Recent attention to the question of value in health care — the ratio of outcomes to long-term costs — has focused on problems of definition and measurement: what outcomes and which costs? Less attention has been given to an equally difficult but important issue: how do health care delivery organizations reliably deliver higher value?

It would certainly simplify health care reform if we could show the superiority of a dominant delivery model (e.g., the accountable care organization or the medical home) and roll it out nationwide, developing and proving new approaches to creating value only once. However, experience suggests that not only do new delivery models — for example, integrated networks — not necessarily live up to their promise, but they are surprisingly difficult to transfer, even when successful; those that succeed in one U.S. region haven’t always done well in another. Organizations considered to be among the nation’s highest performers, such as the members of the new High Value Healthcare Collaborative, often have unique personalities, structures, resources, and local environments. Given the health care sector’s mixed record of disseminating clinical innovations and system improvements, how do we learn from leading organizations?

Although high-value health care organizations vary in structure, resources, and culture, they often have remarkably similar approaches to care management. Specific tactics vary, but their “habits” — repeated behaviors and activities and the ways of thinking that they reflect and engender — are shared. This is important because experience suggests that such habits may be portable.

The first common habit is specification and planning. To an unusual extent, these organizations specify decisions and activities in advance. Whenever possible, both operational decisions, such as those related to patient flow (admission, discharge, and transfer criteria), and core clinical decisions, such as diagnosis, tests, or treatment selection, are based on explicit criteria. Criteria-based decision making may be manifest in the use of clinical decision support systems and treatment algorithms, severity and risk scores, criteria for initiating a call to a rapid-response team or triggering the commitment of a future resource (e.g., a discharge planner, preprocedure checklists, and standardized patient assessments), and for patients, shared decision making.

Specification also applies to separating heterogeneous patient populations into clinically meaningful subgroups — by disease subtype, severity, or risk of complications — each with its own distinct pathway. For example, Dartmouth’s Spine Center uses a detailed intake assessment that combines the 36-Item Short-Form Health Survey, computerized visual aids, and shared decision making to sort patients according to the likelihood that they will do better with either medical or surgical care. Similarly, genomic testing has allowed oncology units to divide patients into separate groups according to their probable response to specific therapies (for instance, KRAS testing for cetuximab therapy). And at Intermountain Healthcare in Utah and Idaho, the needs of psychiatric patients are divided into mild (routine care by a primary care physician), moderate (team care), and severe (specialist referral), with a scoring system based on published guidelines. Some organizations, such as Children’s Hospital Boston, are developing standard approaches to uncommon and complex conditions.