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In an earlier article, we mentioned that interoperability is the technique through which different health information systems (irrespective of who the provider is) work in tandem with each other to share patient data seamlessly across all networks, without any hindrances or boundaries. This way, any healthcare professional from anywhere in the world can access the patient’s records and provide care and treatment without delay. Though it may sound all rosy and good, there are some barriers to achieving interoperability.
This article is a continuation of our blog series on Interoperability. In this post, we will be talking about those barriers and how they can be overcome.
Before actually getting into the barriers section, it would be worth having a quick brief of the different levels in interoperability because that would help you understand where the barriers can arise as well:
1. Data interoperability, by level
There are three levels of interoperability - Structural, Foundational and Semantic. Each level has its capabilities and uses in the modern healthcare industry. This would be discussed in a future article.
2. Data deployment, depending on the deployment type
There are two kinds of interoperability based on the deployment category - Cloud-based deployment (users can utilize the same management tools and software) and On-premise interoperability (mainly used by large hospitals).
3. Data on an application level
Data interoperability on the basis of the application here means collecting and sharing data while a diagnosis is made. A huge repository of data is thus being disseminated at the diagnosis stage so they can be shared as people get diagnosed at the initial stage and start timely treatment.
4. Data interoperability through the right model
When data is shared on the basis of a centralized model, there is a higher level of protection and only those have the permission can see it. This would prevent unauthorized access to a great extent.
5. Data interoperability by end-user or patient
The growing occurrence of chronic diseases means the number of patient admissions is also increasing, leading to consequent generation of patient data; data that must be used appropriately.
Keeping those points in mind, let’s have a look at the challenges in achieving interoperability:
As the number of data sources increases, so will the amount of data that’s coming in. And this would increase when more patients are using smart devices and wearables. The huge influx of patient-generated health data comes from elderly patients, patients with chronic conditions, in-hospital patients, from wellness programs and even from remote patient monitoring systems.
All the levels of interoperability that we mentioned above come into practice here. But the problem is that all this information requires several layers of interoperability, and also involves semantic interoperability and syntactic interoperability. And there could also be social, cultural, economic and even policy barriers to interoperability. So when the influx of data is huge, then it would be difficult to clear them all and collect relevant data.
The more partnerships you form, the more complicated it becomes to manage them. For example, if a huge hospital system acquires smaller hospitals, then aligning the IT systems could be a tough job. These IT systems may have to combat a number of tasks in order to make the data interoperable, including scheduling, and even sometimes converting the paper records.
Often when acquisition happens, this part is often neglected, leading to time-consuming jobs by the IT people. In order to ensure that the end-user or the patient doesn't suffer as a result of the acquisitions, the hospital systems must conduct a thorough audit and then make an interoperability game plan. Make a reasonable timetable to move the data and make it interoperable so the healthcare professionals and the patients would benefit from it.
You can think of interoperability as having two different layers - one layer would be the data layer, and the other, the workflow layer. Problems with workflows can hinder interoperability. Actually, workflow interoperability is one layer above data interoperability, and it is much more difficult to achieve. Sharing data is one thing, but sharing the workflow of another organization and using that information to coordinate it with the internal workflow of another organization is critical.
Once that happens seamlessly, because there would be both speed and quality, the patient can move from one clinic to another or from a clinic to a rehabilitation center and back (each would require a different aspect of patient care) seamlessly. Creating automatic workflows is one answer to this, and once the workflows move from one organization to the partner organization, progress can be fast.
There is great progress with the data layer, and once this problem with the workflow layer is completely eradicated, there would be a rapid exchange of information and output.
The Centers for Medicare and Medicaid Services (CMS) issued a new rule on February 22, 2019, concerning the many programs that CMS manages. According to the rule, patients can access their health information on interoperable forms. The patient’s health information, including their health plans and providers, complete with a history of illnesses will all be shared. The payers here, mostly insurance providers that offer QHPs or Qualified Health Plans, have massive information about patients that would be really helpful to the providers.
But so far, they have been slow in cooperating with the healthcare providers, and some of them do not want to share their data at all. That has to change. Hopefully, with the new rules, there will be a change in that soon. Once that happens the payers would have to maintain open APIs to enable third-party applications to quickly retrieve patient health data (with approval from patients, of course) and adjudicated claims data, clinical data, provider directory data and so on.
