Designing Healthcare For Real People

Diseased Is The New Normal

What happens to healthcare when everyone has a disease?

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Large proportions of the populations of developed countries have a chronic disease[1] and this is expected to increase steadily over the coming years as advances in healthcare keep more people alive for longer. Older people in particular are likely to have more than one diagnosis. It is not hard to imagine that very soon, the majority of people – certainly the majority of adults over 30 – will have a disease.

This is a problem, although not perhaps for the reasons that might first spring to mind.

I blame William Farr.[2] Farr was a 19^th century British doctor who took an interest in causes of death, arranged the marriage of statistics and medicine and kicked epidemiology into gear. The world has not been the same since, not least because Farr built the foundations for the systematic classification of diseases. This has turned the focus of healthcare from caring for ill-health (discomfort and impairment) to dealing with ‘disease’.  

Disease is a concept that throughout history has been a product of efforts to explain and remedy ill-health. It is inextricably tied to prevailing concepts about the causes of ill-health and our ability to describe them – whether by invoking demons, curses, miasma, microbes, disturbances to energy flows or environmental toxins. Ill-health is a fact (or at least a phenomenon) but a disease is nothing more than an idea[3] – the continually evolving nature of which is evidenced by the revision of the International Classification of Diseases (ICD) ten times in little more than 100 years.

And the ICD has grown each time it has been revised, from containing 139 disease entities in the late-19^th century to 14,199 in 1990[4]. Notwithstanding the fact that ill-health is experienced in a wide variety of ways, it is still remarkable that so many ideas are needed to describe and explain these experiences. It is perhaps even more remarkable that the more we seem to learn about health, the greater in number these explanatory ideas become; rather than converging on a comprehensive, efficient understanding of what sickness is all about, healthcare knowledge seems to be diverging into a universe of complexity. The number of ailments affecting people has not grown one hundred-fold in one hundred years – just the challenge that healthcare has brought upon itself.

With so many new labels on the rack, it is inevitable that more people than ever are being dressed for the healthcare ball. The problem is, a shortage of facilities means that they are all-dressed up but have nowhere to go.

And just as the latest cuts from Milan might not sit too well on the man who has only the local bar for a night on the town, these new diseases might not be fit well into an existing healthcare context that is optimised to handle acute illnesses.

The fundamental aim with acute illness is to achieve a cure, or at least a short-term remedy. Broadly, the logic of acute-illness treatment flows from presentation to diagnosis to intervention to discharge. This process will vary in duration but in all cases, it is essentially time-limited. Both patient and clinician have discharge in their sights and the healthcare system administrator would like this to occur as quickly as possible. The defining moment is when the clinician tells the patient that no further visits are required. The process is linear and limited.

On the other hand, the management of chronic illness, by definition incurable, is an indefinite process. The goals (enhance quality of life through maximising functioning, enhancing functional status, minimizing distress, and prolonging life through the prevention of acute exacerbations and complications) persist until the end of the patient’s life. They are never completed. Further visits are always required. The process is circular and indefinite. The sole end point is death.

In fitting people up with new diseases, healthcare has laced itself into its own strait-jacket and the water level is rising. It is desperately hoping that information technology, various system-management contortions and a variety of financial diets will enable some Houdini-like escape.

Perhaps they will conjure up some magic – or at least, create some room to move. But I think that a viable future for healthcare depends on a more radical re-visioning. Essentially, this involves a re-think of the values upon which healthcare systems are predicated.

First: Care is paramount. The role of healthcare first and foremost is to care for sick people. Yes, it’s a ‘Back To The Future’ scenario where healthcare practice comes out from behind healthcare technology. Its first priority is attending and bearing witness to the suffering of people who are unwell. The quality of the relationship between healthcare professional and patient is recognised as an essential platform upon which all healthcare interventions need to be built.

Second: Illness is recognised as being a defining part of humanity. It is normal to become unwell. Illness is to be managed, endured, survived – can even be ennobling and enlightening. People may involve professional health-carers but they accept that their illness is first and foremost their own problem, not that of the professional healthcare system.

Third: Quality of life is the justification of healthcare. Healthcare’s job is to help people have a better life, not necessarily a longer one. 

Fourth: People claim ownership of their health. Healthcare is a standard life-skill that is learned and is practiced day-to-day, by ordinary people (i.e. not healthcare professionals) who are taking primary responsibility for their own health and well-being. Healthcare professionals are there to assist from time-to-time with their special knowledge and skills but the less involved they are in the day-to-day care of people’s health, the better. Healthcare is a social contract in which people have both a right to care and a responsibility to care.

Fifth: Chronic illness management is the core healthcare model. The healthcare system is optimised (structurally, conceptually, technologically) to deal with chronic illness. Acute conditions are treated as exceptions or ‘acute exacerbations’. Healthcare professionals are trained primarily in the care of people with chronic illnesses in primary-care or community-based care. Primary healthcare is recognised as the most complex and challenging arena of work, representing the intellectual and ‘technical’ pinnacle of professional healthcare.

