Category Archives: Policy

Healthcare Cognology: autonomous agency for patient empowerment and system reform

Healthcare systems have been slowly evolving toward a model of care delivery that seeks to leave behind the traditional medical model, based on fighting diseases – sometimes called the lesion-theory of medicine – and which has driven health care thinking since the 1800s.

The direction of travel is toward the health ecology model conceiving healthcare as about helping people live their lives well, seeing ill-health and disease within an ecology comprising choices they make, the context in which they lead their lives and importantly, on the central role of the individual within that ecology to decide how to organise healthcare to help them lead this life. In that respect, the ecological model is more in tune with the real, complex, nature of the world with the various parts working together more in a self-organising manner to achieve desired results. This contrasts with the never-ending top-down plans of state run systems, over harnessing the forces in the society to drive quality and performance improvement of outcomes.

Self-care has been the main policy response to the realisation of this complexity and we have examples such as the expert patient, patient activation, patient reported outcome measurement, disease or care management programmes, managed care, and health promotion and lifestyle programmes.

At present, many health systems and policymakers are focused on chronic ill-health or long-term conditions, which entail continuing healthcare requirements perhaps over the lifetime of individuals and requiring varying degrees of support and perceived unsustainable funding needs. Many long-term conditions arise from lifestyle choices in part and that explains why there has been a focus on engaging the patient in the care process, to ensure that they are inclined to make the necessary choices to avoid further exacerbations in their conditions, or indeed to avoid these conditions in the first place; another goal of self-care is to achieve a policy driven cost-shift to the patient user, to exploit financial co-payments, for instance, to alter behaviour, in the spirit of liberal paternalism.

The California Healthcare Foundation has stated [www.chcf.org/topics/health-it]: “information technology is still fairly new and untested in health care, making experimentation, analysis and evaluation critically important”.

We know technology helps to enable not just efficiencies and effectiveness, but also the greater personalisation of services – consumerisation. The impact of technology, therefore, includes, but is not limited to:

  1. breaking down (or disintermediating) processes to remove steps that do not add value to the end-user experience, or which have no useful role to play, despite being seen as current good practice by professionals; this can create novel service integration
  2. shifting skills toward customer-facing staff (e.g. consider how different banking has become)
  3. widening public access to hitherto restricted health information to patients, including information on clinical performance. In some cases, this has been mandated (such as public information on hospital performance) or has evolved in response to customer interest (such as health websites providing information and advice on health conditions)
  4. enabling organisations to create new ways to engage with the consumer or end-user more effectively in improving products and services than the traditional customer/supplier relationship.

A particular impact is relevant in healthcare, namely, moving knowledge across the boundaries of regulated professions (e.g. to imaging technologists from radiologists, from doctors to nurses).

Healthcare is highly controlled and the application and use of professional knowledge legally regulated. The effect of this has been to compartmentalise knowledge and skills within a broad hierarchy, with the doctor at the top, in effect, as the default health professional who supervises and validates the application of knowledge and skills by other professions. This, of course, is changing, partly as a response to the sheer complexity of healthcare and the levels of knowledge and skill involved, but also through new ways of working, in teams, and across organisational boundaries, with skilled nursing care facilities, polyclinics, etc. The patient, though, has not been an immediate beneficiary of this.

As knowledge has migrated away from people into devices, we have seen the invention of patient-use devices which in the past have required sophisticated testing and professional knowledge; an obvious example is the pregnancy testing kit, and many mice and rabbits are no doubt relieved at its invention.

The impact of embedding knowledge in devices in healthcare, and thereby the potential impact on the internet of things within hospitals and for patients unbundles knowledge cartels and redistributes it.

Putting knowledge into people means training them, and it can either shift knowledge to other professionals, such as is found in interventional radiotherapy (imaged-guided surgery), whereby surgeons interpret imaging results in theatre, replacing a separate radiologist. Knowledge can also be given to patients, often by simply enabling them to have access to more knowledge and insight; this has been a key impact of the internet and which has raised many issues around the quality of health information on the internet.

Knowledge can be put into devices, which can be used by patients and consumers, and where the device does what a health professional used to do. This is the artificial intelligence revolution.

Finally, technology can enable knowledge to be put into ‘systems’ to generally interact with people, such as in the home, or hospital, for instance; it is the embodiment of smart devices within systems that offers particular benefits.

