Healthcare software development plays a significant role in
modernizing and improving healthcare. As healthcare systems
increasingly become digitized and rely on digital solutions for
managing patient information, streamlining processes, and improving
care delivery, we must develop robust and effective software to
facilitate these processes. Healthcare software systems include
electronic health records (EHRs) and other forms of practice
management software to allow clinicians to keep track of patient
information more efficiently; telemedicine services, which are
increasingly necessary for connecting rural and underserved areas to
care; and patient engagement systems, which facilitate patients’
involvement in managing their medical conditions.
Even if writing healthcare software sounds great, it does have its
challenges regardless of the difficulty of programming in general.
Firstly, you have technically and legally complex requirements of the
healthcare industry. Secondly, life and death decisions may depend on
the correct functioning of your code. Therefore, you should care about
data privacy, security, interoperability with legacy systems, and
usability of your software interface. This blog will overview these
common challenges and provide insights into how they can be addressed
to minimize the risk of working on a successful but non-compliant
healthcare solution.
Data protection is of paramount importance in healthcare software
development because healthcare data is hyper-sensitive in nature. Not
only for providing optimum care but also for maliciously re-using or
exploiting, healthcare data contains information such as medical
history, test results, personal identifiers, and more. Given the
sensitivity of such data, it not only forms a juicy target for
cyber-attacks but also severely affects individual lives, ranging from
identity theft and financial fraud to loss of patient trust.
Some of these issues can be addressed in the principles and practices
of data protection. Data minimization – i.e., the collection and
retention of only the data that is strictly necessary to provide the
service that the patient expects – is a central principle. Another
principle, purpose limitation, states that data can only be used for
the purpose for which it was actually collected, and another practice
is to regularly encrypt all data, hold it in secure storage
environments, analyze access logs, and limit access to qualified
individuals.
Despite all the control over data, healthcare presents many well-known
security challenges during the development of software. There is the
risk of a data breach: data can be accessed through several vectors,
including phishing attacks, malware, and software bugs or
vulnerabilities. Data exposure comes with steep costs, including fines
and loss of patient confidence in both the trust and scientific
expertise of physicians and hospitals. As healthcare systems become
increasingly linked, there is a greater risk of breaches between
integration points between different systems and platforms that are
connecting due to interoperability.
It is important to adopt strong security measures to overcome such
issues. Encryption, which encrypts data at rest as well as in transit,
is one of the most effective methods. For example, if malicious data
interceptors gain access to data, they are still unable to read the
data without the corresponding decryption keys. Another important
security measure is multi-factor authentication, which adds a layer of
protection against unauthorized access. Finally, healthcare entities
that handle PHI should conduct security audits on a regular basis to
identify gaps in security. This will help prevent security breaches
and ensure a higher level of data security and compliance in
healthcare.
Interoperability – the faculty for multiple systems to co-exist and
efficiently interact – is essential as it helps software work with
other systems already in use, including patient records, Electronic
Health Records (EHR), Laboratory Information Systems (LIS), and other
digital tools. To successfully advance toward value-based care, it is
imperative that systems can exchange and use information in a smooth,
efficient way. This allows for coordinated patient care across
different healthcare platforms, reducing fragmentation of patient
information and improving standards of care. When systems are
interoperable, providers access a single, comprehensive view of the
patient’s data, which facilitates decision-making, reduces human
error, and maximizes the efficiency of care. Interoperability, for
example, between EHRs automatically and correctly updates the record,
giving clinicians the current and past information that is
contemporaneous with the patient.
Besides ensuring that patient care is accurately documented and
available where it’s needed, effective interoperability reduces the
cost of care. Stakeholders can minimize the amount of time their
personnel spends on manual record-keeping or on duplicating tasks
performed within another system. The benefits of closed-loop care –
where updates in one system are quickly captured by others and saved
automatically – also provide better support to care coordination
activities across a wide range of medical disciplines, including
specialists in both inpatient and outpatient settings. Similarly, data
from interoperable systems helps to reduce the costs of administering
appointments, billing for medical care, and trafficking supplies. On
the population health management (PHM) side, interoperability makes it
possible to combine patient data from multiple ecosystems before it’s
fed into a health analytics engine driving policy design and public
health initiatives, as well as improving care for patients with
chronic conditions.
