Yossi Cohen, Physician Executive at InterSystems writes for MTI about the challenge of integrating and effectively using data as NHS trusts strive to digitally transform operations.
In today’s rapidly evolving healthcare landscape, the integration and effective use of data stands as a pivotal challenge. Integrated Care Systems and NHS trusts are striving to digitally transform their operations, and to integrate new digital technologies to enhance patient care, for which we are seeing significant progress happening. The UK’s digital health market is estimated to reach around 3.85 billion GBP in revenue this year, and is projected to reach 5.166 billion GBP by 2028, exhibiting an annual growth rate of 7.61% in the next two year period. This reflects the extent InterSystems of industry involvement and the high number of participating suppliers.
However, many healthcare regions and providers are still hampered by legacy systems, siloed departments, and a lack of interoperability, leading to disparate, non-standardised, and duplicated data. This is in turn, hindering the process of collating data in one Shared Care Record (SCR), and easily leveraging it to support real-time, accurate decision-making. This fragmentation also makes it difficult to derive value from the data, hampering the efficiency of healthcare services and affecting the quality of patient care.
Data quality challenges play an important role when amalgamating data from different sources into a single comprehensive SCR. Often, similar but not identical data sets reside in different sources and once collected into a single record, discrepancies and duplications are not uncommon. While most healthcare IT projects aim to have a well detailed and complete specification at their inception, it is often the case that the exact nature and quality of the data coming from the various data sources is unknown. This makes the drafting of complete specification at inception challenging.
In addition, different data types may require different processing methodologies when integrated into a meaningful and clinically safe SCR. Some clinical data types, for example allergy information, are more clinically sensitive than others when it comes to removing discrepancies and duplicates and unless fully structured, are often concatenated ‘as is’. Some other data types present more complex issues where different content of the same data type requires different considerations. For example, for past medical history, while having ‘Diabetes type 2’ coming from multiple sources can be consolidated into a single Diabetes diagnosis, multiple instances of ‘Myocardial Infarction’ may mean that a patient suffered more than one Myocardial Infarction in the past, a clinically important observation, and therefore cannot be consolidated into a single entry easily.
Because of the high number of different clinical systems available on the market, there is often only a small or partial overlap between the clinical systems used in one healthcare region to another. That means that while the nature of challenges faced by healthcare regions regarding data may be the same, the implementation of solutions being used to address these challenges would be different. Even when the same clinical systems are used at different regions, often the actual use and user data captured is different, shaped by the nature of users, local governance, and organisational culture.
Overcoming data challenges
A clinically governed approach is needed to address these challenges. While it may seem like a cliché to assert that healthcare IT projects are not technology projects, but rather transformation projects that should be clinically led and governed – it really is true. While an important part is technical interoperability between different systems, this is just a prerequisite to addressing the clinical opportunities that such interoperability presents and tackling the data challenges that were articulated previously.
Therefore, appropriate clinical leadership and governance are essential for successful healthcare IT projects. In healthcare IT projects where interoperability plays key parts, such as Shared Care Records, appropriate clinical leadership should include stakeholders from all the facilities participating in the healthcare data exchange. These could be facilities contributing data, consuming data, or both. While they all have a vested interest in the outcomes of such projects, often different parties have different conflicting requirements with regards to the processing and presentation of information and a consensus should be reached.
In addition, well-represented clinical leadership is key to achieving clinically safe user-facing solutions. Interoperability, while presenting opportunities for healthcare institutions, also opens the door to clinical safety challenges. Data quality, as previously explained, can present safety considerations and should be carefully managed. Other factors such as the availability of data from systems interoperating in real time should also be considered. The bigger the number of systems that participate in exchanging data, the higher the likelihood that a participating system will be ‘down’ at the time of query, thus not sharing the data that it normally shares. Frontline care providers develop an assumption over time that what they see is a complete representation of the patient’s record and ‘no data’ means by exclusion that a patient does not have for example a particular disease, or a diagnostic study or a prescribed medication. However, this is not necessarily the case and has to be managed by both user training and an appropriate user interface.
Another notable example for the role of clinical leadership and governance to achieve clinical benefits safely is the management of the organisational changes required to take advantage of interoperability. Interoperability is an enabler that can support new workflows, but without corresponding organisational changes, these new workflows will either not deliver or present new risks. For example, interoperability could be key for notifying a team of an event with clinical importance such as that a patient with heart failure was just admitted to hospital for the second time in four weeks after a recent admission in a nearby hospital. This is a clinically important notification but has to be followed up appropriately by the relevant team. This team needs to be aware of such notifications, have a clear interface to accept them, be properly resourced to address them, and make sure they are properly followed up. While this level of organisational change is easier to implement in a single medical facility, it is more difficult to achieve when involving different facilities that collaborate over the care of a patient and where interoperability is the underlying fabric.
The path forward
The path forward naturally involves a shift in mindset from individual organisational needs to a broader perspective that considers caring for a patient cross-organisationally, including primary care, secondary care, social care, and more. This path forward is an incremental one. No health economy can achieve this change overnight. Instead, there is a natural process of expanding data sharing and interoperability over time to include more systems and more organisations, while in parallel developing new workflows that take advantage of new functionality.
As more and more data is being shared, measuring the benefits over time is key in order to encourage continued investment in such endeavours and prioritise further activities accordingly. Benefits realisation could be at any level, starting from measuring if the data being shared is being consumed by front-line care providers, up to the measurement of patient outcomes such as readmissions, medication compliance, morbidity, mortality, and so on.
Benefits realisation should always be compared with a reference point and the best reference point is the performance before any changes were introduced. Often, healthcare IT projects start with impetus and considerable energy is put into implementing key components of the solution while limited attention is given to measuring any benchmarks for future outcomes. Having a baseline to compare to pays dividends when such projects, that are complex in nature, are periodically reviewed to assess success and the delivery of the benefits required.