The Internet of Things, for want of better terminology, can help achieve greater personalisation of service delivery and move toward such notions as the Smart Hospital and the Smart Home to support the Smart Consumer.

Why do we want this greater personalisation within a healthcare context? Because evidence demonstrates that customising services is effective – patient outcomes are improved, patient experience is positive, and the provider gets better value for money.

Personalisation has the potential to be enabled through autonomous agents acting on behalf of patients, enabling the patient/consumer to drive their preferences and choices, rather than these emerging through professional delegation or proxy interpretation. Is this Alexa or Google’s Assistant on steroids?

A vast array of device technologies are used in healthcare, particularly in hospitals, probably the most complex organisations in our society. A known priority within healthcare is to integrate the vast sea of information produced, whether conclusions by clinicians, activities of patients, the output of devices, or underlying information such as financial performance, inventory, or quality. Progress is slow and mixed.

E-health has largely failed to get substantial traction, either as a mode of service delivery, or commercially, despite being seen as having considerable potential, by enabling better linkages between operational parts of the healthcare system with the patient. This, despite evident progress is still work in progress.

There are many approaches to integrating information across the information value chain, with the electronic health record (EHR) seen as key from a clinical perspective, along with opportunities real-time monitoring of patients outside hospital through sensors, or interacting with patients through video teleconferencing. Most countries are grappling with how to enable patient access to the EHR, with concerns around identity determination, privacy regulations and security being central, but this debate is being carried by the healthcare providers and their regulators that see the EHR as belonging to them, and not something owned and under the control by the patient.

Electronic prescribing, is seen as reducing medical errors, and better correlating patient data with rational prescribing, but the benefits to patients are limited, in the main, to electronic delivery of the prescription to the pharmacy of their choosing, but this is a choice that is already theirs that is not enhanced by e-prescribing itself. The benefits here accrue to reduce processing time, or commercial capture of the prescriptions themselves through co-location of pharmacies and prescribers, which in the end sort of defeats the point from a patient perspective.

Other areas, such as care management programmes using remote monitoring, SMS alerts, etc. but little of this is really new, as they are mainly automating existing activities, and facilitating better communication.

Let’s consider starting in a different place.

I am mindful of underlying clinical requirements in the hospital, such as linking the dispensing of a medicine to a patient (informed through clinical decision-support prescribing systems and documented accordingly) with bed-side capabilities to ensure the right patient gets the right medicine, and linking that in turn back to batch control and inventory control, budgeting and procurement, not to mention links to quality assurance, audit and utilisation review. And should the patient react badly to the medicine, batch control can help identify any problems with the medicine itself, such as expiration date, or even whether it is counterfeit. How are we to design a system that seamlessly makes all this work?

I am starting with the relationship between the patient and the hospital (mindful that perhaps what we mean by hospital will evolve over the next decade for other reasons), a relationship, built on trust, and on service delivery, communication, treatment, and information. Illustratively, a wireless world of healthcare is possible, which respects this.

Autonomous agents and the next stage of evolution of the Internet of Things

“Cognology” is a term coined by myself to describe the evolution toward technologies with embedded intelligence. So what can the internet of things be in this context? I have adopted the operational definition of how the internet of things should work in healthcare from Kosmatos et al 2011:

“… a loosely coupled, decentralized system of smart objects—that is, autonomous physical/digital objects augmented with sensing, processing, acting and network capabilities.”

The implication of operationalising devices within a cognology and fitting this definition is to alter our current notion of the internet of things from a cognitive perspective. That is to say, the ‘thing-ness’ of devices that we perceive to be the interesting development evolves as autonomous agents give functional purpose to these things. This in effect means moving from a view of the internet of things defined as bundles of technological capabilities, and more as a ‘distributed cognitive system’ [Tremblay 2005] defined in its ability to evolve and transform itself in response to changing circumstances, rather than a strict functional hierarchy.

Conversion of the internet of hospital things into the internet of self-care (or what might be thought of as ‘my things’), through autonomous agents bridges the gap between the hospital setting and the personal context (home, school, work, play), in effect by having the autonomous agents ‘repurpose’ the device.

In a wireless world, the individual is the focus of the cognological capabilities provided by smart device technologies. This achieves the additional benefit of shifting the focus away from technologies that can deliver this or that service, to the use of the information and its manipulation to achieve various goals.

I also think it is important to adopt Simon’s technological agnosticism, to ensure we are focused on results, rather than ‘things’ as such.

