EBD is Not a ‘Lavish Perk’
The debate continues on the merits of Evidence-based Design (EBD). The Wall St. Journal is jumping in with a blog post by one of its writers in response to an article written by Laura Landro in her “Informed Patient” column in today’s Journal.
The WSJ blogger says that EBD is a largely unproved “perk” that doesn’t make sense in this economic climate. However, what he doesn’t understand is that EBD is a process of making design decisions based on credible research that results in improved outcomes. If creating a facility that helps improve both the staff and patient experience results in shorter patient stays, reduced staff turnovers, and other clinical and operational improvements, it will actually save money.
What’s more annual operating expenses far outweigh annual capital budgets, so the investment in better building design is a good one. We know this, because we’ve made the business case using actual real data. (FYI, An updated white paper outlining the business case is being published soon — we’ll be posting it on our website for free download as well as a 10-minute webcast summary.)
I’m glad, though, that people are writing and blogging about EBD. We need to continue the conversation so that, one day, everyone understands exactly what it is and what it is not.
August 21st, 2008 at 11:02 am
While evidence-based design may be a new term, it has been in practice since the time planning and design of buildings emerged as a profession. For instance, we have always brought into consideration expert opinions while designing buildings, both through codes/guidelines and through active involvement of experts in project decision-making. An analogy could be drawn from evidence-based medicine. The following two classification system (extracted from Wikipedia) shows that expert opinion do count as evidence (look at the last level in both classifications).
Systems to stratify evidence by quality have been developed, such as this one by the U.S. Preventive Services Task Force for ranking evidence about the effectiveness of treatments or screening:
• Level I: Evidence obtained from at least one properly designed randomized controlled trial.
• Level II-1: Evidence obtained from well-designed controlled trials without randomization.
• Level II-2: Evidence obtained from well-designed cohort or case-control analytic studies, preferably from more than one center or research group.
• Level II-3: Evidence obtained from multiple time series with or without the intervention. Dramatic results in uncontrolled trials might also be regarded as this type of evidence.
• Level III: Opinions of respected authorities, based on clinical experience, descriptive studies, or reports of expert committees.
The UK National Health Service uses a similar system with categories labeled A, B, C, and D. The above Levels are only appropriate for treatment or interventions; different types of research are required for assessing diagnostic accuracy or natural history and prognosis, and hence different “levels” are required. For example, the Oxford Centre for Evidence-based Medicine suggests levels of evidence (LOE) according to the study designs and critical appraisal of prevention, diagnosis, prognosis, therapy, and harm studies:[9]
• Level A: Consistent Randomised Controlled Clinical Trial, cohort study, all or none (see note below),clinical decision rule validated in different populations.
• Level B: Consistent Retrospective Cohort, Exploratory Cohort, Ecological Study, Outcomes Research, case-control study; or extrapolations from level A studies.
• Level C: Case-series study or extrapolations from level B studies.
• Level D: Expert opinion without explicit critical appraisal, or based on physiology, bench research or first principles.
It may not, thus, be (technically) accurate to say that evidence-based design is an untested paradigm. The appropriate question is what level of evidence is one dealing with?
It is even more inaccurate to state that there is a lack of evidence. The Center for Health Design has come up with two comprehensive reports (among others) on studies that provide scientific evidence to support healthcare decision-making. To state that the studies cited in the CHD report do not count as evidence would be analogous to stating that the hundreds of journals that published the study findings, the hours spent on peer review process, the billions spent in funding those research, and the enormous time spent by researchers on those studies do not deserve our confidence.
The more pertinent issue is whether one wishes to use the evidence that exists. When the available evidence is used in decision-making, the process immediately qualifies as an evidence-based design process. The key distinction is between the availability of evidence and the application of such evidence.
That does not mean that evidence is currently available to support all decisions. Creating hospitality type environment in hospital entrances (possibly with all best intentions), for instance, may not have the evidentiary foundation to predict any outcomes – which means it is not an example of evidence-based design and should not be described as one, until empirical evidence is generated.
However, evidence is available in other aspects of the environment pertaining to patients and staff, and that cannot be disputed. The key question is whether such evidences are considered during decision-making. Evidence has the ability to provide a structured approach to a process that seeks to obtain a consensus among a varied stakeholder group (with potentially conflicting interests), regarding capital investment in areas that predict the closest match with short and long-term organizational objectives, and a process where physical design decisions are taken in conjunction with operational design decisions. If, after that process, the collective decision of the stakeholders is to provide care inside tents, then that would represent the best informed decision in that particular context.
The WSJ article generated a truly healthy debate, and I hope it would lead to a stronger commitment to generate more credible evidence in more domains of healthcare decision-making, rather than discounting the evidence that already exists.