Hospitals and pharma companies have traditionally looked at themselves as transactional partners, but that relationship is changing dramatically. Hospitals have improved their leverage as partners, realizing the value locked within patient data. However, during the Corona pandemic they experienced that when patients no longer can ‘reach’ the hospital – that value needs to be captured otherwise. That’s where they have forever transcended the role of simple purchaser, and became a fertile ground for novel types of collaboration.
In 2017 already, the Association of the British Pharmaceutical Industry, signed a ‘Memorandum of Understanding’ (MoU) between the pharmaceutical industry and Greater Manchester. It was the first of its kind, providing a unique foundation for joint working to deliver improved healthcare quickly and support the realisation of the city-regions vision – which is to become the safest and most effective place to receive medicines in the world and cement its position as a world-leading hub for life sciences research and development.
The impact of that partnership has been immediate. It has spurred a 4.4% increase in commercial studies; more effective approaches to collaborative working management and use of digital products and case reviews. The ambitious work programme has ground-breaking projects including becoming the first UK city to eliminate Hepatitis C (by 2025), optimising medication for 67.000 COPD patients and securing rebates in mental health drugs through outcomes-based pricing. A few collaboration examples with pharma giant Novartis for instance are summarised here.
Most importantly, the MoU is recalibrating the National Health Services’ (NHS) relationship with the pharmaceutical industry, moving it away from traditional customer-supplier to one of collaboration with risk-sharing and co-production of solutions in partnership with their competitors and the NHS. At the same time, it is helping shift the mindset of hospital staff and patients towards viewing industry as a trustworthy partner that is part of the solution to creating a sustainable world-class health system.
We believe this is the proper foundation for a new breed of hospital – pharma collaborations.
As the Corona vaccine roll-out has strengthened that trust even further, the last 6 months we have seen an increasing amount of pharma – hospital partnerships applying digital technologies for the better.
April 2021, AstraZeneca for instance, entered into a collaboration agreement with Massachusetts General Hospital (MGH) to pilot a new digital health platform, AMAZE, including a patient app and clinician dashboard, to help improve outcomes for patients with asthma and heart failure. Through remote monitoring, AMAZE, developed by digital health platform builder Bright Insight, identifies at-risk patients and delivers insights to the clinical care team at the point of care to improve the management of complex patient populations. Preliminary data, presented late August, showed the research and commercial partnership more than doubled adherence rates and cut readmission rates down to a third of the industry average.
September 2021, Bayer introduced its Alleye Home Monitoring Sponsorship Programme to the Egyptian healthcare sector. The global pharma giant is reportedly working with some of the country’s top-rated eye hospitals to “support Egypt’s ophthalmologists in the new era” of virtual care for patients with either Diabetic Macular Edema, a complication of diabetic retinopathy, or age-related macular degeneration. A good reason to think so is that the Middle East has a rising population of diabetes in general, according to numbers from the National Diabetes Associations.
Part of the Zurich-based Oculocare Medical, Alleye is a mobile medical software application that detects, characterises, and/or tracks progression of visual distortion in patients with diabetic retinopathy and/or AMD. Diabetic retinopathy is a complication of diabetes, caused by high blood sugar levels damaging the retina. Patients are able to use the app at home to perform self-tests, with information shared with their physicians or healthcare provider.
While both examples above (and many more we have analysed recently) still deal with managing patients, we now envision a novel service at tomorrow’s hospital.
On our way to future disease prevention, a logical intermediate is faster, more accurate disease prediction and identification. We see big opportunities here for novel hospital services benefitting pharma, and thus the patient. Indeed if your hospital provides me a novel screening service, which upon disease detection presents me to your specialists, the hospital wins. If a patient gets diagnosed faster, gets aware of his disease, the pharma comes into the picture sooner, hence the patient wins twice. The following example should clarify.
Multiple Sclerosis (MS) is a neurological condition which widely affects people 50-60 years of age. While clinical presentations of MS are highly heterogeneous, mobility limitations are one of the most frequent symptoms. A recent study examined a machine learning (ML) framework for identifying MS through spatiotemporal and kinetic gait features. Gait data from 20 persons with MS and 20 age, weight, height, and gender-matched healthy older adults were obtained during self-paced walking on an instrumented treadmill. Using two strategies to normalize data and minimize dependence on subject demographics, it was shown the integration of gait data and ML may provide a viable patient-centric approach to aid clinicians in monitoring MS.
The results of this study inspired us to explore and contemplate other disease prediction strategies and ways to monitor disease progression in seemingly healthy citizens. More so, it constituted the basis for an exercise questioning which emerging, sometimes even simple technologies we could envision make up the equivalent of the car assembly line inspection of a future hospital. A dedicated novel service to detect disease early.
The latter would be good to increase patient awareness on one hand, but also to broaden the spectrum of available medications to prevent disease progression rather than to cure disease. Indeed, the more disease progresses, the harder it becomes to treat. Next to MS, predicting diseases like Parkinsons, osteoporosis and fall sensitivity detection come to mind. Our proprietary databases of digital health tools provide a good idea which technology would allow us to do so, expanding easily to convenient analysis of sleep and eyesight today and to the use of next generation cardiovascular, cancer and inflammatory disease screening tools tomorrow. For the latter, also see our dedicated post here.
And one more thing. For novel types of collaboration, don’t look to far. Be inspired by what you see around. Look to Lego for instance, creating win-win collaborations via deals with game designers (Minecraft), filmstudios (Bond and beyond), car manufacturers (Aston Martin). Be aware we developed a Delight Thinking methodology specifically to help you apply this way of learning from others for your specific business as well.