This vision is not far out of reach. All of this happens every day to many people in many places – but it is not the dominant culture.

The next article will discuss challenges to achieving a culture-change that might bring this vision (or something like it) to the top.

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[1] Devol, R., &  Bedroussian, A. (2007). An Unhealthy America: The Economic Burden of Chronic Disease. Milken Institute, Santa Monica, California.

[2] Rhetorical device alert. I don’t really blame Farr. That would be silly.

[3] For an excellent, albeit challenging explanation of why diseases are nothing more than ideas, I recommend Kelly, M. P., & Moore, T. A. (2012). The judgement process in evidence-based medicine and health technology assessment. Social Theory & Health, 10(1), 1–19. doi:10.1057/sth.2011.21

[4] Knibbs, S. G. (2010). History of the development of the ICD. Retrieved April 19, 2013, from http://www.who.int/classifications/icd/en/HistoryOfICD.pdf

Does Healthcare Treat Patients Or Parameters? Part Two – Evidence-Based Practice

Part one of this two-part discussion of the problem of healthcare data looked at the use of data for administrative purposes. This article looks at the use of data in evidence-based practice and attempts to draw some conclusions about how to use data while honouring the principle of primum non nocere.

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In the first part of Does Healthcare Treat Patients or Parameters? I raised some concerns about how data can be misused in healthcare administration, to the detriment of healthcare’s over-arching goal of helping sick people. I linked this adverse effect to a doctrinaire approach to performance management, which perverts the valid contribution that performance management can indeed make to system governance.

Closely intertwined with data zealotry in healthcare administration is data zealotry in healthcare practice. Although healthcare has throughout history inferred cause-effect relationships from observed phenomena, scientific developments during the 19^th and 20^th centuries led to healthcare becoming strongly empirically driven. This gave rise to the Evidence-Based Medicine movement in the last quarter of the 20^th century, which has striven to base all healthcare practice on empirical research findings. Beginning in medical practice, this dogma has infiltrated most disciplines within healthcare and in general discussions of healthcare, is perhaps better named Evidence-Based Practice (EBP).

The data of EBP has given healthcare practitioners a comforting promise of certainty and precision when faced with their awesome responsibilities in the midst of baffling ambiguity and complexity.  It also gives them a defensive position (both emotionally and medicolegally) when things go wrong – ‘the surgery was a complete success but unfortunately the patient died’.

But data’s seductive quality has the potential to harm clinical practice in much the same way as it is harming healthcare administration. Rather than science informing healthcare practice, it seems to be distorting the healthcare provider’s view of the patient, to the detriment of practice quality.

While researching this article, I discovered that much of what I wanted to say had already been written – and far more cogently than I could ever have managed. Dr. Trisha Greenhalgh, Professor of Primary Health Care and Co-Director of the Global Health, Policy and Innovation Unit at the Centre for Primary Care and Public Health in London, incisively unpacks the problems of data zealotry in healthcare practice in her 2012 Editorial in the Journal of Primary Health Care[1], which I strongly recommend you read.

She parses the development of EBP using insights from the philosophy and history of science —

“The pre-paradigmatic research of off-road break-away groups is typically slow, messy and characterised by wrong turnings and periodic pile-ups. But eventually some tracks are laid and a clear direction of travel is pointed out.” (p.92);

a deep understanding of the methodological underpinnings of EBP —

“ . . . the language of [EBP] . . . converts the unique individual narrative into abstracted population categories and Bayesian probabilities . . . Most medical cases, especially in primary care, fit the clean, efficient, probabilistic language of [EBP] remarkably poorly.” (p.94);

and practical examples of how these issues play out in everyday practice —

“In . . . context, and taking account of intuitive cues built from 25 years of listening to patients coughing, I classified this patient’s cough alongside the abdominal pain for which he had been fully investigated (no organic cause found) and his recurring headaches accompanied by flashbacks (post-traumatic stress disorder). I removed my doctor-as-diagnostician hat and . . . bore witness to his suffering.

 

The medical student who was sitting in with me later called up a guideline . . . and challenged me. Why had I not listened to the patient’s chest or asked him to blow into a meter? Why had I not completed the decision support algorithm? Why, he implicitly asked, had I not followed the rules?” (p.94)

Why, indeed? I cannot speak for Dr Greenhalgh but my answer would be: because to slavishly follow the guidelines would be to miss the opportunity to add true value for that patient, at that time.

Take the case of patients who have the audacity to suffer more than one condition at a time. There is a useful evidence-base for treating depression, diabetes or asthma but no evidence-base for treating the depressed patient with diabetes and asthma. (Most treatment studies specifically exclude patients with multiple diagnoses). A recommended treatment for one condition (corticosteroids for asthma) may be contraindicated in the treatment of the other condition (diabetes). The patient may be currently motivated to deal with one problem but not highly motivated regarding another.