I think of this shift from technology to cognology as achieved in part through advances such as the potential of the internet of things, with the embedding of functional intelligence in devices transforming them from physical things into cognitive things.

In this respect, the internet of things is a misnomer.

The internet of hospital things

Healthcare technologies should have certain degrees of freedom:

of geography: in terms of home, hospital/clinic, ambulance, workplace, etc. to support location-independent care;

of intelligence: embedded ‘intelligence’ of one sort or another proving a constellation of capabilities, but perhaps most importantly, a predictive and anticipatory capability;

of engagement: seeking out and exchanging at various levels and in various forms with people (doctors, nurses, patients, carers, etc.), with processes (admission, discharge, alerting, quality monitoring, etc.) and with other objects (blood gas monitor, diabetic monitor, cardiac monitor).

I see the Internet of Things as a different approach, which, when coupled to the use of autonomous agents, offers substantial opportunities to recast clinical processes so making the patient central to healthcare. This consumerist approach will render dated many e-health initiatives for example, as well as the current approach to the use of EHRs.

References and Want to Know More?

Autonomous Agents and Multi-Agent Systems for Healthcare, Open Clinical, www.openclinical.org/agents.html#properties

Kosmatos EA, Tselikas ND, Boucouvalas AC, Integrating RFIDs and Smart Objects into a Unified Internet of Things Architecture, Advances in Internet of Things, 2011, 1, 5-12, doi: 10.4236/ait.2011.11002

Lehoux P, The Problem of Health Technology, Routledge, 2006.

Simon LD, NetPolicy.com: public agenda for a digital world, Woodrow Wilson Centre, 2000.

Storni C, Report on the “Reassembling Health Workshop: exploring the role of the internet of things, Journal of Participatory Medicine 2(2010), www.jopm.org/media-watch/conferences/2010/09/29/report-on-the-reassembling-health-workshop-exploring-the-role-of-the-internet-of-things/

Tremblay M, Cognology in Healthcare: Future Shape of Health Care and Society, Human and Organisational Futures, London, 2005

Tremblay M, The Citizen is the Real Minister of Health: the patient as the most disruptive force in healthcare, Nortelemed Conference, Tromso, Norway, 2002.

Wireless World Research Forum (2001) Book of Visions 2001.

Want to know more? There are some diagrams I excluded which showed a schematic of the system at work.

9 Tribes of the Internet and their health interests

Discussions on health literacy are increasing as healthcare providers, clinicians, payers and patients consider what this means for healthcare. Having been involved in launching the world’s first digital interactive health channel in the UK in 2000, one thing I learned is not to assume that everyone is alike or has common interests.

Healthcare systems are poor doing what retailers take for granted, namely the segmentation of their users. When we did the health channel, we worked with a simple framework drawing on work by the California HealthCare Foundation, in their report “Health E-People”. This gave us a workable model of the different types of users and their different needs, and that in developing content and services for them through the Channel, we needed to be mindful of this. More recent work by the Pew Internet Project has identified the “9 Tribes of the Internet”, to reveal how different people interact and use technology. Of course, segmentation can be quite elaborate, but at this stage we need a scaffold to guide our further understanding.

The main assumption we need to make about technology is how it will be used by people and thereby how this informs the adoption/diffusion process. Health and social care are traditionally “high touch” activities, given the way that knowledge has been organised, who knows it and how it is used. This, however, is being challenged by technologies that embody what traditionally has been found in the brains of specialist clinicians — what I call ‘cognologies’.

Increasingly we are seeing technological innovations that can embody both that knowledge (in decision algorithms for instance) and in skill (in robotic devices, vision systems for instance). Will people accept a shift toward high technology care at the expense of its traditional focus on care by humans? Is that an aesthetic preference (we like it) or might people come to prefer “lower touch” technologically-enabled services if it is reliable, and on-demand?

