Clinical information technology in hospitals: Evaluation in a rural state

In light of continuous concerns over patient safety and quality of care, and with the release of the Institute of Medicine reports (1999 and 2001), hospitals face challenges that necessitate changes associated with patient care. Hospitals are turning to new Information Technologies (IT) to reduce errors, save money and improve efficiency. With the exception of a survey instrument developed by Pare and Sicotte (2001), very little empirical research has addressed measuring IT sophistication, or assessing IT capacities in rural settings. This project uses the instrument by Pare and Sicotte (2001) to provide an overview of technological capacities in Iowa hospitals, and assesses the extent to which variables load together and measure the same/similar underlying constructs.; The study has a cross-sectional design and involves primary data collection. The study population includes all hospitals in Iowa (n = 115), excluding Veterans' Administration hospitals, mental health institutions and resource centers. The survey measures the level of IT capacities, and collects general data about the hospital and the respondent. The clinical IT sophistication section constitutes the major part and includes three types of questions assessing dimensions of IT sophistication in clinical areas. Functional Sophistication is measured using binary questions (1 was assigned for each computerized process and a 0 otherwise). Technological Sophistication questions measure the extent of use of technologies on a 0--7 scale. And the level of IT integration is assessed on a 1--7 scale.; The results show a relatively low level of IT complexity in Iowa hospitals, with considerable variation between settings. Many existing processes and technologies are basic and expected in any hospital setting (e.g., inpatient/outpatient admissions, dictation). Advanced technologies and computerized functions were rare, and the level of integration was low, except for patient management and laboratories systems. The findings from exploratory factor analysis show better grouping of variables based on the measured concept than the represented dimension. Similar analyses on larger and different samples will provide more insight of the underlying IT constructs in this instrument. The results of this study can be used for data reduction and scoring, and further research employing confirmatory factor analyses.