Html1 Day Ago2 Days Walden Week 11 Health Informa

Health Informatics and Surveillance

Dr. Srikanta Banerjee

Maia Junco

Interoperability

Post an analysis of the obstacles that impact the interoperability of the disease surveillance systems.

Interoperability in health care is the ability to exchange information and data between systems, which allows us to integrate and unify a health system network (Lombardo & Buckeridge, 2007). Smart systems can automatically transmit information through digital innovations. For example, if a person has a lab test, every health provider involved in the health of this patient would be able to see the results. This functionality enables the transmission of the structure (Syntactic) and meaning (Semantic)of data in a uniform way.

One of the challenges that health care currently has is the number of separate companies that are trying to provide interface networks for health care providers. They are all competing to be used by providers and creating separate data systems that are all isolated from each other, which is a problem for providers who need a unified and interoperable system. Stakeholders are responsible for agreeing on a unified data system when designing a surveillance system. The software used for a system will determine the vulnerability of the data and the chance of it being stolen or staying protected (Panesar, 2019). The fragmentation of data is common today across a patient’s areas of experience; Phones, Fitbits, watches, and electronic health records collected by physicians are all stored in different systems and owned by different companies(Panesar, 2019). When physicians and labs incorrectly map Health Electronic Records (EHR) it can cause a domino effect on automatized systems and can cause epidemiologists to receive inaccurate data (Rajamani et al., 2018).

Propose ways to improve/address the legal, ethical, and practical data-sharing obstacles.

Developing and investing in data science teams that are focused on learning from data, is key to a successful healthcare strategy. Improving value for both the patient and the provider requires a team of data science professionals’ (Panesar, 2019). Increasing health surveillance systems’ efficiency of sharing data reduces the disclosure of unprotected information. As technology continues to evolve, we are constantly developing new health surveillance systems that are faster and more reliable than previous systems. Digital technology and Interoperability with integrated semantic and syntactic functions creates better understanding and efficiency for any system. The Center for Disease Control (CDC), the Association of Public Health Laboratories (APHL), and the Informatics Messaging Services (AIMS) platform created the Electronic Case Report system (ECR), which ensures that data shared by surveillance systems are minimally identifiable (ECR, nd). Rules for reporting data have enforced the spread of information between state health departments and national institutions. This new system helps health care providers report to a single system, where the information can be properly protected.

Evaluate how this impacts the future of local, state, national, and international systems/agencies sharing and use of information.

The distortion of data and data disasters from big, unregulated companies such as Facebook and Google have made individuals and physicians reluctant to use digital platforms for health. Back in 2004, Google released a Machine learning algorithm that profits from predicting user’s behavior, and after this was implemented, Google’s profits increased incredibly (Docherty & Fanning, 2019). Worldwide, Artificial Intelligence (AI) tools serve the people who control them, if those people’s interests are against humankind, then civilization is in danger. Scientists using AI, at all levels, have the responsibility of protecting society from this new technology.

In my view, the use of data by governments for social control is unethical. The Chinese government invasively monitors surveillance cameras and cellular data to identify social “misbehavior” that goes against their own political agenda (Docherty & Fanning, 2019). Digital technology interventions should be used for better analysis and efficiency of all systems in society. AI-driven self-care, virtual care, EHR systems, and the exchanged between systems should be unified by a singular high-quality network.

Keeping an individual’s private information secure is a necessity for the ongoing evolution of data sharing systems. Data should be organized around the patient at the local, state, and federal level and kept safe by the HIPPA law that protects the individual’s right to privacy (U.S. Department of Health and Human Services, n.d.). Prioritizing data efficiency and interoperability will shape the future of data sharing by creating a centralized, collaborative environment for health care and public health (Panesar, 2019).

References

Docherty N.,Fanning D. (2019) In the Age of AI [Documentary]. Frontline PBS.

Lombardo, J.S. & Buckeridge, D.L. (2007). Disease surveillance: A public health informatics approach. John Wiley & Sons, Inc Publications.

Panesar A. (2019).Machine Learning and AI for Healthcare Coventry, UKISBN-13 (pbk): 978-1-4842-3798-4https://doi.org/10.1007/978-1-4842-3799-1

Rajamani S. , Kayser A., Emerson E., Solarz S.Evaluation of Data Exchange Process for Interoperability and Impact on Electronic Laboratory Reporting Quality to a State Public Health Agency. Online Journal of Public Health InformaticsISSN 1947-2579 http://ojphi.org * 10(2):e204, 2018

Re-Post (please disregard previous):

Interoperability is the ability to get used or operated reciprocally (i.e., in an interrelated manner) (interoperable, n.d.). Most essentially in health care, interoperability enables the exchange of health care information (FairWarning, n.d.).