As we think about this, I suggest the following as some thoughts for policy makers and care providers:

  1. Eventually, the individual will have to own, in some form, their own health record if much of the desired changes in patient behaviours are to be realised. This will lead to patients having a new understanding of information about themselves, and as such this information will need to be clear without mediation or interpretation by others. Patients will, therefore, become involved in decisions about what to do with their information, and with whom it is shared and used; for instance, use in databases whether in commercial or public organisations that will be accountable to the patient for the use of that information. The patient, as what I call the ‘auditor of one’ will come to take a keener interest in the accuracy of the health record and be less tolerant of mistakes or inaccuracies, as is the case in other areas (e.g. banking, credit scoring).
  2. Not everyone will be digitally enabled in the way technology pundits fantasise about. This is not a digital divide and is not evidence of social exclusion, but is a personal choice of people to lead their lives as they wish in a pluralistic society; it may be that in the end, we all end up as digital natives over time, but some will still be hold-outs, or ‘islands in the net. The key implication here is that service providers will need to move in some cases very slowly to adopt technologies with some types of people. In time, perhaps people may adopt low-level access and interactivity, but for some people technological interactivity will remain at best an option not a preference within an evolving technological ecosystem. It remains to be seen whether this will continue to be the case; evidence from other technologies suggests not, that in time, technologies are broadly universally accepted, but not necessarily used in the same ways by everyone.
  3. The assessment of benefits of technologies in the traditional health technology assessment [HTA] model will need to pay much greater attention to the segment of the population likely to be involved and the social context of that group, taking account of distinct patterns of use and preferences. This challenges the current paradigm used within HTA communities. The conclusion that one-size-fits-all HTA assessment will increasingly prove inadequate. This means that designing and implementing technologies will need to be far more flexible when it comes to the structure of service delivery as the adoption/diffusion process itself will come to determine the socio-economic benefits. Consider that few today would subject the telephone to an impact assessment – it is now part of our expectations, and we should not be surprised if the same thing happens to evolving technologies in healthcare focused on the use by consumers and patients.
  4. The tribes model suggests that not everyone will necessarily buy into the technology revolution. For many people, they work in care precisely because they want to have personal contact with people, and not through intermediating technologies. Since many patients also would have that preference, organisations may need to structure services and staffing to ensure the right mix of people to service the right publics. This will challenge approaches to the organisational design of service providers, in the main suggesting more pluralism in variety, scale and function.
  5. Patient compliance, concordance, adherence may become more dependent on the features of the technologies, their design and ease of use, than on the willingness of the patient to follow a particular care regime. Patients are deliberately non-adherent for many good reasons (some of which reflect fundamental flaws from the medicine itself, its delivery system, or side-effects). Accidental non-adherence is another matter obviously. Helping people understand their limitations in using and working with technologies as matter of personal preferences will become very important, which increases the focus on personalisation.

It is common for health and social care systems, especially where the state is the main source of funding, to tend toward omnibus systems of service delivery, which has difficulty dealing with individual service preferences. Whether it is fully appreciated, such systems favour professional and provider interests and depend on proxy interpretations of patient preference. It would be a mistake to assume a similar approach with technologies. Instead, we should be encouraging approaches which are sensitive to the preferences and usage patterns of individuals. In this way, too, we may actually see services being offered that people will value and use.

The 9 Tribes in Health

Background

Pew Internet Project identified the “9 Tribes of the Internet” in a report in 2009 [http://www.pewinternet.org/2009/06/10/the-nine-tribes-of-the-internet/], to ascertain how different people interact and use technology. The California HealthCare Foundation, in its “Health E-People” [http://www.pewinternet.org/2009/06/10/the-nine-tribes-of-the-internet/], identified three broadly defined populations: the well with an interest in health, the newly diagnosed, and those with long-term or chronic health conditions.

The Pew research was instructive in thinking about how people might deal with a more technologically enabled health and social care system. I have sketched out some relationships in the table which gives an overview of the sort of considerations that are likely relevant and important.

NOTE: This was first written in 2010, and updated in 2019.

Pain

Pain is, well, a pain. It is the one thing we all have direct experience of, and can communicate to others, but which defies direct clinical measurement. We are left with subjective measures of pain, such as the ‘oucher’ scale or similar.

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While it is frustrating not to have a direct way to measure pain, it is a reminder that pain is also something we do create. Elaine Scarry’s insightful book, The Body in Pain, [https://www.goodreads.com/book/show/118287.The_Body_in_Pain]I think puts pain into the right context, as easy to feel but hard to describe, so we are left with metaphors. As well, there is historical evidence that today’s patients are less tolerant of pain itself as Edward Shorter wrote about in “Doctors and their patients: a social history” [https://www.goodreads.com/book/show/371664.Doctors_and_Their_Patients].