When faced with a real-world patient with all their real-world complexity, the healthcare provider still has to ‘make it up as they go along’ – the scientific evidence-base is of some help but by no means provides definitive answers.

Improvising in this way is still EBP, just not data driven practice. David Sackett, one of the main modern progenitors of Evidence-Based Medicine, noted the value of experiential learning and clinical improvisation in a 1997 paper:

“The practice of evidence-based medicine means integrating individual clinical expertise with the best available external clinical evidence from systematic research. By individual clinical expertise we mean the proficiency and judgment that we individual clinicians acquire through clinical experience and clinical practice.”[2]

 

A nuance here is the level of ambiguity inherent in the clinical presentation. To meaningfully apply the ‘best-evidence’, first the practitioner needs to correctly identify which set of evidence is best for each particular presentation. In certain contexts, there might be an evidence-base for how best to do make those decisions (evidence-based assessment protocols; trauma presentations to the emergency department, for example) but even then, decisions leading to best outcomes will rest on some clinical wisdom about which assessment protocol to apply. Barking up the wrong tree does no-one any good, no matter how well ‘by the book’ it is done.

Data is most helpful when the presentation most closely matches the scenarios in the studies from which the data are derived. The more dissimilar the presenting patient and condition from the study conditions, the less relevant the data will be. Further, the more ambiguous the presentation, the more the final outcome will be determined by clinical wisdom rather than data.

In primary care, the presentations are often very ambiguous indeed, the scope for assessment very limited in terms of both time and resources and the individual patients quite dissimilar from the study samples in ways that are very important to long-term clinical outcomes (medical complexity, language, culture, health literacy, access to resources, etc). Thus, in primary care, data can be more often than not, barely relevant and of little practical help in the management of individual patients.

The future of more effective and efficient healthcare lies in primary care rather than in the specialty departments of hospitals, so how can we use data in healthcare (particularly in primary healthcare) in ways that improve quality of care without doing harm to patients in the process?

I suggest we begin with remembering that facts change all the time – half of what we think we know will turn out to be wrong (but we won’t know which half for another 15 years). Sometimes what we thought was wrong will actually turn out to have been right all along. It follows from this that some humility is always in order and we should avoid dictatorial approaches to healthcare administration and provision.

Healthcare is the patient’s journey. Without the patient, there would be no role for the provider or the healthcare administrator. Providers and patients are in it together, stumbling through the dark with the dim torch of science to help guide the way. Even when the provider has been this way many times before, it may never have been with this patient, and never on this night.

Both are taking risks, both will be affected by their own and one another’s anxieties (and other feelings) and the quality of their working relationship may have as much to do with the eventual outcome as anything else, particularly in primary care[3]. In the end, the patient’s needs, objectives and beliefs are paramount, not the provider’s. Healthcare administration should support and facilitate this relationship and avoid creating structures and processes that impede it.

Data should not dominate the processes of providing healthcare. Data’s proper role is to inform healthcare practice and to lead its development but not to drive it.

Healthcare providers should be thoroughly familiar with the evidence-base for the most common things they encounter and know how to access it for the less common things. However, the map is not the territory and the guidebook won’t have a standard operating procedure for every obstacle encountered along the way. For either providers or administrators to pretend otherwise is to risk ignoring important realities or imposing irrelevant constraints.

Healthcare providers should understand what is regarded as ‘quality care’ in the community within which they practice. This will assist them in their use (and disregard) of data. I suggest the working definition of  ‘quality care’ is what one’s colleagues would regard as mainstream best-practice; if you are going to be wrong half the time, you want to do it in good company. In the end, consensual validation is the only real defense one can have if asked to explain one’s actions at some point in the future.

Healthcare providers should strive to understand and reflect on their patients as people. Data requires a context to have a meaning and the correct context is the patient whose healthcare is being attended. If the healthcare provider does not understand the patient, the chances increase that the wrong meaning will be ascribed to the data and the patient will be mistreated.

What should patients do? That is a topic for another article.

 

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[1] Greenhalgh, T. (2012). Why do we always end up here? Evidence- based medicine’s conceptual cul-de-sacs and some off-road alternative routes. Journal Of Primary Health Care, 4(2), 92–97.

[2] Sackett, D. L. (1997). Evidence-based medicine. Seminars in Perinatology, 21(1), 3–5. doi:10.1016/S0146-0005(97)80013-4

[3] E.g. Hojat, M., Louis, D. Z., Markham, F. W., Wender, R., Rabinowitz, C., & Gonnella, J. S. (2011). Physicians’ empathy and clinical outcomes for diabetic patients. Academic Medicine : Journal of the Association of American Medical Colleges, 86(3), 359–64. doi:10.1097/ACM.0b013e3182086fe1

Does Healthcare Treat Patients Or Parameters? Part One – Healthcare Administration

Data ≠ information ≠ knowledge ≠ wisdom.
GIGO – garbage in, garbage out.
The map is not the territory.

