This work did not focus on comparative effectiveness, because it will be considered in a separate series. It is, nevertheless, necessary to stress that evidence-based treatment and comparative effectiveness analysis of treatments are two sides of the same coin. Both together would provide a strong scientific foundation in formulating evidence-based policies thereby improving the quality, efficiency, effectiveness, long-term consistency, and sustainability of the health care system. These evidence-based policies would be equated to the standard of care, which would have a higher bar in courts. The preceding chapters have clearly made the point that clinical evidence-based decisions are driven by a clinical question that either the clinician poses, or (and) the patient may pose as well. Hence, from that starting point, a research synthesis question is developed, which is framed within the constraints of the patient characteristics, the interventions under consideration, and the clinical outcome sought – hence the acronym, PICO.

The PICO question is so stated as to provide the keywords necessary to gain access to the entire body of published reports on the question of interest, the available bibliome corpus. Several fully validated instruments are then utilized to establish the best research evidence. Having obtained the best available evidence, the research synthesis investigator then obtains a consensus statement, which the clinician integrates in the process of clinical decision making for treatment intervention. These decisions incorporate on an equal footing the best available evidence, and the patients’ needs/wants, medical and clinical history, and insurance coverage (or means for private payment). The process is grounded on the logic model, which, as discussed in earlier chapters, permits careful formative and summative evaluation by means of fully validated instruments (e.g., AMSTAR, AGREE, GRADE).

Typically, an evidence-based provider would approach the patient with a statement such as: the best available evidence for your condition recommends this or that treatment intervention. Now, in your case, with your history, etc., these are my clinical re com mendations.

In other words, the best available evidence is an adjuvant for optimal treatment care for every individual patient. Clinical evidence-based decision making is par excellence personalized health care.

Comparative effectiveness research¹ (or analysis) also rests on the best available evidence, which it obtains by means of exactly the same research synthesis process: a PICO question is formulated, in which the outcome, O, focuses on cost– and risk–benefit relationships. Differences arise at the tail-end of the process, where decisions are grounded on a probabilistic model (cf., earlier chapters), akin to the Markov decision tree. Clinical decisions that rest on comparative effectiveness analysis are oriented toward societal benefits (i.e., financial, risk-minimizing).

It is fair to say, as has already been proposed in the pertinent research literature, that comparative effectiveness analysis is the other side of the same coin as evidence-based treatment interventions [5, 6]. One focuses on effectiveness issues from the viewpoint of societal benefit and reducing overall costs, the other is directed at individualized health care. One cannot be considered without consideration of the other.

Future research in the field will increasingly establish the intertwined nature of the relationship between comparative effectiveness research and analysis on one hand, and evidence-based clinical decisions for optimal treatment on the other.

Moreover, cognizant of the fact that both these elements rely on the entire body of available information about a given patient population (“P” in PICO), future research will increasingly recognize the urgency of the need for human information technology (HIT) in this sector. It is urgent, timely, and critical that we develop databases of patient histories, so that as the product of research synthesis becomes available, they may be integrated with these HIT repositories, and probabilistic (comparative effectiveness) or logic model (evidence-based treatment) decisions obtained by com pu t er ized algorithms.

Finally, and perhaps most importantly, because both comparative effectiveness analysis and evidence-based intervention depend upon the systematic evaluation of the best available research evidence (i.e., research synthesis), future research in the field of research synthesis must reach novel and improved frontiers in the next decade.