Having effectively achieved the successful implementation of and “meaningful use” of electronic health records (EHRs) within the American medical system (Reisman, 2017), the federal government’s next officially declared goal, called Promoting Interoperability (PI), is to enhance the capacity for electronic exchange or sharing of that health information (Morse, 2018; CMS, 2021).

Interoperability offers several benefits, including providing physicians are more complete picture of a patient’s medical history, enabling the patient (via development of an application programming interfaces, or APIs) (Morse, 2018) to view all of his or her own information across multiple providers within one, single patient portal, decreased redundancy and therefore improved efficiency, and easier detection of doc-shopping (Rosenfeld, 2019; CDC, n.d.).

01. Post an analysis of the obstacles that impact interoperability of the disease surveillance systems.

Yet, the path is not without obstacles, some owing to differing priorities between different stakeholders. Given that companies like Epic and Cerner profit from the use of their proprietary systems (and not so much from the inter-use with other, possibly rival systems), it is reasonable to question whether they desire that their systems be interoperable at all (Rosenfeld, 2019). Allegations have been made that these or other companies are actively (even if quietly) resisting progress towards interoperability, a practice called “information-blocking” (ONC, 2021; Powell & Alexander, 2019; Reisman, 2017). Although the practice is already, in at least some sense(s), illegal under the 21st Century Cures Act, information-blocking can take many forms and so is difficult to define (ONC, 2021) and perhaps even more difficult to identify and then prove (Monica, 2017; Powell & Alexander, 2019; Powell & Alexander, 2019).

Three other, ongoing challenges, partly owing to siloed/programmatic funding and its consequent number of varied and proprietary information systems, standing in the way of promoting interoperability include (TigerConnect, n.d.; Monica, 2017; Leider, Shah, Williams, Gupta, & Castrucci, 2017): 1) lack of consistency or standardized way to identify patients, 2) lack of standards for transmission between medical care systems, and 3) coordinating stakeholders across the health industry.

Two complicating factors that slow progress towards interoperability are the need to first ensure “semantic interoperability” (e.g., verifying or achieving that a two separate data fields in two different databases are really talking about the same thing and in the same way) and that interoperability expertise itself can be an expensive service to obtain (MedHost, n.d.; CMS, 2021).

One digital marketing analyst observes that the more partnerships an institution forms, the more complicated trying to manage them all can become (Mahin, 2019). She notes that this is especially true if a huge hospital system acquires several smaller ones with interoperability issues being considered only as an afterthought. She recommends that institutions should conduct an interoperability audit before such partnerships are even agreed to, much less undertaken (Mahin, 2019).

One challenge is that medical care providers use different standards for clinical and/or administrative data, with some of the major ones being (Mahin, 2019): Health Level Seven (HL-07), International Classification of Disease (ICD), Digital Imaging and Communications in Medicine (DICOM), OpenEHR, CEN/ISO EN13606, and others (Mahin, 2019). Even a given medical care provider sometimes uses more than one standard. Use of more than one can lead to errors (Mahin, 2019).

02. Propose ways to improve/address the legal, ethical, and practical data-sharing obstacles.

Regarding the partnering or merging of different medical care providers, I join with Mahin (2019) in recommending that institutions perform an interoperability audit before agreeing to partner up.

Regarding the differing data standards, I recommend using a uniform measurement framework: Interoperability Standards Measurement Framework (Mahin, 2019; HIMSS, 2018; ONC, 2017). This might be easier said than done, however. Even a cursory comparison and contrast between the differing data standards will show that they are not so easily meshed together. Even in their declared aims, differences exist between the systems. Most obviously, for example, HL-07 focuses on interrelating clinical data with administrative data, whereas ICD focuses on classification of diseases for clinical and research purposes. There are likely to be some fundamental, not-so-reconcilable differences in those two somewhat differing purposes.

Other interoperability priority areas should include developing common data element standards common to many registries, developing some specialty-specific standards (e.g., for pathology specimen collection), and improving patient-matching (especially important in specialties with considerable patient overlap, such as anesthesia) (Blumenthal, 2018).

The trickiest challenge: Information-blocking. Thoughts, class?

Information-blocking strikes me as the trickiest challenge, and I’m genuinely undecided on how to deal with it. To whatever extent it is currently happening (perhaps poorly detected), clearly the 21st Century Cures Act is insufficient in dealing with it. The most tempting solution would be to simply compel companies by force of law (e.g., with legislation) to make their systems interoperable along some arbitrary timeline, but I would caution against that approach for two reasons: one practical, the other moral. First, practically speaking, I would need a clearer picture of the technical challenges involved. Second, morally speaking, let us not forget that, presumably, the companies designed the systems primarily to be used by the clinics, not to be inter-used with other, possibly rival systems. As an analogous hypothetical, let us suppose that Bob has, for profit, built a Lego fort for Trevor, and Trevor is much pleased with it. Is it really fair for some third party, Kevin, to come along after the fact and demand that Trevor somehow modify the Lego fort be compatible with Kevin’s sand castle? The idea of the Lego fort and the sand castle being interoperable sounds like a great state of affairs, but that may never have been considered in the design of the Lego fort that both Bob and Trevor are happy to have transacted, and arguably Kevin has no moral authority to compel how the Lego fort shall now be used and/or is being insensitive to the complexities involved in what he is demanding.