In terms of my own views, I did once chair a review of my hospital’s pain management, both acute and chronic, as we found poor compliance with pain protocols, so something wasn’t working. Indeed, we learned that burn patients may experience psychosis from the medicines that in effect separate their heads from their bodies to minimise the burn pain: they experienced a sense of being disembodied as they couldn’t feel their bodies. Surgeons, we also learnt, are of two types: those who would medicate post-operative pain slightly above the pain threshold, and those that felt the patient should perceive no pain. And then when patients could control their pain meds post-operatively with a pump, they tended to take less. The pain was telling them something important. Having said that, pain complaints are a frequent cause of medical litigation, as patients often feel that if they leave hospital in pain, the procedure was not successful. This is also telling us something important. In all cases, pre-operative pain counselling was an important part of surgical preparation for the patient.

This, of course, brings us to opioids.

While there is ongoing litigation, it is not appropriate to speculate on the outcome. It is, however, possible to look at the “pain ecosystem” and whether we can learn something. As while today it is opioids, tomorrow it may be psychoactive drugs for depression, or some thing we can’t today imagine.

Patients and doctors exist in a type of dance. Patient expectations, perhaps culturally or socially influenced, lead doctors to prescribe. And doctors need a good way to end the consultation, apart from standing up and holding the door open. Not getting a prescription for many patients is evidence that their needs have not been taken seriously; given how little time doctors actually spend listening to a patient — about 13 seconds! — should we be surprised? Doctors too exists in a type of dance with medicines and they are influenced as much as the weak clinical and practice guidance as the low quality of evidence and information available. They are left with taking each patient on their own merits, as they say, “the patient before me” to do what they think will work. Of course, there may be influential peers advocating specific pain practices, and often for pay — so-called Key Opinion Leaders.

The opioid crisis is a creature born of that broken medicines system, which plays off the anxieties of patients, and beliefs that somewhere “there’s a pill for that”. It plays off the failure of regulators to do their job, to ensure robust clinical guidance, and pain audits to capture emerging clinical / medicines risk. It plays off the failure of prescribing doctors to use evidence informed control and indeed self-restraint over the use of medicines. And it plays off the inappropriate use of incentives to pharmaceutical sales representatives.

In that respect as a systems problem, the opioid crisis is based on a profound ignorance and lack of evidence informed judgement by patients, doctors, industry and regulators.

If we do not get this sorted out, we will simply have this type of problem reassert itself again; indeed, it may already be lurking in the data.

Payer decision making

The relevance of value in establishing the positioning of medicines is the new normal for pharmaceutical marketing. Pharmaceutical companies have customers who are highly constrained by whether healthcare system funding is sustainable long term. Remember, payers think epidemiologically and in multiple years of costed care so industry needs to assess how that can be understood for product value. The pharmaceutical industry is constrained by its ability to generate revenues from medicines sales to cover the costs of research and development.

These two collide in the decision making process to adopt, or not, a medicine. The payers broadly have to balance the sustainability of their budgets with a potentially innovative medicine that will improve care outcomes. The pharmaceutical companies have to construct the value case to demonstrate these care outcomes. That probably means at least two things among many;

  1. Stop pricing drugs by the pill or pack, and start pricing valued outcomes for a defined set of patients over a number of treatment years, and
  2. Forget about trying to ‘time’ the market for product launch. The right time is set by payer budget cycles and their drug investment and disinvestment decisions. And, oh yes, the evidence.

By the way, my approach does differ from the journey model of Ed Schoonveld in important respects, by identifying the structured, and gated, decision processes involved; that why medicines aren’t sold, but bought.

Let’s first look at the colliding priorities. The diagram shows that payers are concerned with the value of a medicine in minimising treatment risk for the treated population. A company is seeking the value of the medicine by maximising the size of the treatment population that they believe benefits. As you grow the treatable population beyond the evidence, risk rises; for payers, reducing that risk is addressed through evidence.

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This is a collision of notions of ‘uncertainty’ in decision making and folks on the industry side should be used to requests for more evidence and novel access arrangements such as conditional reimbursement with evidence generation, and so on. As in any model of competing interests seeking a common price, the intersection of these two notions of uncertainty is defined by a price at which both parties will agree the price pays for the uncertainty it quantifies (i.e. it quantifies uncertainty in a certain way). The intersect quantifies risk, and sets the size of the treatment population that can benefit for that price.

