Today, 21.8% of people choose to self-diagnose their symptoms online due to the lack of immediate access to a medical appointment, and this may lead to patients becoming misinformed about their health.
The offer today
Patient expectations are rising rapidly due to experiences of increasingly simplified, direct and personalised services in other consumer industries. The experience of customers and staff in industries such as banking, aviation and retail have vastly improved thanks to the digital revolution. A similar sea change in the impact of technology has not yet been felt in healthcare - indeed, as reported earlier this year, patients reported the lowest satisfaction of General Practice services since 19831.
The NHS continues to be the biggest consumer of fax machines in the world and, famously, adoption of innovation from initial product development takes an average of 17 years2. There are, however, a growing number of indicators that change is happening: the digital health sector is now the fourth largest employer within life sciences in the UK; the number of digital health companies has doubled within the last ten years; and, widespread provision of digital access to NHS care over the next ten years is a core commitment of the NHS Long Term Plan published in January 2019. If the power of digital is finally harnessed, what changes to the healthcare system will we see and how will this impact patient experience?
Patient journey of the future
Healthcare today is largely reactionary, with patients seeking medical attention only once they present with symptoms. Patient experience is often poor: it is no longer exceptional for a three-week wait for a GP appointment for unplanned healthcare needs, and the subsequent pathway regularly exceeds the 18-week target. Delays in treatment can lead to further manifestations and complications of disease, with detrimental effects on the patient and the health economy. Furthermore, after treatment, an absence of active tracking of the patient’s condition can lead to subsequent avoidable diseases.
Prevention – one of the keystones of the NHS Long Term Plan – focusses on cutting smoking and obesity to prevent Type II diabetes, cancer and heart disease. The necessary consequence of this policy is a firm emphasis on empowering patients with the information and tools to take greater responsibility for their own health. The ‘Internet of Medical Things’ (IoMT) – the interconnection of medical devices that send and receive data – will be an important enabler for prevention, facilitating 24-hour connectivity between a patient’s monitoring device and the system by which a clinician can subsequently view their health data. Not only can this enable the continuous tracking of a patient’s health metrics, but also a new door to clinical research of a disease can be opened3.
An IoMT device will go beyond tracking heart rate and step-count, and potentially encompass continuous tracking of glucose levels, hydration, blood pressure, blood levels and nutrition intake. Wearables will be embedded into clothing, skin patches, and electronic skins, and linked with smartphones or smartwatches, collating multi-faceted health data to build a ‘digital twin’4. Diseases could be detected earlier, and patients and carers alike alerted to intervene.
Next, symptom checkers will use machine learning algorithms to extrapolate data from the IoMT devices to effectively triage patients. Today, 21.8% of people choose to self-diagnose their symptoms online due to the lack of immediate access to a medical appointment, and this may lead to patients becoming misinformed about their health5. The next ten years could see AI symptom checkers providing clinically safe advice to the patient, automatically creating a self-care treatment plan and identifying the medications required for easy-to-treat diseases. Patients will be able to do this from the comfort of their home, reducing pressure on GP surgeries so that there is more time for sicker patients to be seen, in a more timely manner.
Before reaching a clinician, full body scanners could be used to help diagnose health problems. By bringing together the patient’s IoMT data with diagnostic tests performed by the scanner, a full picture of their health state could be stitched together. This will be another point of contact for effective triage, where only patients requiring further examination or require specialist intervention will be seen by a clinician6. For those with easy-to-treat conditions, the diagnostic hubs will also be able to dispense a personalised medicine using 3D printing, bespoke to the patient’s genetic make-up – providing the greatest efficacy and least side-effects.
Secondary care will be more efficient
Hospitals will become much more efficient, with patients arriving at the right ‘front door’ and leaving more quickly, thereby reducing pressure on hospital beds and improving A&E and referral to treatment (RTT) times. Machine learning will be driving these efficiencies by using predictive analytics to model public health issues, hospital operations and patient flow. These models will have the capability to respond to external factors such as seasonality, patient demographics or workforce rostering7. Patients requiring surgery will be automatically given a date at the point of consultation, where a digital platform automatically calculates the date based on the RTT waitlist, the urgency of the operation and the surgeon specific times for the procedure.
By 2053, it is estimated that robots will be carrying out routine surgical procedures, freeing up hospital staff and surgeons to perform more complex surgery8. Robotic assisted surgery is already carried out within the NHS, and new innovations are ready to disrupt the market. CMR Surgical has developed ‘Versius’ in Cambridge, which aims to offer a greater variety of procedures, to minimise infection rates, surgical complications and to reduce length of stay after surgery. It is still unclear whether there is a health-economic benefit of robotic assisted surgery, once the upfront and ongoing costs are compared to savings from reduced hospital stay length9, but there are clear benefits for patients.
The challenge presents great opportunity
We recognise that several challenges must be overcome to facilitate widespread adoption of technology – the usability of any product or service must: satisfy the patient’s clinical needs; demonstrate time, clinical or financial benefit to the workforce; yield a strong economic benefit to commissioners; and. maintain robust safety standards to regulators10. But the building blocks are in place to shape a healthcare system which enable the patient of tomorrow to benefit greatly from technology adoption. Data has the potential to provide new ways for the NHS to learn, alongside artificial intelligence, which is providing new analytical capacity for diagnosing patients, effective triage and logistics. This is the right time for the NHS to build dynamic partnerships across commissioners, providers, technologists and external specialists to ensure it converts policy into value, provides a digital first offer for patients and ultimately drives a positive change in patient experience.
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1. Public expectations of the NHS. (2018, February 12).
2. Morris ZS, Wooding S, Grant J. The answer is 17 years, what is the question: understanding time lags in translational research. J R Soc Med. 2011;104(12):510–520.
3. Haghi M, Thurow K, Stoll R. Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices. Health Inform Res. 2017;23(1):4–15.
4. Signals Analytics. The Future of Wearable Technology.
5. Push Doctor. Digital Health Report.
6. Innovate UK. (2017, July 17). Predictions - Future of Healthcare.
7. KenSci. (n.d.). Operational Analytics and Risk Prediction Platform for Healthcare.
8. Grace, K., Salvatier, J., Dafoe, A., Zhang, B., & Evans, O. (2018). When Will AI Exceed Human Performance? Evidence from AI Experts. Journal of Artificial Intelligence Research, 62, 729-754.
9. Royal College of Surgeons. (n.d.). Surgery set to be transformed for millions of patients by a new wave of technologies.
10. Klonoff, D. C., & Kerr, D. (2017). Overcoming Barriers to Adoption of Digital Health Tools for Diabetes. Journal of diabetes science and technology, 12(1), 3–6. doi:10.1177/1932296817732459
See our previous piece, the Citizen of Tomorrow or return to the Customer of Tomorrow page.
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