Yet the more important interoperability becomes, the greater the
integration challenges are: legacy systems, in particular, pose
significant problems for achieving integration. Many healthcare
organizations continue to rely on decades-old software and
infrastructure, and technical hurdles will be harder to overcome where
outdated formats, protocols, and interfaces remain or where there is
limited vendor support for modern interfaces. Such historic
incompatibilities are common in existing hardware and software; they
can also pose hurdles around data governance, creating challenges
around synchronizing information sharing relating to patient privacy
issues. Often, addressing such challenges requires costly system
upgrades or the construction of custom interfaces that can bridge the
divide between legacy and modern technologies.
System standardization and system compatibility are the second set of
barriers. Different formats of storage and different standards can
lead to ineffective and inefficient interoperability. In database
storage, health information is not often stored in the same way, and
the same code language is used. Thus, different forms of EHRs can use
different coding for medical terminologies. For example, in the Kenya
EHR system, the standard is LOINC (Logical Observation Identifiers
Names and Codes); however, in Epic EHR, the standard is SNOMED-CT
(Systematised Nomenclature of Medicine – Clinical Terms). This causes
a lot of time-consuming, expensive, and inconvenient teething
processes to realign similar information. To overcome these issues,
the healthcare facility will not only have to use standardized
protocols such as HL7 and FHIR (Fast Healthcare Interoperability
Resources) but will have to collaborate and influence vendors to
ensure that their systems match and are capable of exchanging
information using standardized formats. These barriers undermine the
promise of interoperability to enhance healthcare delivery.
Implementing user-centered design tends to make healthcare software
effective and easy to use, which should go hand-in-hand. When software
has optimal UX characteristics, it’s more likely that care
professionals, as the end-users, will easily find the information they
need to make appropriate decisions. Good usability – where software is
designed for the end-user and adapts to their workflow – speeds up the
care journey and minimizes the scope for error. That’s not to say that
usability is the single most important factor in making software
accepted and used by clinical and care staff but it’s certainly a
critical part of the puzzle. Good UX minimizes the time and effort –
the ‘cognitive load’ – that people need to spend learning to use a
system. It encourages quicker, accurate completion of tasks by
minimizing the effort that’s required. It also increases user
satisfaction and, ultimately, leads to better care and effective use
of the software.
Among the most important are simplicity, consistency, and clarity.
Simple refers to presenting content in the simplest way possible, that
is, by not burdening users with irrelevant information or details.
Consistent means providing the same kind of functions and elements in
the same manner everywhere in the software, giving the users a sense
of predictability in terms of how they can interact with the system
and reducing their cognitive workload at the same time. Finally,
clarity concentrates on how easy it is to navigate, i.e., it’s all
about using labels, icons, or instructions that are readily
understandable. If developers focus on these principles, they can lay
down the means to support the user in his task and enhance his
experience with the software and the world.
Among the various challenges of UX design when it comes to healthcare
software is the middle path of both functionality and ease of use.
Unlike software for other fields where functionality is less
time-critical and not life-affecting, the major functionalities
embedded in healthcare software are more human and life-critical. For
example, the three major categories of healthcare software include EHR
(Electronic Health Records), telemedicine tools, and billing software.
The functionality of each category is very different and unique even
to users. An EHR, for example, stores a continuous history of patient
medical records, including medical tests, treatments, and medications,
as well as describing issues with health and lifestyle, prompting
patients to take action. A telemedicine tool could potentially involve
remote surgery as well as digital remote appointments.
Billing software, however, could be as simple as recording,
processing, and storing insurance and patient payment information. All
of these features need to be accessible and available, however,
without alienating users bearing with the UI experience. In other
words, designers have to deal with the challenge of squeezing a wide
variety of time-critical and life-affecting upper-level functional
components while providing clean and uncluttered interfaces that will
not affect the users’ workflow of getting things done.
