What qualifies as a good outcome in healthcare? For health systems and professionals, measuring things like lab values, physical performance, mortality rates, length of stay, or readmissions is key and routine practice. But if you are the patient, facing an acute illness or chronic condition, what matters the most? Am I going to live? Are things going to get back to normal? What’s my life going to look like? What patients care about is managing symptoms, carrying on with daily activities, keeping up with family or keeping mentally healthy. Such quality of life matters that come directly from the patient are called patient-reported outcomes (PROs). PROs often cause patients to seek out help. Health systems that aim to provide better care should be able to systematically monitor and respond to PROs. Monitoring brings in the concept of PRO measures (PROMs). These are self-reported questionnaires developed with direct patient input and enabling direct patient output (PRO information) for health professionals to act upon. Monitoring and response introduce technology and the concept of electronic PRO (e-PRO) systems or ‘e-PROactive’ systems.

The concept of ‘proactivity’ is fundamental in this area. The e-PRO system systematically prompts the patient to report (hourly, daily, weekly) on his/her health status using system-embedded PROMs and irrespective of health status. System-embedded algorithms and thresholds (perhaps complemented by automated linkage to sensor data from wearable devices) immediately identify symptoms/signs of critical severity/intensity, flagging to the clinical team patients that require immediate attention and management. This proactive approach doesn’t require the patient to self-diagnose or rely on own volition to seek help, while it allows for continuous remote monitoring at home without an absolute need for, say, a GP appointment or visit to A&E. Where symptoms don’t require clinical intervention, the e-PRO system can generate symptom-specific advice for patients and enable supported self-management.

It’s true that use of PROMs and implementation of ePRO systems has expanded across areas and levels of practice in the last decade. Particularly, my area of practice/research (cancer care) has been the field of extensive research. Both theoretically and empirically, ePRO systems are linked to better outcomes: they can reduce preventable admissions, reduce resource use and costs to treat severe symptoms, increase the odds for early treatment and even increase survival. e-PRO systems can even generate large sets of data to enable tailored predictions of a patient’s risk to develop certain symptoms, side-effects or deficits based on his/her unique characteristics. Such information can play a role in patient and clinical decision-making. For instance, might decisions differ if a patient’s chance to develop long-term urinary incontinence post-radiotherapy treatment for prostate cancer were 80% v. 20%?

What is key is that e-PRO systems are consistently linked to better patient-reported experience (PREs) of care; patients do like e-PRO systems as a means of better care provision. Conversely, gains in PROs and clinical outcomes have been less consistent across studies and effect sizes variable. One key aspect when developing e-PRO systems is that there’s no ‘one size fits all’. Although health systems/organisations/professionals have universal preferences for the provision of adequate care, they also have unique infrastructure requirements or face unique resource challenges that demand e-PRO systems to be reconfigured, modified or tweaked (at times extensively) to enable successful implementation. Such variability makes evidence difficult to converge when outcome effectiveness across studies/health systems/areas of practice/countries is considered.

Researchers around the world work hard to develop more rigorous, flexible and user-oriented e-PRO solutions with direct patient and health professional involvement. It is the buy-in and implementation where things might get stuck along this process. There is no doubt that more research and additional dedicated funding is required. More funding can generate the evidence to establish what intervention mechanism seems most suitable for e-PRO systems. It can promote comparative effectiveness research to establish outcome-effectiveness and cost-effectiveness. It can also help to identify solutions to enable integration within the electronic health record; information governance concerns and infrastructure still have an adverse impact. Funding should not be just for extra research; it is also needed to support implementation where evidence is clearly promising.

Tackling staff shortages can lead to formation of dedicated clinical teams to run e-PRO enabled clinics. Imagine such multidisciplinary or nurse-led clinics running in primary and secondary care. What might be the costs involved to provide such proactive services? In the long run, perhaps less than having to treat people presenting in critical condition that could have been prevented or detected at an earlier stage. Making funds available to recruit and train more staff and prepare the health system for e-PRO implementation and integration with other systems (e.g. remote GP consultation) would be a decisive step ahead. Perhaps, a particularly important one to help deal with time restrictions and information overload that overworked staff (across the world and more specifically in the NHS) face when e-PRO systems are considered.

And here’s some more food for thought. In the current unprecedented situation (and perhaps in future healthcare crises), could remote e-PROactive monitoring of early signs/symptoms of infection or of recovery/relapse be a solution? Where patients are already facing delays in cancer diagnosis and/or treatment due to Covid-19, could e-PRO systems offer an option for less work-intensive, yet systematic, monitoring to decipher and streamline cases that require immediate attention/treatment and those that can be managed electively at post-lockdown? In a parallel universe, you say. But why not in this one too?

Further reading:
• Basch E et al. (2017), JAMA, 318(2):197–198.
• BBC Health (2020), Thousands missing out on cancer diagnosis, BBC.
• Franklin PD et al. (2017), eGEMs, 5(1):17.
• Haywood KL et al. (2016), Patient, 9:495–498.
• Holmes MM et al. (2019), Patient Related Outcome Measures, 10:385–394.
• Kotronoulas, G. et al. (2014), Journal of Clinical Oncology, 32(14):1480–1501.
• Maguire, R. et al. (2017), BMJ Open, 7(5).
• Rivera, S. C. et al. (2019), Health and Quality of Life Outcomes, 17: 156.
• Schwartzberg, L. (2016), ASCO Educational Book, 35:e89–e96.
• Warrington, L. et al. (2019), Journal of Medical Internet Research, 21(1):e10875.
• Zbrozek, A. et al. (2013), Value in Health, 16(4):480–489.

Dr Grigorios Kotronoulas, Lecturer, School of Medicine, Dentistry and Nursing, University of Glasgow (Twitter: @nursGK704)