Even the term “blocking” can be misleading. Simply not making systems interoperable is not “blocking”; it is passive in nature, a non-action. Actively opposing steps towards interoperability would constitute blocking, and so accusing the industry of that active verb would be an intelligible accusation (but that would require proof). Simply inferring active opposition from mere passivity or non-action on their part seems to me unfair.

So, as I write this, I genuinely don’t know yet how I would propose to deal with information-blocking, but I think that a good next step might be to hold congressional hearings with all stakeholders involved, including the industry’s providers. I would solicit their thoughts on the desirability and “doability” of achieving industry-wide, nationwide interoperability.

03. Evaluate how this impacts the future of local, state, national, and international systems/agencies sharing and use of information.

The Office of the National Coordinator for Health Information Technology (ONC) has predicted that the nation’s health system shall achieve interoperability sometime between 20201 and 2024 (Reisman, 2017). I expect this to happen, but, in order to get there, some challenges will need to be addressed, including physician dissatisfaction with EHRs, some of the more burdensome aspects of current regulation of EHRs, cost, and (to whatever extent it might be happening) information-blocking (Reisman, 2017).

Once achieved, nationwide operability would maximize the benefits described previously (i.e. providing physicians are more complete picture of a patient’s medical history, enabling the patient to view all of his or her own information across multiple providers within one, single patient portal, decreased redundancy and therefore improved efficiency, and easier detection of doc-shopping (Rosenfeld, 2019; CDC, n.d.). It would likely also benefit surveillance by making data across jurisdictions more relatable.

References

Bayley, K. B., Belnap, T., Savitz, L., Masica, A. L., Shah, N., & Fleming, N. S. (2013). Challenges in using electronic health record data for CER: Experience of 4 learning organizations and solutions applied, 51(8), s80–s86. https://doi.org/10.1097/MLR.0b013e31829b1d48

Blumenthal, S. (2018). Improving interoperability between registries and EHRs. AMIA Joint Summits on Translational Science Proceedings, 2018, 20–25.

FairWarning. (2020, December 4). How privacy and security can overcome interoperability challenges in healthcare. FairWarning. https://www.fairwarning.com/insights/blog/how-privacy-and-security-can-overcome-interoperability-challenges-in-healthcare.

Healthcare Information and Management Systems Society [HIMSS]. (2018, October 12). Proposed interoperability standards measurement framework. HIMSS. https://www.himss.org/resources/proposed-interoperability-standards-measurement-framework.

interoperable. (n.d.). Dictionary.com. https://www.dictionary.com/browse/interoperability?s=t. Based on the Random House Unabridged Dictionary

Leider, J. P., Shah, G. H., Williams, K. S., Gupta, A., & Castrucci, B. C. (2017). Data, staff, and money: Leadership reflections on the future of public health informatics. Journal of Public Health Management and Practice, 23(3), 302–310. https://doi.org/10.1097/PHH.0000000000000580

Mahin, S. (2019, November 26). What Is Interoperability? (+Barriers to Achieving It). G2. https://learn.g2.com/interoperability.

MedHost. (2020, July 16). 9 challenges in the era of interoperability. MEDHOST. https://www.medhost.com/blog/9-challenges-interoperability/.

Monica, K. (2017, August 14). Top 5 challenges to achieving healthcare interoperability. EHRIntelligence. https://ehrintelligence.com/news/top-5-challenges-to-achieving-healthcare-interoperability.

Morse, S. (2018, May 1). How ‘promoting interoperability’ will take health data sharing to another level. Healthcare IT News. https://www.healthcareitnews.com/news/how-promoting-interoperability-will-take-health-data-sharing-another-level.

Powell, K. R., & Alexander, G. L. (2019). Mitigating barriers to interoperability in health care. Online Journal of Nursing Informatics, 23(2).

Reisman, M. (2017). EHRs: The challenge of making electronic data usable and interoperable. Pharmacy & Therapeutics, 42(9), 572–575.

Rosenfeld, S. (2019, February 6). Weighing the benefits and challenges of interoperability. Healthcare Analytic News. https://www.idigitalhealth.com/news/weighing-the-benefits-and-challenges-of-interoperability-.

TigerConnect. (n.d.). Four challenges to achieving healthcare interoperability.

United States Centers for Disease Control and Prevention [CDC]. (n.d.). Doctor-shopping laws. https://www.cdc.gov/phlp/docs/menu-shoppinglaws.pdf.