This is part one of a two-part discussion of the problem of healthcare data, looking at the use of data for administrative purposes. Part two will look at the use of data in evidence-based practice and attempt to draw some conclusions about how to use data while honouring the principle of primum non nocere.

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If a tree falls in the forest but not on a healthcare performance report, does it make a sound?

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Healthcare management, like most aspects of public administration, has become enamoured with performance measurement in recent decades – for reasons that might have as much to do with the needs of funding administrators as the needs of healthcare organisations, workers and patients[1].

It is certainly true that healthcare governance, like the governance of any complex system, requires some method of performance monitoring and management. This is no different in principle to a pilot’s use of instruments to fly an aircraft in cloud – provided the instruments are accurate, calibrated and reliably related to the fundamentals of flight, successful aviation is possible by just using the dials.

However, aviation has a few advantages over health: the laws of physics governing flight are well-understood, they are highly predictable and they can be accurately and reliably measured.

In healthcare, the causation of an outcome is often complex, indirect and poorly understood, if it is understood at all. Consequently, meaningful performance measurement can be extremely difficult to achieve – what do you measure? When? How?

Take, for example, emergency department (ED) throughput. This seems at first glance to be a relatively straight-forward parameter and it is often chosen as a target in the governance of (hospital) healthcare quality. The rationale for improving ED throughput is based on evidence that overcrowding increases the risk of mortality and other adverse clinical outcomes[2].

And indeed, measuring the time taken to process people through ED is relatively easy to do. But when it comes to using the data, we run into problems.

First, we need a framework for evaluating the measurement results – is the value found good or bad? Are our goals or standards achievable and reasonable? Do we evaluate performance normatively, comparing one ED with another? (But are the services truly and fairly comparable?) Or do we do it idiographically, using the past performance of an ED as a baseline for its future performance? (Then, how do we deal with ceiling effects and the law of diminishing returns? And do we set an absolute goal [criterion = at or above a specified level] or a relative one [criterion = a specified amount of improvement]?)

Second, if we don’t yet know how to improve performance, the measurement is of little value to governance. Research on the factors affecting ED length of stay has actually been very inconclusive. There is great variation between studies in the factors examined, effect sizes have not been great[3], and the strongest effects may be factors beyond the control of the ED, such as the severity of the patient’s condition[4], the number of children presenting as patients, the number of ambulance arrivals[5] and the need for cooperation from other departments in the hospital[6]. Further, the contribution to clinical outcomes made by overcrowding is modest and the absolute risk of adverse outcomes is very low[7].

In other words, we don’t really know very much about the mechanisms by which ED overcrowding leads to adverse clinical outcomes and the major, plausible correlates that have been identified are not amenable to intervention.

When it comes time for the governors to tell the boss how well they have done, the situation becomes even more complicated. The boss is probably not that interested in ED throughput as such – she wants to know whether the quality of care in the ED has improved as a whole, possibly even across all EDs under her purview. A single indicator, such as throughput, shows but one aspect of overall quality and even if a collection of indicators is included in the report, most aspects of overall quality will remain undescribed.

The validity of synecdoche – taking a part to stand for the whole – in healthcare governance rests on some seriously shaky assumptions. Metaphorically, they are vividly captured in the tale of the blind men and an elephant and have been well described with reference to the British National Health Service (including the possibility of cheating by both target-setters and target managers) in an articulate 2006 paper by Gwynn Bevan and Christopher Hood[8].

Over and above these serious issues of measurement validity is the problem of diversion of clinical resources away from clinical care. Almost invariably, data collection requires a great deal of commitment from front-line clinical staff. These are people who are trained to provide clinical services and are motivated to do their jobs for the patients in front of them. They may not understand the organisational and systemic governance objectives, nor have any interest in them beyond whether the ‘system’ seems to be aiding or impeding their own achievements.

Filling in a form to satisfy the reporting requirements of an unseen manager may not be a high priority compared with responding to the distress of the patient on the examination bed – and it shouldn’t be. It is healthcare service that sick people want and need, not healthcare data.

So, the ‘cheating’ referred to above might not even be due to perverse ‘incentives’. It might just be that the clever healthcare professional has learned that ticking apparently meaningless boxes on the computerised form in a certain pattern is the quickest means of avoiding contingent pop-up windows, so they can get back to their patients with less delay. Their behaviour is shaped by their priority of getting on with their jobs of treating patients and the rubbish data gets fed into the governance machinery, resulting in consequently nonsensical policy decisions. Garbage in, garbage out.

There is no doubt that accurate, reliable data are invaluable for the advancement of healthcare but by generating misinformation and diverting clinical resources, there is a very real possibility that a preoccupation with data can be contrary to both the governance of healthcare and healthcare’s fundamental objective of helping sick people[9]. If the governors were pilots flying on instruments, they might follow the dials straight into the ground while the cabin crew are counting the sales of in-flight snacks.

Performance measurement ought not to be lightly undertaken. It might be better to be sure that we don’t know than to create a false illusion of knowledge.

