How data can drive true efficiency in modern healthcare

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The earliest known medical records are Paleolithic rock paintings of rudimentary surgical procedures dating back 4,500 years. But only in the 19th century began to keep records of patients. For the next 100 years, ever-growing mountains of paper tracked every investigation, observation, diagnosis, and intervention.

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In the middle of the 20th century, the first medical information systems emerged, mainly to manage appointments, beds, and many administrative aspects of hospital care, while clinicians stuck to pen and paper and often illegible handwriting. By the 1960s, the first clinical computer systems were in place and their adoption increased over the next 60 years. In parallel, coding systems arose and evolved to make the recorded data more accurate and consistent than the quirks of natural language.

Today, we have reached the point where Electronic Patient Records (EPRs) have become widespread. The most recent government initiatives for the NHS in England aim to increase the digital maturity of health care providers by providing investment support to achieve a level of maturity that will significantly reduce reliance on paper records and supporting the exchange of records between care providers. within the regions. Just this month, the inclusion of NHS Digital in NHS England was accelerated, representing the commitment to put data, digital and technology at the heart of the transformation of the NHS.

Business Reporter: How Data Can Power Modern Healthcare
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Perhaps for the first time in history, differences in data standards and technologies no longer stand in the way of making data available to support operational decision-making, research and service development.

The gruesome picture of “critical incident” claims, ever-increasing waiting times in emergency rooms and the availability of a workforce are exacerbating the pressure faced by health services across the country. Health care leaders face the near-impossible task of doing more with less year after year. The role of data in addressing these challenges is recognized in public policy (Data Saves Lives, June 2022), which aims to further improve data availability and access.

However, data accessibility has not yet fully translated into data use. Despite the drive to build data lakes and use big data analytics to support population health management, there are still few real examples and even fewer practical benefits. Very often, these initiatives come from technical and data scientists taking “field of dreams– create it and they will come – rather than a clinical or operational approach to solving known specific problems. Therefore, the impact on clinical pathways remains limited.

Critically, we need to plan care models that support behavioral health and focus on preventive interventions that improve health and reduce the need for emergency care.

The use of fitness monitoring devices is touted as one way to improve public health. But the sheer amount of data can be overwhelming, and the self-selected cohort that currently uses these devices tends to be better and healthier than average. However, the use of remote monitoring for patients with chronic conditions can provide much more patient-centered care and provide earlier warning of worsening conditions while leaving patients at home. But it also poses the challenge of ensuring that symptoms are not missed and care is personalized. Not every trial will prove useful, but the availability of data makes it possible and worthwhile.

Likewise, digital twins are being offered that will revolutionize the management experience. First introduced to production 20 years ago, the digital twin combines the collection of multiple data points with process simulation to provide real-time insight into operational implementation. However, health care is much more difficult. Each patient is unique, and the impact of emergency hospital admissions and critical reliance on a highly skilled workforce make understanding the implications of process change more challenging, but also more rewarding. This can be dealt with by simplifying the model, by choosing one clinical path, or by applying more complex mathematical methods drawn from probability theory. Both approaches are likely to become apparent in the coming years.

On the other hand, clinical trials continue to collect and use much smaller sets of data to test specific treatments using protocols that have been developed and fine-tuned over the past 100 years. Very few studies, especially the recent Covid RECOVERY study, have combined the discipline of clinical trial with large data cohorts. So far, over 48,182 participants from all over the world have taken part in the trial. This points the way to the future, but is still the exception, not the rule.

Access to large longitudinal datasets supports real-world research. The ability to analyze the medical records of millions of patients, understand the impact of demographic factors, and measure the outcome of different treatment regimens can provide new insights far beyond the capabilities of individual clinical trials. The ZOE Covid Study (the world’s largest ongoing Covid-19 study with more than 4.5 million participants worldwide) has shown how data from mass population sources can provide valuable clinical results. The ability to combine patient-reported outcomes with clinical records at scale would provide a true measure of the impact of therapy on patient experience.

We need to find innovative ways to use existing data to revolutionize healthcare delivery models and improve efficiency. The challenge now for healthcare leaders is to determine how the data will be used to benefit patients and their doctors.

Nautilus Consulting is a leading digital health consulting company specializing in data streaming technologies used in operational, clinical and research functions across the NHS.

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Originally posted on business reporter

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