United States Centers for Medicare and Medicaid Services [CMS]. (2021, January 8). Promoting interoperability programs. CMS.gov. https://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms.

United States Office of the National Coordinator for Health Information Technology [ONC]. (2017, September 8). Proposed interoperability standards measurement framework public comments. HealthIT.gov. https://www.healthit.gov/topic/interoperability/proposed-interoperability-standards-measurement-framework-public-comments.

United States Office of the National Coordinator for Health Information Technology [ONC]. (2021, January 5). Information-blocking. HealthIT.gov. https://www.healthit.gov/topic/information-blocking.

Post an analysis of the obstacles that impact interoperability of the disease surveillance systems.

Interoperability is the computer systems or software’s ability to exchange and use information (Lombardo & Buckeridge, 2017). Inoperability is essential, especially with disease surveillance systems, because it assists in monitoring, identifying, and tracking health events and trends that may not go unnoticed between the different levels (local, regional, state, national). While technology and science have significantly evolved to create such systems, there are always limitations that impact either efficiency and effectiveness. Similar to other disciplines, Public Health and its’ abilities depend on funding, and in the case of Public Health, much of its’ funding comes from the federal government, and then the money is reallocated to agencies, states, and lower levels of government. Depending on the disease surveillance system’s complexity and depth, it can be costly to maintain (Lombardo & Buckeridge, 2017). Placing these systems in places with financial barriers will impact interoperability by leaving gaps in the effectiveness of the disease surveillance system because some areas won’t be able to report health events/trends.

Interoperability is about exchanging/sharing information, but there are no universal standards amongst agencies that house disease surveillance systems. “Semantic interoperability is “the ability to import utterances from another computer without prior negotiation”’ (Dixon et al.,2013). The way to achieve semantic interoperability is to have a set of standards on defining, collecting, timing, recording (e.g., ICD-10 codes, electronic health records (EHR)), sharing, and representing health trends and events. Without interoperability, there is a risk of incomplete disease profiles, case series/reports, and events going unnoticed amongst the internetworks of agencies, states, and local health departments (LHDs). Further, this can significantly impact data analysis and the dissemination of honest and proven statistics, records, and facts. With populations becoming more transient, it is vital to have a complete picture and holistic approach to reduce or eliminate the threat.

Lastly, privacy and trust are issues that impact the interoperability of the disease surveillance systems. All types of surveillance (active, passive, and sentinel), except for syndromic, rely mostly on laboratory confirming tests that are placed in electronic medical records (EMRs) or electronic health records (EHRs). While the enactment of the Health Insurance Portability and Accountability Act (HIPPA) aims to ensure a standard of privacy and security for patient health information in the form of protected health information (PHI), there are always risks when sharing across servers or databases. Many local health departments (LHDs) are contractually not authorized to share data out of a specified jurisdiction or network outside of the threat hacks. This is not only a challenge for Public Health but disease surveillance systems.

Propose ways to improve/address the legal, ethical, and practical data-sharing obstacles.

An ethical and legal recommendation on addressing data-sharing obstacles would be to create a set of standards on identifying, recording, timing, representing, and sharing information data amongst networks and agencies. This may require additional training amongst healthcare and health informatics professionals or hiring a specific team to verify and clean information as it is collected. This may be expensive, but the long-term impacts will be beneficial. In addition to establishing a set of standards, a practical recommendation for improving data-sharing obstacles is assessing interoperability before creating partnerships. This compatible way systems can make internetworks and at least on those levels will maintain the effectiveness of the disease surveillance system. Lastly, to improve data-sharing obstacles, increasing security, like including password/code protected over the data, could prove beneficial.

Evaluate how this impacts the future of local, state, national, and international systems/agencies sharing and use of information.

The recommendations previously mentioned can positively impact the future of local, state, national, and international systems/agencies sharing and use of information by securing partnerships and networks. Creating a universal set of standards will help in all types of public health services since they rely heavily on health records and health indicators (e.g., signs and symptoms). Evaluating the collecting, recording, timing, and sharing of lab reports, EMRs, and PHI in the healthcare field will help make surveillance systems more efficient and effective in monitoring, identifying, and disseminating health events and trends (Office of Public Health Scientific Services, 2018).

References

Dixon, B. E., Siegel, J. A., Oemig, T. V., & Grannis, S. J. (2013). Towards Interoperability for Public Health Surveillance: Experiences from Two States. Online Journal of Public Health Informatics, 5(1), e51.

Lombardo, J. S., & Buckeridge, D. L. (2007). Disease surveillance: A public health informatics approach. Hoboken, NJ: Wiley-Interscience

Office of Public Health Scientific Services. (2018). Public Health Surveillance: Preparing for the Future. Atlanta, GA: Centers for Disease Control and Prevention