Some suggestions for the safe use of data in health administration:

1. Remember that performance measurement is not an end in itself but a means to improve quality and efficiency. The numbers are just an input to guiding progress towards a goal – and may not actually be all that critical to the achievement of the goal.
2. Performance is much more affected by the quality of management than the quality of measurement. Determining the most important priorities, and making sure everyone knows what they are and that they work by them is good management, will enhance performance and make the measurement of performance easier.
3. Considerable research may be required to establish the most effective basis for defining standards or goals to measure against. If the research is judged too costly to undertake, the measurement wasn’t worth doing.
4. Select metrics that are valid (they actually measure what they purport to measure), reliable (they measure it consistently across times and contexts) and are sufficiently correlated with the target goal or standard to be a meaningful indicator of true changes.
5. Measures need to be meaningful to the people charged with collecting the data – if something is being measured but is not seen as being important by those doing the measuring, it probably won’t get done (and if forced, they will cheat).
6. If it is not possible to validly measure differences (across time, between service units, etc), don’t do it. The numbers are worthless and at best, a waste of time collecting. At worst, they will lead to bad decisions that are disruptive and damaging.

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[1] The example of the United Kingdom is discussed in Bevan, G., & Hood, C. (2006). What’s Measured Is What Matters : Targets And Gaming In The English Public Health Care System. Public Administration, 84(3), 517–538.

[2] Bernstein, S. L., Aronsky, D., Duseja, R., Epstein, S., Handel, D., Hwang, U., McCarthy, M., et al. (2009). The effect of emergency department crowding on clinically oriented outcomes. Academic emergency medicine : official journal of the Society for Academic Emergency Medicine, 16(1), 1–10.

[3] Zun, L. S. (2009). Analysis of the literature on emergency department throughput. The Western Journal Of Emergency Medicine, 10(2), 104–9.

[4] Yoon, P., Steiner, I., & Reinhardt, G. (2003). Analysis of factors influencing length of stay in the emergency department. CJEM, 5(3), 155–61.

[5] Chan, L., Reilly, K. M., & Salluzzo, R. F. (1997). Variables That Affect Patient Throughput Times in an Academic Emergency Department. American Journal of Medical Quality, 12(4), 183–186.

[6] Zun, L. S. (2009). Analysis of the literature on emergency department throughput. The Western Journal Of Emergency Medicine, 10(2), 104–9.

[7] Richardson, D. B. (2006). Increase in patient mortality at 10 days associated with emergency department overcrowding. The Medical Journal Of Australia, 184(5), 213–6.

[8] Bevan, G., & Hood, C. (2006). What’s Measured Is What Matters : Targets And Gaming In The English Public Health Care System. Public Administration, 84(3), 517–538.

[9] Hood, C. (2012). Public Management by Numbers as a Performance-Enhancing Drug: Two Hypotheses. Public Administration Review, 72(s1), 85–92. 

Falling By The Wayside On The Pathway To Better Health

Patients can easily slip off the path to better health. Where, how and why? What can be done?

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Scenario: you have a disease.

How do you know?

1. Your mama told you what to watch out for?
2. Your friend/partner says, “That ain’t right. You should see someone about that”?
3. You Googled that funny sensation?
4. The clinic found it at your annual check-up?
5. It’s OK, you won’t have a disease. You never get sick. (Good luck with that).

Now what?

1. Pray for the strength and wisdom to accept the will of God?
2. Make an appointment with the doctor/osteopath/naturopath/faith healer?
3. Go immediately to the E.D.?
4. It’s probably nothing serious. Just wait and see if it gets better?
5. Google it?

You decide that to get better, you need the correct diagnosis and an effective treatment. Your doctor/naturopath/faith healer/Dr. Google knows what they are talking about, right? And you believe them? And you understand and can afford to do what they suggest? And you will actually follow their advice, won’t you?

If you are justifiably confident on all those points, good for you — but you are probably an exception. Research published by the World Health Organisation found that between 24% and 62% of people in seven different countries have diabetes that is undiagnosed and untreated. Even for those diagnosed, treatment goals were achieved by no more than 12% of individuals. Some developed countries, such as England and Scotland, were among the worst performers[1].

Figure 1 tracks the pathway from onset of a disease to its outcome. One thing stands out in this: the patient’s behaviour is as pivotal in influencing outcome as anything the healthcare system does.

 

Figure 1. The healthcare pathway from disease to outcome

 

For a disease to be presented for diagnosis, the symptoms of the illness have to be recognised as requiring healthcare attention — usually the patient (or someone close to them) is in the best position to notice these symptoms.

And yet the American Heart Association reported that 50% of people in the United States suffering an acute myocardial infarct die in or before reaching an emergency department or hospital[2].  The report notes:

“The decision to seek treatment is heavily influenced by patients’ social context, cognitive processes, and emotional reactions . . .” (p.171) and “The importance of [these] factors . . . cannot be overemphasized.”(p.175)

After the patient has presented for healthcare, the quality and timeliness of diagnosis remains significantly dependent on their behaviour.  They need to truthfully and accurately report their concerns, cooperate with investigations and re-present as required for further assessment, diagnosis and management.

