Revising the National Ambient Air Quality Standards: The integration of science and policy

Under the Clean Air Act, Congress granted discretionary decision making authority to the Administrator of the Environmental Protection Agency (EPA). This discretionary authority involves setting standards to protect the public's health with an "adequate margin of safety" based on current scientific knowledge. The Administrator of the EPA is usually not a scientist, and for the National Ambient Air Quality Standard (NAAQS) for particulate matter (PM), the Administrator faced the task of revising a standard when several scientific factors were ambiguous. These factors included: (1) no identifiable threshold below which health effects are not manifested, (2) no biological basis to explain the reported associations between particulate matter and adverse health effects, and (3) no consensus among the members of the Clean Air Scientific Advisory Committee (CASAC) as to what an appropriate PM indicator, averaging period, or value would be for the revised standard. ^ This project recommends and demonstrates a tool, integrated assessment (IA), to aid the Administrator in making a public health policy decision in the face of ambiguous scientific factors. IA is an interdisciplinary approach to decision making that has been used to deal with complex issues involving many uncertainties, particularly climate change analyses. Two IA approaches are presented; a rough set analysis by which the expertise of CASAC members can be better utilized, and a flag model for incorporating the views of stakeholders into the standard setting process. ^ The rough set analysis can describe minimal and maximal conditions about the current science pertaining to PM and health effects. Similarly, a flag model can evaluate agreement or lack of agreement by various stakeholder groups to the proposed standard in the PM review process. ^ The use of these IA tools will enable the Administrator to (1) complete the NAAQS review in a manner that is in closer compliance with the Clean Air Act, (2) expand the input from CASAC, (3) take into consideration the views of the stakeholders, and (4) retain discretionary decision making authority. ^

Global practice experience: A useful lens for understanding patient and staff perceptions of successful integration of health information technology in primary care

Making healthcare comprehensive and more efficient remains a complex challenge. Health Information Technology (HIT) is recognized as an important component of this transformation but few studies describe HIT adoption and it's effect on the bedside experience by physicians, staff and patients. This study applied descriptive statistics and correlation analysis to data from the Patient-Centered Medical Home National Demonstration Project (NDP) of the American Academy of Family Physicians. Thirty-six clinics were followed for 26 months by clinician/staff questionnaires and patient surveys. This study characterizes those clinics as well as staff and patient perspectives on HIT usefulness, the doctor-patient relationship, electronic medical record (EMR) implementation, and computer connections in the practice throughout the study. The Global Practice Experience factor, a composite score related to key components of primary care, was then correlated to clinician and patient perspectives. This study found wide adoption of HIT among NDP practices. Patient perspectives on HIT helpfulness on the doctor-patient showed a suggestive trend that approached statistical significance (p = 0.172). Clinicians and staff noted successful integration of EMR into clinic workflow and their perception of helpfulness to the doctor-patient relationship show a suggestive increase also approaching statistical significance (p=0.06). GPE was correlated with clinician/staff assessment of a helpful doctor-patient relationship midway through the study (R 0.460, p = 0.021) with the remaining time points nearing statistical significance. GPE was also correlated to both patient perspectives of EMR helpfulness in the doctor-patient relationship (R 0.601, p = 0.001) and computer connections (R 0.618, p = 0.0001) at the start of the study. ^