The resulting curve may be thought of the ‘community effectiveness curve‘ depicting the optimal balancing of risk for the treatment community and a proxy for price agreement along that curve. This, by the way, is a better way to identify price corridors for people who still think that way.

This structured process is what this article is about.

Here is the gated decision process for payer decision making. While payers may not formally see themselves going through this in a linear way, they are thinking these thoughts, in this order.

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Gated Payer Decision Making for Market Entry of New Medicines

From the payer perspective, information needs to be specific to the decision gate and having the wrong information at the wrong time (e.g. the right information at the wrong gate) will just frustrate folks and probably irritate decision makers.

The diagram is read left to right, and a ‘yes’ answer to a question is needed in order to move through the gate. Getting a ‘no’ means the information supplied failed to make the case.

The following is a quick tour of the underlying logic. By the way, I call this a gated process as there are criteria for satisfying the conditions for passing through the gate; it is, I believe, unhelpful to decision making to characterise them as hurdles, as this suggests they are imposed to make life difficult. They are, actually, simply the structure of decision making.

Looking at this from a behavioural perspective, i.e. psychology informing decision making, each gate means this:

  • To get through the first gate, the payer is confronted with existing treatment options and asks why do I need another, or why change? Unfamiliarity may also be at work, with novel treatment benefits that lack comparators. Evidence of unmet need might be helpful along with good epidemiology to demonstrate the possibility of better outcomes.
  • Satisfied that a new therapy may be warranted, there is the question of risk and benefit compared to current treatment. While a new therapy might be indicated (yours?), the associated risk may be unacceptable compared to not using it. The benefits really do have to hold under increased uncertainty for a payer to agree to increased treatment risk. I suggest this is where discussion of standards of care begin to be quantified, having been introduced at the first gate. Payers often are not as aware as they should be on the current standards of care evidence in misdiagnosis, medical error and patient dissatisfaction.
  • Then having agreed that this uncertainty and its associated risk are acceptable, we are confronted with the cost and efficacy issue. Now we are beginning to price that risk. Good analysis of the costs of care and mis-care are useful, again because payers are not often aware of whole system costs (i.e. the costs of a treatment pathway) either because they are using using a fee schedule linked to DRG type classification or haven’t proofed their capitation models.
  • Success in pricing that risk moves to the question of the medicine in the context of total treatment costs and whether the treatment costs themselves for the patient population can be managed or will the scaling of the costs overwhelm the system for this treatment population versus all other options. Companies may see themselves as just suppliers of medicines for a price, and not a partner in the total system. But understanding the cost drivers along the whole treatment pathway, not just the costs a new medicine may drive, becomes an important element in final value pricing. If you have a medicine that reduces associated costs, or avoids certain costs (think the Triple Aim, here), then the determinants of value are much clearer. It may be that a biomarker is a value-add from one perspective but only if it reduces medical error and misdiagnosis, without increasing costs, so precision patient identification becomes important. If you’ve got this far, though, you’ll have already shown you can demarcate the treatment population, including the responder subset with a degree of precision.
  • Finally, the payer thinks about the future and whether there will be new medicines coming along that might address the same treatment population, alter risk differently, improve outcomes, avoid costs, with better patient adherence, and so on. Given, broadly, a medicine is alone in its treatment class for months, rather than years, payers may choose to delay decision making or consider options you’ve ignored that may trade off future costs and present priorities. This may be where a payer will be thinking disinvestment or product substitution and the determinants of that are critical in this final phase. Here’s a scenario: Why might a particular medicine not be a preferred medicine on a hospital formulary? The answer is simple: don’t have production problems where supply cannot be guaranteed. The lesson is that this is where the long game gets played out.

For those of you who read Kahneman’s “Thinking Fast and Slow”, or similar, there are decisional heuristics at work here. And across that gated process, you are contending not just with highly structured evidence informed quantitative information, but also how humans can be influenced by how human’s think they think. This has a raft of factors such as confirmation bias, hyperbolic discounting, choice overload, loss aversion, endowment effect, anchoring, mental accounting and social proof. It will pay to be attentive to when you present what information and the frame of mind decision makers are in. The reason this is important is that that regulators and payers in different countries, hospitals or regions can make different decisions from the same evidence, so something else is going on.

