges for public health informatics, we can add another challenge, potentially more important (if less concrete) than the rest to apply information technology in unanticipated ways to reengineer public health and invent new ways to protect and promote community health. If, as we have said, the goal of public health is to promote health and prevent unnecessary disease, injury, and disability and the means are open-ended, we suggest that unexplored and unimagined ways to promote and protect community health using the power of modern information technology still exist. We have briefly outlined and illustrated the complex, multidimensional nature of public health as a discipline; we anticipate working with our clinical informatics colleagues as critical partners in addressing these major public health informatics challenges.

Sources

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC130068/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4371422/

http://eknygos.lsmuni.lt/springer/56/379-402.pdf

http://samples.jbpub.com/9780763771157/71157_CH01_001_018.pdf