Health benefits represent employers’ fastest growing operating expense. Efforts from human resources to control healthcare spending through restrictive plan design changes and corporate wellness programs may not achieve employer health and financial goals and may negatively impact employee outcomes. Employers are increasingly contracting directly with providers in order to access quality medical care and to control spending. The purpose of this practice-focused paper is to provide survey data collected from 10 employers as part of the quality improvement activities of a direct primary care (DPC) program. Overall, survey responses of employees engaged with DPC had higher patient satisfaction, group health plan rating, perceived organizational support, and job satisfaction than survey responses of those registered into the program but not yet engaged with a DPC physician. Implementation considerations and DPC characteristics are provided.

I-O psychologists often face the need to reduce the length of a data collection effort due to logistical constraints or data quality concerns. Standard practice in the field has been either to drop some measures from the planned data collection or to use short forms of instruments rather than full measures. Dropping measures is unappealing given the loss of potential information, and short forms often do not exist and have to be developed, which can be a time-consuming and expensive process. We advocate for an alternative approach to reduce the length of a survey or a test, namely to implement a planned missingness (PM) design in which each participant completes a random subset of items. We begin with a short introduction of PM designs, then summarize recent empirical findings that directly compare PM and short form approaches and suggest that they perform equivalently across a large number of conditions. We surveyed a sample of researchers and practitioners to investigate why PM has not been commonly used in I-O work and found that the underusage stems primarily from a lack of knowledge and understanding. Therefore, we provide a simple walkthrough of the implementation of PM designs and analysis of data with PM, as well as point to various resources and statistical software that are equipped for its use. Last, we prescribe a set of four conditions that would characterize a good opportunity to implement a PM design.