But patients routinely obscure and distort the facts of their illness to their doctors[3]. Many do not complete the monitoring procedures requested[4] and do not follow treatment recommendations, both pharmacological and otherwise[5].

It is hardly surprising that it can be difficult to replicate clinical trial results in real-life practice[6].

From a healthcare point-of-view grounded in bio-science, it makes little sense for patients to jeopardise their future health in these ways. And yet, healthcare workers trained in bio-science behave in all the same ways themselves when they are in the patient role, underlining the American Heart Association report comment on the power of social and emotional drivers, at least.

Some of the factors that probably shape these behaviours:

• Health literacy
  • Knowledge of disease symptoms and signs
  • Understanding of where and how to seek appropriate care
  • Understanding of information and advice received
  • Decision-making skills and ability to negotiate with the healthcare system
• Health beliefs and expectations
  • Concerns about the specific disease and its treatment
  • Expectations about disease timeline (acute vs chronic)
  • Perceived necessity of treatment
  • Tolerance of ill-health
• Culture and values
  • Determinants of disease and prognosis (fate vs will of God vs science)
  • Primary sources of care (healthcare or other sources?)
  • Outcome priorities (cure? control? tolerance? dignity? escape?)
• Affect and coping
  • Coping style (avoidance vs approach)
  • Coping skills
  • Mood/mental health
  • Self-efficacy
• Social security
  • Partners, family and friends
  • Employment status and financial security
  • Housing

But there is so much to do here. Where on earth do we in healthcare start?

Well, I think we can do some whittling of the list fairly quickly.

1. Social security lies beyond the realms of healthcare. Healthcare is a very expensive and inefficient place to deliver remedies for social dis-ease. That is not what healthcare professionals are trained to do or motivated to do and directing them to do it is a misallocation of resources.
2. Health literacy is a product of education and socialisation. The education system is probably much better positioned than the healthcare system to get results here.
3. Culture and values are phenomena that emerge from an obscure morass of influences that can only be shaped over long time periods, to the extent that they can be deliberately shaped at all. Healthcare operates on a much shorter time scale.

I am not suggesting that these three categories of drivers of health-related behaviour are unimportant – quite the contrary. But they are not within the power of healthcare to fix.[7]

That leaves the psychological drivers – beliefs, expectations, affect and coping. There are already well-established strategies and interventions within healthcare for addressing these and some promising applications of technology such as text-messaging[8] and the internet[9] to make them available to large numbers of people.  

Progress in this area has been remarkable considering the comparative lack of investment[10] and there is much room for increasing knowledge and capability[11]. By investing both in the development of new knowledge and skills and the implementation of what we already know, we may gain a great deal more value from past and future investment in the bio-science basis of healthcare.

 

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[1] Gakidou, E., Mallinger, L., Abbott-Klafter, J., Guerrero, R., Villalpando, S., Ridaura, R. L., Aekplakorn, W., et al. (2011). Management of diabetes and associated cardiovascular risk factors in seven countries: a comparison of data from national health examination surveys. Bulletin of the World Health Organization, 89(3), 172–83. doi:10.2471/BLT.10.080820

[2] Moser, D. K., Kimble, L. P., Alberts, M. J., Alonzo, A., Croft, J. B., Dracup, K., Evenson, K. R., et al. (2006). Reducing delay in seeking treatment by patients with acute coronary syndrome and stroke: a scientific statement from the American Heart Association Council on cardiovascular nursing and stroke council. Circulation, 114(2), 168–82. doi:10.1161/CIRCULATIONAHA.106.176040.

[3] Fainzang, Sylvie. (2005). When doctors and patients lie to each other. In van Dongen, Els and Fainzang, Sylvie (eds). Lying and Illness: Power And Performance. Het Spinhuis, Amsterdam. Pp. 36-55.

[4] Raebel, M. A., Carroll, N. M., Andrade, S. E., Chester, E. A., Lafata, J. E., Feldstein, A., Gunter, M. J., et al. (2006). Monitoring of Drugs. The American Journal of Managed Care, 12(May), 268–274.

[5] Fischer, M. J., Scharloo, M., Abbink, J. J., Van ’t Hul, A. J., Van Ranst, D., Rudolphus, A., Weinman, J., et al. (2009). Drop-out and attendance in pulmonary rehabilitation: the role of clinical and psychosocial variables. Respiratory medicine, 103(10), 1564–71. doi:10.1016/j.rmed.2008.11.020; Osterberg, L., & Blaschke, T. (2005). Adherence to medication. The New England Journal of Medicine, 353(5), 487–97. doi:10.1056/NEJMra050100

[6] Davidson, M. H. (2006). Differences between clinical trial efficacy and real-world effectiveness. The American Journal of Managed Care, 12(15 Suppl), S405–11. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/17112328

[7] I see little alternative than for government to take responsibility for developing a policy framework across economics, education and social welfare that promotes a society that enhances the well-being of all its citizens. The healthcare and justice systems are the indicators of how well these policies are working.

