By Jason Roys
The healthcare industry has seen huge gains from the rapid growth of technology, but it also faces disruptions and challenges as that technology develops. We now see the digitalization of health care in the use of electronic medical records (EMRs), in computerized patient records, and in medical equipment, wearable sensors, and mobile health applications. This technology is available 24/7 and produces data 24/7.
Health care has shifted from paper-based systems to computer-based systems and is heading toward a nearly paperless system. Healthcare delivery of information has changed – from moving between departments, to moving between organizations, to moving globally. Technologies originally were developed for providers, but now patients use it daily as a network-enabled clinical environment grows.
Meanwhile, Big Data has allowed for the anonymous use of a huge amount of patient information for healthcare research. The value extends far beyond the data’s original use for a given patient’s outcomes. New systems need to be developed to deal with all the growth, both in the kind and in the amount of patient information that is being handled.
Here’s a look at some of the technological challenges faced by the health care industry and some suggestions for dealing with them.
Standardized user interface
As the use of healthcare technology grows, standards must be set and met. A carefully designed user interface (UI), focused on the look and feel of the digital project, should visually guide the user. The images, icons, fonts, buttons and colors should be self-evident and intuitive. They should allow the user and the medical device or website to interact easily, and they should be designed with an eye toward the human factors: to make the user’s experience as easy as possible.
Whether it’s the design of a website or the interface for EMR, user ease should be a primary concern. What is the user’s level of technological skill? How can the interface be designed to make it easy for the user to answer questions or input data?
Standardization also is vital. A doctor or nurse who must input and read information on six different devices in one day is ill-served by technology that presents six different ways of, for example, supplying patient ID information. This invites error and causes frustration.
A simple lack of standardization can confuse users and cause serious problems. Maybe there’s too much data on the screen at one time, or maybe one system’s “Save” is another system’s “Continue” or “Next.” Problems this basic can set off malpractice cases.
Healthcare organizations can (and should) work with software manufacturers as new systems are developed to let them know what’s needed. That kind of cooperation can lead to standardized, accessible user interface. The machines should accommodate the people. That includes people who need adaptations for screen size, font size, or internet speed issues. When it comes to customer-facing devices, designers also should consider compliance with ADA (Americans with Disabilities Act) standards.
When health care providers and organizations buy equipment, they should make standardization and user interface important factors. And they should let the vendors know that. Also, governments and governing bodies need to set and endorse standards for development, design, and testing of software.
Interoperability
One of the most critical portions of digital health care is interoperability, or the ability of systems to exchange information and then use the information. Healthcare organizations must be able to access and exchange data across organizational boundaries – and even across national borders. Interoperability is a critical part of digital health care, and one that never ceases to present new challenges. For example, something as fundamental as identifying a patient – or at least linking a patient’s data with an identification tag for use in research and analysis – still escapes experts. The various systems used by organizations within a country and internationally stymie attempts to perform deep analysis of data.
Another challenge facing healthcare organizations is maintaining patient data security while sharing with other care teams. Healthcare providers see great benefits coming from the ability to share data. They want to move data across all departments of a hospital, from hospital to hospital, and even from a hospital to national or international health agencies. Patients also could benefit from the ability to see all their medical information online in one place. However, all parties have concerns about maintaining privacy while sharing such information. Hospitals have made great strides in merging various systems into Health Information Exchanges. These HIEs, however, are valuable micro-solutions.
At the national and global level, healthcare providers, government agencies, software companies, equipment manufacturers and nonprofits like the Center for Medical Interoperability have many problems to work out.
Big data - making sense of it all and getting the most value from it
All this interoperability, this secure trading of patient information, involves not only information from electronic health records (EHRs) but also from consumer applications and from wearable devices. The amount of information flowing into health care’s IT systems today is staggering. Can it be used for research? Big Data says yes.
Some describe the Big Data in health care as having “four Vs”:
Volume, or the amount of patient data created by patients, providers, payers, labs and pharmacies.
Velocity, or the rate, in real time, of all of this valuable information arriving at your site.
Variety, or the different types of data (from questionnaires, smartphones, sensors, wearables, and other medical devices).
Validity, or the ability to assure that the onslaught of information is accurate information.
The ability to secure, access, and store all that patient information while maintaining patient security, as well as the ability for the provider to convert it into meaningful insights, are the challenges for the use of Big Data in the health care realm. Enabling automated surveillance can allow for the mining of specific data, but the ethics of this automated surveillance have not yet been firmly addressed. Artificial intelligence (AI) and machine learning (ML) are beginning to help researchers shape and use the flood of patient data, so that health care organizations can use it for the strengthening of public health.
Privacy and security of data
We’ve mentioned the critical importance of securing patient data. Even as IT teams guard against worms and viruses, cyberattacks continually occur. They cause breaches in patient privacy, of course. But they also create downtime for health care IT, lost work hours, and expenditures for new technology and networks. And though you don't hear about it often, providers sometimes pay large amounts of ransom to attackers.
One of the strongest risks to patient security involves the ability to match patient information, not only within but also across EHRs, and then across organizations, states, and nations. Australia, the UK and other countries have developed unambiguous patient identification while others, including the U.S., haven't. The industry needs a way to accurately link patients across locations.
And, as the Internet of Things (IoT) becomes more prominently used, the industry must pay more attention to finding new methods to secure privacy and to deflect hackers.
Implementation/training of new while retaining old
Software can quickly go out of date, and outdated software can lead to insecure data. Sure, a healthcare system can update its operating system – and can hire an IT department that’s fluent in every operating system, both old and new (with a strong focus on the new) so that issues like interoperability, the ability to use Big Data safely, and data security can be addressed. In this way, risk assessment can be performed so that patient information can remain secure. But not only is that expensive, would it be enough?
Many believe that incidences of hacking or cyber insecurity are grossly underreported. That’s why new tools that can estimate the occurrence and the severity of these attacks must be developed. But even as security issues are addressed, the constant development and redevelopment of health care systems generates issues for providers. Pain points between new systems and old systems, or between old systems and updated versions include loss or inaccessibility of data, training costs and training fatigue. We also find a higher risk of error for overstressed health care workers navigating between systems and trying to remember how to use each one. In this regard, it echoes the standardization problem.
Looking for solutions
Many believe that digital health care should apply the level of security used in the nuclear, aerospace, or defense industries. Some countries are developing guidelines for the oversight of IT in health care. The U.S. Food and Drug Administration has announced a pre-certification program for software developers. The U.S., however, has not yet developed a government- or industry-led regulatory system.
As the use of artificial intelligence (AI) grows in the health care industry, it will be critical to train health care providers on issues like: How can the user know when it’s important to overrule the computer? And, on the other hand: How can they know when it’s safe to follow AI-driven directives?
As we find the answers to health information challenges, advances in technology will create new challenges.
SDV International
Keeping up with advancements in health care technology and the digitalization of health care information can be daunting. It pays to have someone in your corner who understands the issues and who is willing to work with you to customize a system to your specific needs. SDV International offers solutions, dedication, and value. Its team – highly skilled professionals all – delivers innovative and cost-effective healthcare technology solutions on time and within your budget.