The ability to scale is crucial because it relates to how well the
software copes when usage levels increase, and it needs to grow with
the organization. For example, you might have an additional rush of
patients or increase the data that needs to be processed in a
different period of the year. You might also need to add an extra
service or module that increases the load on the code. Software with
good scalability enables the demand to increase without having to
rewrite the code for the applications. As an example, if someone
builds a new practice, the company’s software should be capable of
accommodating this increased number of users. Scalability means
foreseeing where to locate the extra capacity when you need it and
designing corresponding functionality to enable the layout to expand
without alteration. It could be that we need different configurations
of servers to hold different parts of the data, in which case we
should explore modular architectures and designs that facilitate
extensibility and interoperability.
Looking ahead from the start, a system with built-in scalability can
be adapted to the changing needs of an organization, including new
technologies and processes or process increased volumes of data, all
without having to rip and replace what already exists when the
organization grows. The early preparation for possible future demands
also helps to prevent bottlenecks in performance and goal-oriented
disruptions while enabling an organization to prosper. With this in
mind, software developers who design their systems with the
possibility of scaling, of an increase in demand, ahead of time are
being farsighted and allowing a system to remain strong and responsive
as demands increase over time.
Performance is a major concern because there are usually many users of
a healthcare software program at any given time, whether they be
clinicians, administrative personnel, or patients who are accessing
data via patient portals. Performance issues that developers should
anticipate include latency in response time to queries, in addition to
overall performance downtime. Both of these problems can significantly
impact the quality of care delivered to patients, in addition to
overall operational efficiency in any healthcare setting.
Latency and reliability are important parts of performance management.
Latency is the speed at which users expect their requests to be
completed or for applications to respond within certain timeframes
that are integral to an application’s basic functionality. Latency can
be caused by bottlenecks due to the amount of work needed to process
data or transport it between different systems. Meanwhile, reliability
is about having redundant systems and failover mechanisms that prevent
applications from crashing. Redundancy provides application backups by
replicating information and providing redundant systems. The
development of effective health IT requires systematic performance
testing and monitoring. Through this process, developers can pinpoint
bottlenecks caused by faulty software design, slow networks, and the
amount of data an app is trying to manage.
Several trends in healthcare software development are reshaping the
future of the industry. The first and foremost is the use of
artificial intelligence, which is revolutionizing healthcare
applications. The use of AI, such as machine learning and natural
language processing, is increasing the accuracy of diagnosis,
improving personalized care and reducing administrative burden. The
second major trend is the rise of telemedicine, where digital
platforms are being used to engage patients in understanding their
medical conditions and to engage with healthcare providers to
facilitate care remotely. Not only are these advancements improving
the quality of care, but they also address clinical factors like
patient engagement and cost management.
Other recent trends include the increase of AI and telemedicine, as
well as wearable health technology and the use of blockchain to
securely share medical data. Wearable devices are becoming widely
accessible to provide continuous health monitoring, and the data
gathered are being leveraged to enhance real-time patient care through
the use of AI and integration into healthcare systems. Blockchain can
potentially address current issues of data security and
interoperability by providing a fairer, more transparent, and secure
way for patients to manage and secure their health records and be
associated with transactions. All of these trends are expected to
bring significant change to the way healthcare software is developed
and used.
There should also be measures taken to anticipate the velocity of
interventions required and futureproof it through continuous research
and development into new technologies that can enhance the
functionality and experience of the software as well as through skills
development within the organization. This can be achieved through
offering regular, meaningful learning and development programs and
opportunities for employees to engage in proposals that can result in
continuous personal development. Another strategy could be building
strategic alliances with specific technology partners and industry
experts who can illuminate the gaps in capabilities and help access
the latest innovations.
Ongoing evolution involves creating a system that is current,
incorporating changes in regulatory requirements, new industry best
practices, and shifting expectations of patients, and that is
sufficiently flexible to update or integrate with future technical
evolution to implement innovation and keep the organization current,
innovative and able to capture the future space. Changing end-user
expectations also presents ongoing challenges that organizations must
adapt to.
With careful consideration from all stakeholders and the application of best practices, challenges in healthcare software development can be overcome to produce healthcare software that will support efficient patient care while also helping healthcare organizations maintain high standards of care. The healthcare sector is changing rapidly and will continue evolving along rapid and uncertain lines. For healthcare software developers, careful usage in these four areas from the start will ensure that the sector has the support it needs to maintain the high quality and efficient care it strives to deliver.