Thứ Sáu, Tháng Sáu 2, 2023
HomeHealthImplementation may be a science, but alas, medicine is still an art...

Implementation may be a science, but alas, medicine is still an art – Healthcare Blog


I’ve been in healthcare for over forty (!) years now, in one form or another, but it wasn’t until last week that I heard about implementation science. In a way, it’s a health care problem.

It is true that I am not a doctor or another clinician, but everyone working in healthcare should know and think a lot about “scientific research into methods that promote systematic adoption.” integrate research findings and other EBPs into routine practice, and thereby improve the quality and effectiveness of health services” (Bauer, etc all).

it takes a JAMA articleby Rita Rubin, to warn me about this fascinating science: It takes an average of 17 years for evidence to change practice—The burgeoning field of implementation science seeks to speed things up.

It turns out that implementation science is nothing new. had one magazine for it (smart naming Science of implementation) since 2006, along with a relatively newer version Scientific communication made. Both focus on articles illustrating “methods to advance the application of research findings to routine health care in clinical, organizational or policy contexts”.

Brian Mittman, PhD., declared that the goal of implementation science is:

  • “To create credible strategies to improve health-related processes and outcomes and to facilitate widespread adoption of these strategies.
  • To generate insights and general knowledge about the execution process, roadblocks, facilitators and strategy.
  • To develop, test and refine development theories and hypotheses, methods and measures.”

Dr. Mittman distinguishes it from quality improvement largely because QI focuses primarily on local problems, whereas “the goal of implementation science is to develop generalization knowledge.”

Ms. Rubin’s headline highlights the problem of healthcare: it can take an alarmingly long time for empirical research results to be incorporated into standard medical practice. Have some dispute about whether 17 years is really true, but it is widely accepted that, whatever the actual number is, it is too long. Even then, Miss Rubin reminds us, it’s extra estimate that only 1 in 5 interventions are included in routine clinical care.

She quotes gastroenterologist Rachel Issaka, MD, MAS of the University of Washington: “implementation science is really trying to bridge the gap between what we know and what we do.” Or rather, between what is known by some and what most do. “The hope of implementation science is that we can synthesize what works for whom, where, and for what disease,” says Nathalie Moise, MD, MS, director of implementation science research at Columbia University. and close that 17-year gap. JAMA.

It is remarkable that implementation science focuses both on encouraging clinicians to initiate new proven treatments as well as to discontinue long-term treatments that subsequently proven to be of little or no value (“discontinued”).

There are science departments or programs running at Brown, Duke, Johns Hopkins, northwest, Penn, UCSF, UNC, University of Michigan, University of WashingtonAnd Awaken the forest, to name a few. Some go to medical school, some go to public health school.

With such extensive training in the field, you’d think we’d do a better job of bridging that gap – or, as Ms. Rubin calls it the “gap” – between what we should do and what we should do. But we are still here, and as Ms. Rubin points out, COVID proves it.

“COVID-19 has shown the world that ‘knowing what to do’ does not guarantee ‘doing what we know’,” said Enola Proctor, implementation science pioneer and emeritus professor of social work and infectious disease expert Elvin Geng, MD, write. , MPH, director of the Center for Dissemination and Implementation at the Institute of Public Health, both at Washington University in St Louis, in a 2021 Science Editor.

Few would argue that clinicians are actively ignoring best practices. It is more about how they are trained, how others around them practice, what they are familiar with/comfortable with and is compounded by the vast body of medical knowledge. medical knowledge estimate doubles every few months, and that half-life gets shorter and shorter; it was estimated to be 2 years just five years ago. No one – not a human after all – can keep up.

Other limitations are that studies may not have sufficiently diverse study populations or they are socioeconomic barriers to the desired care. Ms. Rubin points out that the lack of transportation after a colonoscopy is the reason some patients refuse to receive them. Dr Issaka notes: “I really think that white, socioeconomically good clinicians don’t know that there are people out there who lack the means of transportation. That’s just one in a million – a billion – blind spots our health care system has about the people who use it.

One has to wonder what kind of industry is healthcare in that it needs a science to study how to implement proven methods to be more effective for its clients. Most other industries focus on this as a matter of course, as a matter of survival, not healthcare.

Much of this, I fear, is due to our historical view that doctors are more like scientists, if not more. We delay their verdict. We lack mechanisms to ensure that they are practicing the same way as other physicians in the community, much less than other communities, and far worse off for best practices/evidence most recent. That’s the main reason why healthcare needs implementation science and why it’s been slow to actually succeed.

Big data and AI give us the tools to change this.

Using Big Data, we have the ability to collect and analyze what happens to patients. We can know what treatments the doctor is prescribing and whether they are in line with best practices. On top of that, it will allow us to measure performance across much larger populations, in more diverse situations, in a much faster time frame.

Using AI, individual clinicians will be better able to update existing medical knowledge. Now that’s an impossible task, but one that AI has already begun to demonstrate. Most current AI is trained on fixed data sets, which can’t include the latest research, but those data sets are still much better than the clinician’s memory, and for the foreseeable future, AI will be able to find current findings in real time. I love that there is science to it and I wish those who practice it great success, but I look forward to the day when healthcare puts its principles into everyday practice.

Kim is a former marketing manager at a Blues grand scheme, editor of The Late & Lamentations Tincture.ioand is now a regular THCB contributor.

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