[8] Cole-Lewis, H., & Kershaw, T. (2010). Text messaging as a tool for behavior change in disease prevention and management. Epidemiologic Reviews, 32(1), 56–69. doi:10.1093/epirev/mxq004

[9] Webb, T. L., Joseph, J., Yardley, L., & Michie, S. (2010). Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. Journal of Medical Internet Research, 12(1), e4. doi:10.2196/jmir.1376

[10] For example, the US National Institute of Health allocates less than 5% of its research budget to behavioural and social sciences. FY2011 Directors Overview. National Institute of Health. Page OD-9. Downloaded from officeofbudget.od.nih.gov/pdfs/FY11/FY%202011%20OD%20CJ%20508%20compliant.pdf.

[11] E.g. Webb, T. L., Joseph, J., Yardley, L., & Michie, S. (2010). Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. Journal of Medical Internet research, 12(1), e4. doi:10.2196/jmir.1376

Perception Is Everything

From the placebo effect through the Hawthorne Effect to fears about drugs and wishful thinking, a patient’s behavior is driven by perceptions rather than objective reality.

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Dear reader, please take a deep breath and bear with me for a moment.

[1S[1α,3α,7β,8β(2S*,4S*),8α β]]-1,2,3,7,8,8a-hexahydro-3,7-dimethyl-8-[2-(tetrahydro-4-hydroxy-6-oxo-2H-pyran-2-yl)ethyl]-1-naphthalenyl2,2-dimethylbutanoate is a white crystalline powder, practically insoluble in water and freely soluble in chloroform, methanol and ethanol[1]. It is produced as a result of the fermentation of the Aspergillus  terreus fungus, which can cause a treatment-resistant infection in some people[2].

(OK, you can breathe again now).

Would you take it, if it was offered to you?

Your answer to this question probably has a lot to do with your past experiences, beliefs and expectations – and yet, the white, crystalline powder in question is the same substance no matter who you are.

It has the potential to markedly reduce your risk of Coronary Heart Disease. There are good reasons why your doctor would not use my opening paragraph in recommending it to you and yet, the white, crystalline powder in question is the same substance no matter which words are used to describe it.

You see, your doctor wants you to agree to take this cholesterol-lowering drug (known more commonly as simvastatin), to feel confident about taking it, and to keep taking it. Even if she knew the chemical description, she is unlikely to scare you with it. Calling it “[1S[1α-whatever” is not likely to help. (OK, chemistry geeks might be an exception, so impressed by the doctor’s grasp of their home turf, that they will comply with everything she says for ever more – but that just goes to prove my point).

Our physiological responses to drugs are determined by the chemistry and are fairly consistent from one person to another. But our behavioural responses (such as whether or not we will follow our doctor’s advice) are not. They depend instead on our past experiences and how these affect our beliefs and expectations. And without congruent behaviour (treatment adherence and persistence), even the most reliable chemistry can have no effect.

Most healthcare practitioners in my experience, especially those in primary care, are very aware of this and try very hard to tailor their services to the individual patient. But they can be remarkably unsupported by structural factors in this effort to be effective. As I noted in the first article in this series, healthcare planning often approaches patients as if their behaviours are as consistent and predictable as their biochemistry.

For example, healthcare training has usually emphasised biotechnology over psychology, so practitioners’ intuitive interpersonal skills are often unsupported by systematic knowledge. They are also often hampered by simplistic healthcare policies, plans and management systems such as those that morph evidence-based guidelines into hard-and-fast rules of practice[3].

I suggest that healthcare plans should make their starting point the subjectivity and behavioural variability of real humans. Different patients should be expected to see and respond to their healthcare differently – especially differently from how healthcare practitioners, strategists and managers (not to mention biochemists) think about it.

To achieve this, the fundamental unit of analysis of healthcare practice should be the person, not the sub-systems of the human body. Beliefs, expectations, feelings, and culture should be where healthcare planning begins and, after the mechanisms of evidence-based treatment have been worked through, it returns for a final sense-check.

Note that I wrote healthcare practice, not healthcare science. There is no doubt that understanding illness and treatment at the level of sub-systems is critical to the advancement of healthcare knowledge. That applies as much in the sociology and psychology of health as it does in cell biology, physiology and biochemistry. But the practice of healthcare should centre on the whole person[4].

How would this change healthcare?

To begin with, it would mean assuming that different ‘solutions’ are going to be required to obtain target outcomes with different patients. The goal can be specified, together with a range of possible evidence-based routes to those goals, but the detailed pathway to success is left for the healthcare practitioner and his or her patient to choose.

It would also mean greater use  in treatment efficacy studies of research designs preserving information on individuals (e.g. case-control studies in which psychosocial variables are used to match participants) instead of the comparison of aggregated group data (e.g. the classic randomised control trial).