There is no doubt that there should be standardization in health IT standards, and even the stakeholders agree on that but it is the way these standards are interpreted and enforced that makes all the difference. There are various interoperability standards that stand as a challenge to interoperability advancement. The ecosystem is so different in today’s healthcare industry. You can see a patient at the clinic, hospital, in hospices and even through online consultation. So care is happening at multiple venues. If there is a lack of interoperability standards, then it makes data exchange difficult or limited.
This can be solved by having a network where the healthcare exchange between all providers and organizations that promote interoperability uses software that’s already embedded into the provider’s EHR system. This will cut out the standardization problems and facilitate exchanges. However, following the same standards is still a limiting factor, but that should be cleared with time.
One of the biggest barriers to interoperability is the inability to match the patient with the records. Errors could happen at any point, right when the patient fills out his forms to the point of care. It is obvious that one small error can really bring the whole thing down and because there are no actual people, once it goes through different systems, the errors may not even be detected.
Inaccuracies, inconsistencies and incomplete forms can pose real problems in actual matching. This makes comparing a particular patient’s information across other health records a tough task. Following certain common practices for identifying demographic data, developing a public-private collaboration effort and sharing best practices are a few ways in which these discrepancies can be avoided.
There are new interoperability movement initiatives springing up every now and then. Government agencies need some kind of uniform method to measure the progress of these initiatives. This would help in the assessment of health IT interoperability in a consistent way. But sometimes, interoperability measurement standards can vary among the stakeholders too. And some of the older measurements can also pose confusion.
Some of these are Health Level 7 standards, International Classification of Disease, Digital Imaging and Communications in Medicine (DICOM), OpenEHR, CEN/ISO EN13606 and so on. Healthcare institutes do not always conform to a single standard, and the use of more than one standard can definitely lead to serious errors.
One way to overcome this problem would be having a uniform measurement framework - Interoperability Standards Measurement Framework. Through this framework, the industry’s activities in implementing interoperability standards would be analyzed, and measured in detail. This would help track the progress on a national scale. Through this framework, the healthcare IT professionals/companies are required to report the following: the percentage of end-users following a particular standard, what are the number of transactions that happen according to these particular standards and after implementation, record the conformation and customization of those standards.
EHRs in the hospitals are government certified so they meet efficiency standards. However, they go through a series of customizations to make them user-friendly. There can be several unique interfaces as well so when it comes to interoperability, complexities arise. Once these complexities are removed, it would be easy to exchange or share patient information. This is why optimizing and installing an EHR system turns out to be a tedious task, especially if it is supposed to work within a work culture.
Another challenge faced with EHRs of the hospitals is that data from nursing homes and other living settings may come in at an alarming date. The hospitals and health systems must develop strategies to integrate the data into the EHRs.
Is it possible to improve what you can’t measure? Probably not. That’s why it becomes next to impossible to quantify the errors and other issues in the health care systems because the systems do not speak to each other in the way they should. This is a difficulty because payers, vendors, healthcare systems and healthcare organizations cannot measure or track outcomes. It is only when providers and payers can measure problems in the healthcare chain that they can analyze problem areas, provide quality treatment and monitor the changes.
With security challenges threatening to expose patient information, the interoperability plunge should be done with a great deal of care. Patient data is sensitive data, and it shouldn’t get into the wrong hands, and state, national and international laws on this must be adhered to. However, the legal landscape on privacy ad security regulations is still hazy. That is why healthcare organizations must prepare their HIPAA compliance audits when they approach interoperability.
The main aim of interoperability is to bring together everyone related to the healthcare sector. This can be started by encouraging medical professionals to use common medical terminology while creating patient health records (so there won’t be mismatches), coordinating the stakeholders in the medical industry because they really do have to collaborate on creating policies and standards in enforcing interoperability, and cut out the staff workflow issues completely.
It is imperative for all healthcare facilities to adopt interoperability because that’s the only way in which they can reach their goal - provide comprehensive diagnosis, treatment and care to the patient. Having patient data at their fingertips would enable healthcare professionals to provide healthcare without delays. And since the data comes in through various levels as mentioned above, those factors have to be considered as well.
Just like there are two sides to a coin, if there are several advantages to achieving interoperability among healthcare systems, the barriers that we mentioned above will also have to be considered. A huge change in this scenario will also come if the hospitals and clinics begin to consider the patient medical records as not their own property, but that of the patients.
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