Whatever happened to eHealth?

Innovations in information technology will continue to transform the health care system to a system that more closely resembles that envisioned in the 1999 and 2001 IOM reports. Healthcare Leadership & Management Report will continue to report over the next few months on practical use of technology to improve the health care system. We are now researching several systems and providers that are using technology to improve their patient care, productivity, bottom line and host of operational concerns.     

浅谈电子健康技术(eHealth)与护理应用

本文简介了电子健康(eHealth)技术与护理应用的部分领域,展示了老年健康及社区和家庭医疗护理方面的应用前景。     

北京电子健康（eHealth）2005研讨会在京召开

2005年9月25日，为期3天的电子健康（eHealth）北京2005研讨会在北京21世纪饭店召开，此次会议由国家863项目——数字医疗核心装备与关键技术课题组会同中国电子学会医疗仪器学会、生命电子学会共同主办。     

Tailored eHealth Lifestyle Promotion: Which Behavioral Modules Do Users Prefer?

Health risk behaviors are widespread among adults and often co-occur. eHealth computer-tailored technology provides individuals with personalized feedback regarding multiple lifestyle behaviors. First, the authors investigated individuals’ preferences for particular lifestyle modules and hypothesised that health preventive behavior modules would be preferred over addictive behavior modules. Second, characteristics associated with these choices were examined. A web-based questionnaire assessed demographics, health status, and five lifestyle behaviors (i.e., physical activity, fruit consumption, vegetable consumption, alcohol intake and tobacco use) among adults (N = 1,828). Responses were translated into a health risk appraisal outlining whether respondents adhered to the national guidelines for these behaviors. Next, respondents could select one of the lifestyle modules providing personalized advice. More than 60% of the participants failed to meet the guidelines for more than one lifestyle behavior. The physical activity module was the most popular, followed by the smoking and fruit modules. Young adults tended to prefer the physical activity and fruit modules, whereas the vegetable module was more popular among older adults. No consistent pattern was identified for the alcohol and smoking modules. The results support the authors’ hypothesis that health preventive behaviors—in particular, physical activity—would be preferred. Although this could imply that physical activity could serve as a gateway behavior when aiming at multiple behavior changes, it is also conceivable that other mechanisms, such as the actual success of behavior change, or the fact that people can choose, may increase chances of multiple behavior change. Hence, mechanisms leading to multiple behavior change need to be further explored.     

Assessing Ghana’s eHealth workforce: implications for planning and training

Background

eHealth—the proficient application of information and communication technology to support healthcare delivery—has been touted as one of the best solutions to address quality and accessibility challenges in healthcare. Although eHealth could be of more value to health systems in low- and middle-income countries (LMICs) where resources are limited, identification of a competent workforce which can develop and maintain eHealth systems is a key barrier to adoption. Very little is known about the actual or optimal states of the eHealth workforce needs of LMICs. The objective of this study was to develop a framework to characterize and assess the eHealth workforce of hospitals in LMICs.

Methods

To characterize and assess the sufficiency of the workforce, we designed this study in twofold. First, we developed a general framework to categorize the eHealth workforce at any LMIC setting. Second, we combined qualitative data, using semi-structured interviews and the Workload Indicator of Staffing Needs (WISN) to assess the sufficiency of the eHealth workforce in selected hospitals in a LMIC setting like Ghana.

Results

We surveyed 76 (60%) of the eHealth staff from three hospitals in Ghana—La General Hospital, University of Ghana Hospital, and Greater Accra Regional Hospital. We identified two main eHealth cadres, technical support/information technology (IT) and health information management (HIM). While the HIM cadre presented diversity in expertise, the IT group was dominated by training in Science (42%) and Engineering (55%), and the majority (87%) had at least a bachelor’s degree. Health information clerk (32%), health information officer (25%), help desk specialist (20%), and network administrator (11%) were the most dominant roles. Based on the WISN assessment, the eHealth workforce at all the surveyed sites was insufficient. La General and University of Ghana were operating at 10% of required IT staff capacity, while Ridge was short by 42%.

Conclusions

We have developed a framework to characterize and assess the eHealth workforce in LMICs. Applying it to a case study in Ghana has given us a better understanding of potential eHealth staffing needs in LMICs, while providing the quantitative basis for building the requisite human capital to drive eHealth initiatives. Educators can also use our results to explore competency gaps and refine curricula for burgeoning training programs. The findings of this study can serve as a springboard for other LMICs to assess the effects of a well-trained eHealth workforce on the return on eHealth investments.

     

Can eHealth tools enable health organizations to reach their target audience?

Data from the health risk assessment operated by the Heart and Stroke Foundation found users were more likely to be female; married; have completed post secondary education; and report hypertension, stroke, or being overweight or obese. In developing and operating eHealth tools for health promotion, organizations should compare users to their target population(s). eHealth tools may not be optimal for reaching some higher-risk sub-groups, and a range of social marketing approaches may be required.     

Predicting Continuance—Findings From a Longitudinal Study of Older Adults Using an eHealth Newsletter

While eHealth technologies are promisingly efficient and widespread, theoretical frameworks capable of predicting long-term use, termed continuance, are lacking. Attempts to extend prominent information technology (IT) theories to the area of eHealth have been limited by small sample sizes, cross-sectional designs, self-reported as opposed to actual use measures, and a focus on technology adoption rather than continuance. To address these gaps in the literature, this analysis includes empirical evidence of actual use of an eHealth technology over the course of one year. This large (n = 4,570) longitudinal study focuses on older adults, a population with many health needs and among whom eHealth use may be particularly important. With three measurement points over the course of a year, this study examined the effects of utilitarian and hedonic beliefs on the continued use of an eHealth newsletter using constructs from IT adoption and continuance theories. Additional analyses compared the relative strength of intentions compared to earlier use in predicting later use. Usage intention was strongly predicted by both hedonic beliefs and utilitarian beliefs. In addition, utilitarian beliefs had both direct effects on intention and indirect effects, mediated by hedonic beliefs. While intention predicted subsequent use, earlier use was a significantly stronger predictor of use than intention. These findings make a theoretical contribution to an emerging literature by shedding light on the complex interplay of reasoned action and automaticity in the context of eHealth continuance.