The result would be a stronger policy and evidential framework for healthcare practitioners, who often have to interpolate normative data and work around rules and systems to provide optimal care to the individuals they treat.

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[1] NZ Medicines and Medical Devices Safety Authority Data Sheet APO-Simvastation. Downloaded from http://www.medsafe.govt.nz/profs/datasheet/a/aposimvastatintab.pdf, 18/02/2013.

[2] Dannaoui, E., E. Borel, F. Persat, M. A. Piens, and S. Picot. 1999. Amphotericin B resistance of Aspergillus terreus in a murine model of disseminated aspergillosis. J. Med. Microbiol. 49:601-606.

[3] I will discuss the two-edged sword of evidence-based practice in more detail in a subsequent article in this series.

[4] At the very least! The health and well-being of individuals are inextricably tied to their relationships with others and the communities in which they live. Ignoring these interpersonal influences on a patient’s health is also a significant risk to obtaining good outcomes. The boundary between intrapersonal and interpersonal may constitute the dividing line between clinical and population health practices.

In Healthcare, Behaviour Eats Biology For Breakfast

When Peter Drucker said “culture eats strategy for breakfast”, he was referring to the challenges of leadership in business organisations. In any enterprise, even the most inspired strategic planning and determined executive fiat cannot achieve good results without the active support of the employees. If there is a discrepancy between the executive team’s plans and the organizational culture, the culture will most often drive the outcomes.

Shift the context to health objectives and much the same applies: healthcare strategists and leaders cannot achieve good healthcare outcomes without the active cooperation of those involved in the actual service processes – and that includes patients just as much as healthcare workers. Whether it is turning up for scheduled appointments, following medication dosing recommendations, doing rehabilitation exercises, cooperating with examination procedures, or just plain telling the truth, all healthcare practice is ultimately dependent  on patient behavior for its success.

But often patients seem to be missing from healthcare policy, planning, strategy, and innovation. Not missing in the sense that patients are never mentioned. Missing in the sense that strategy rarely reflects the realities of patient behavior and its effect on clinical outcomes.

The ‘patient’ of the strategy document is often a uniform entity with characteristics defined by measures of central tendency from some population deemed to be relevant. These ‘patients’ will vary in head count, perhaps vary on disease parameters, but are unlikely to vary in psychosocial terms. This ‘patient’ is expected to behave rationally (i.e. do what the strategists think they should do) – and if they don’t, this is regarded as a deficit that ‘education’ or ‘information’ will correct. (But if it was just a matter of so-called ‘health literacy’, no healthcare professional would smoke, eat too much, exercise too little, drink excessively, take non-researched medicines, ignore symptoms, or fail to complete their course of antibiotics.)

Most importantly, patients are missing from healthcare plans as active decision-makers. Yes, quite often patients are ill-informed. Sometimes they do forget. Sometimes they just can’t afford to access the care they need. But very often, they are choosing to behave in ways that diverge from the strategist’s vision – they do understand, they do remember and they could access the treatment but they don’t want to. And even if the reason seems irrational to the healthcare system, it will make perfectly good sense to them, given their circumstances.

Circumstances such as: their prior experiences; their beliefs and feelings about themselves, their illnesses and the treatments they are being offered; the other commitments and obligations they have in their lives; their cultural and spiritual values.

I suspect that if we shift our innovation investment from developing new biotech solutions to developing better systems and interventions that provide patients with healthcare that fits them well, we would get much better value from existing treatments and much better results from new healthcare technologies. This requires attention to the factors determining people’s behavior rather than their physiology.

For example, large proportions of medication prescriptions issued are never filled and as many as a half of patients prescribed long-term medication regimens stop taking the medication within six months[1]. No matter how effective the biochemistry, if the patient doesn’t take the medicine, it won’t work. And all the medicines sitting untouched in bathroom cabinets are healthcare money down the drain.

And the impact on evidence-based strategic planning? Non-adherence creates a gap between clinical trial efficacy data and actual treatment outcomes. This gap can potentially mislead and confuse healthcare payors, who see worse-than-expected results for their medicines investment, and frustrate pharmaceutical suppliers, whose reputation may be tarnished despite having an effective product — that is, a product that would be effective if patients used it as prescribed.

So how can we design health service systems and interventions that better match what patients are really like and thereby deliver better healthcare outcomes and better returns on investment?

In the following articles in this series, I will discuss some of what we already know can make a difference, some of what I think will help, and some of the obstacles to change that we need to deal with.  Please add your commments, subscribe to the feed or email me directly.

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[1] The following papers provide detailed data: World Health Organization. Adherence to Long-Term Therapies. Geneva Switzerland: World Health Organization, 2003; DiMatteo MR. Variations in patients’ adherence to medical recommendations: a quantitative review of 50 years of research. Med Care 2004;42:200-9; Osterberg L, Blaschke T. Adherence to medication. N Engl J Med 2005;353:487-97