The development of an ICF-based questionnaire for patients with chronic conditions in primary care.

OBJECTIVES: We developed a self-reported questionnaire for patients in primary care with chronic conditions aged 50 years or older. The questionnaire supports a more person-centered approach by adopting a biopsychosocial focus on functional status instead of a focus on disease. STUDY DESIGN AND SETTING: Based on the International Classification of Functioning, Disability and Health (ICF), an ICF Primary Care set for patients with chronic conditions was constructed in three phases. In the first phase, we identified relevant ICF categories for the ICF Primary Care set by using existing ICF sets for chronic health conditions. The ICF Primary Care set was completed by a multidisciplinary expert panel and consisted of 52 ICF categories covering ICF's body functions, activities, participation, environmental factors, and personal factors. In the last phase, we constructed a draft version of the questionnaire by converting the ICF categories from the ICF Primary Care set into questions and corresponding scales. To improve the draft version of the questionnaire, we conducted cognitive interviews with patients with chronic conditions in an iterative process, focusing on the problems patients experienced in answering the items of the questionnaire. Interview analysis was used for assessing the content and construct validity of the questionnaire. RESULTS: Thirty cognitive interviews with patients were conducted in five different interview rounds. In these interviews, we identified 124 problems in the responding process of answering the questionnaire, mostly concerning difficulties with the comprehension of the constructs of the questions. The number of problems reduced from an average of 11 problems per interview in the first round to an average of two problems in the last round. CONCLUSION: Conclusion: The final version of the questionnaire demonstrated high content and construct validity (i.e., patients are well capable of describing their functional status in terms of ICF) and is applicable in primary care in the Netherlands.

Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks?

BACKGROUND: General practice based registration networks (GPRNs) provide information on population health derived from electronic health records (EHR). Morbidity estimates from different GPRNs reveal considerable, unexplained differences. Previous research showed that population characteristics could not explain this variation. In this study we investigate the influence of practice characteristics on the variation in incidence and prevalence figures between general practices and between GPRNs. METHODS: We analyzed the influence of eight practice characteristics, such as type of practice, percentage female general practitioners, and employment of a practice nurse, on the variation in morbidity estimates of twelve diseases between six Dutch GPRNs. We used multilevel logistic regression analysis and expressed the variation between practices and GPRNs in median odds ratios (MOR). Furthermore, we analyzed the influence of type of EHR software package and province within one large national GPRN. RESULTS: Hardly any practice characteristic showed an effect on morbidity estimates. Adjusting for the practice characteristics did also not alter the variation between practices or between GPRNs, as MORs remained stable. The EHR software package 'Medicom' and the province 'Groningen' showed significant effects on the prevalence figures of several diseases, but this hardly diminished the variation between practices. CONCLUSION: Practice characteristics do not explain the differences in morbidity estimates between GPRNs.

The influence of population characteristics on variation in general practice based morbidity estimations.

BACKGROUND: General practice based registration networks (GPRNs) provide information on morbidity rates in the population. Morbidity rate estimates from different GPRNs, however, reveal considerable, unexplained differences. We studied the range and variation in morbidity estimates, as well as the extent to which the differences in morbidity rates between general practices and networks change if socio-demographic characteristics of the listed patient populations are taken into account. METHODS: The variation in incidence and prevalence rates of thirteen diseases among six Dutch GPRNs and the influence of age, gender, socio economic status (SES), urbanization level, and ethnicity are analyzed using multilevel logistic regression analysis. Results are expressed in median odds ratios (MOR). RESULTS: We observed large differences in morbidity rate estimates both on the level of general practices as on the level of networks. The differences in SES, urbanization level and ethnicity distribution among the networks' practice populations are substantial. The variation in morbidity rate estimates among networks did not decrease after adjusting for these socio-demographic characteristics. CONCLUSION: Socio-demographic characteristics of populations do not explain the differences in morbidity estimations among GPRNs.

Defensive testing in Dutch family practice. Is the grass greener on the other side of the ocean?

BACKGROUND: Ordering laboratory tests and diagnostic imaging can be part of the defensive behavior of the physician. How often does this occur in family practice in the Netherlands? Defensive behavior is defined as a clear deviation from the family physician's usual behavior and from what is considered to be good practice in order to prevent complaints or criticism by the patient or the patient's family. METHODS: Over a 1-year period, 1989-1990, 16 family physicians in 11 practices with 31,343 patients recorded all episodes of care involving an order for laboratory tests or diagnostic imaging or both (n = 8897). The physicians selected one or more reasons to order each test from a fixed list of clinical considerations. In addition, they recorded whether they acted defensively for every test order. RESULTS: The participating physicians reported that some degree of defensive medicine was associated with 27% of all test orders. Defensive testing varied with the clinical reasons to order a test: the wish to exclude a disease or to reassure the patient was a much stronger motive for defensive testing than the intention to confirm a diagnosis or to screen. Defensive tests generally resulted in fewer abnormal findings. CONCLUSIONS: Defensive testing is an important phenomenon in Dutch family practice: it forms a well-defined element of practice despite the variations implicit in the different clinical reasons to order a test. Defensive testing is associated with a lower probability of finding an abnormal test result. The analysis of family physicians' clinical reasons for ordering tests becomes more meaningful when defensive testing is included.