And so, a comment on pricing. To short-circuit this challenging gated process, it is common simply to cut the price, i.e. discount. Discounting is a quick win trick that only works if payers are trying to reduce present costs, which they all are. However, payers with their eye on the future are more likely to be interested in pricing arrangements that address uncertainty over time and so will be amendable to arrangements such as coverage with evidence development or outcomes guarantee. If they are focused on whole system issues, they will be interested in care pathway (cohort/whole system) pricing for instance. If, though, the future costs are a priority, think about capitation arrangements, or simple price/volume but be mindful that this last is like selling products door-to-door in the 1950’s.

I happen to think care pathway pricing of carefully demarcated patient populations with costs taken over say 5 years is a better pricing model for both parties. Value can be demonstrated on both sides along with evidence of such things as improved adherence (to reduce waste by non-responders) or diagnostic decision support aids to address misdiagnosis and sources of medical error or reduce time to the correct diagnosis, in the case of rare diseases for instance.

This article is designed to emphasise product value determination under conditions of uncertainty to arrive at a sustainable long-term relationship.

Mixed economy of healthcare is more intelligent than a supertanker

From the UK Guardian: private healthcare providers.

The research on comparative performance of for profit and not for profit healthcare providers is well developed, so it is surprising to see such a weak quality assessment about private providers in the NHS.

The NHS is a very difficult customer for a number of reasons, primarily the glass box of public scrutiny and politics. But many countries successfully navigate public scrutiny of providers in general. So what is the story behind this newspaper article?

  1. It is true that many private providers have handed back their contracts to the NHS usually because either they didn’t do their sums properly, or found the environment more challenging than they expected. But a significant number of NHS providers are in substantial financial trouble, too, and they can’t hand back their contracts, but instead get a state bail-out. This is hardly a level playing field of course, but indicates that the financial regimes for public and private providers is different and that the commissioners may be unable to purchase care services from a mixed economy of providers.
  2. Private providers are often accused of not providing the highest standards of care. This is an interesting problem as virtually all the doctors on private contracts work the bulk of their time in the NHS and all belong to their Royal Colleges and the GMC regulates doctors, not just NHS doctors. It is worth being reminded that the NHS employs 57% or so of all registered nurses, while 37% work in private settings and an additional 7% in nursing homes. As well, the public sector is not the major employer of pharmacists and nutritionists, and the list goes on. Are these health professionals agreeing to work in less well-run and managed private facilities or do they believe they are providing a higher personal standard of care.
  3. Yes, the private hospitals are free-riders on the training system for health professionals as they don’t participate in that system, but there is no reason they couldn’t. They also don’t have emergency facilities, which is pointed to as evidence of poorer standards of care as a patient in trouble would need to be transferred to an NHS provider. But in the NHS, A&Es are being rationalised, converted into trauma centres, and patients transferred to superior treatment facilities when a particular hospital cannot cope. Patients and ambulances are apparently queuing outside the A&Es. There could be a case to be made for private urgi-care centres (18 hours a day, out-patients only), but the private sector would need to made a strategic decision that they wanted to elevate their service mix above elective, private insured care. Until they do something to fix that fault line, they’ll likely be continuing target.
  4. As for the money, in the total scheme of things, private contracting is still less than 10% of total expenditure on the NHS. The article typically falls into the trap of making numbers look big, when as a proportion they are quite small.
  5. NHS managerial expertise is generally what is used to run private hospitals. Many former NHS managers work in hospital contract management, where a hospital is run by a management team on contract.
  6. Circle had trouble not because the Hinchingbrooke is particularly challenging but because the managerial and financial environment was unsustainable partly because of underfunding of the contract by the NHS among other reasons.
  7. It is worth keeping in mind that while the US is seen as a bastion of private healthcare, the majority of providers are not-for-profits (including the hospitals associated with universities where the care is of world class excellence) and that the US care system is over 50% funded from the public purse. Private care providers exist globally and we might usefully look to countries in Asia, such as South Korea, to see what at future healthcare system might look like. Think Samsung.

What Cognology would say:

The government does not consider healthcare as a whole system but fragments regulation by ownership type; this is the root cause rather than something intrinsically problematic with private care, especially given the substantial evidence of problems with NHS care. This means they have failed to create a single regulatory environment to cover both public and private providers which would benefit all citizens in the country regardless of their personal choices. Taking this one step further, embedding intelligence in organisations, hive mind type logic which drives complex adaptive systems, would alter the objectives of regulators and embolden the component parts toward greater autonomy.