Customer-centered careflow modeling based on guidelines.
ABSTRACT In contemporary society, customer-centered health care, which stresses customer participation and long-term tailored care, is inevitably becoming a trend. Compared with the hospital or physician-centered healthcare process, the customer-centered healthcare process requires more knowledge and modeling such a process is extremely complex. Thus, building a care process model for a special customer is cost prohibitive. In addition, during the execution of a care process model, the information system should have flexibility to modify the model so that it adapts to changes in the healthcare process. Therefore, supporting the process in a flexible, cost-effective way is a key challenge for information technology. To meet this challenge, first, we analyze various kinds of knowledge used in process modeling, illustrate their characteristics, and detail their roles and effects in careflow modeling. Secondly, we propose a methodology to manage a lifecycle of the healthcare process modeling, with which models could be built gradually with convenience and efficiency. In this lifecycle, different levels of process models are established based on the kinds of knowledge involved, and the diffusion strategy of these process models is designed. Thirdly, architecture and prototype of the system supporting the process modeling and its lifecycle are given. This careflow system also considers the compatibility of legacy systems and authority problems. Finally, an example is provided to demonstrate implementation of the careflow system.
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ABSTRACT: The concept of caring is important to both patients and nurses. If patients and nurses perceive caring behaviours differently, patients may not have their needs met or will be dissatisfied with the nursing. This study conducted in mainland China compared the perceptions of nurses and patients concerning nurses' caring behaviours. From November 2011 to June 2012, 680 patients and 540 of their nurses in five hospitals in southern, central and eastern China were invited to complete a descriptive comparative survey with four subcategories, the Caring Behaviors Inventory-24. Respondents scored each of the 24 items on the survey from 1 (low) to 6 (high). Of those invited, 595 patients (87.50%) and 445 (82.41%) nurses completed the survey. The mean item score on the Caring Behaviors Inventory-24 was 4.32 and 4.96 for patients and nurses, respectively. The subcategory with the highest mean score for both groups was knowledge and skills (4.73, 5.25), and the lowest for both groups was positive connectedness (3.98, 4.51). Nurses' scores were significantly higher than those of patients for all four subcategories (P < 0.001). The gap between the two groups indicates that nurses need to improve their understanding and response to patients' actual and perceived needs and expectations. In China, patients require more support from nurses of their psychological needs. Participants came from a limited number of hospitals in three cities. A larger sample from different hospitals in mainland China could have increased the power of the study.International Nursing Review 09/2013; · 0.74 Impact Factor
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ABSTRACT: A clinical process is typically a mixture of various latent treatment patterns, implicitly indicating the likelihood of what clinical activities are essential/critical to the process. Discovering these hidden patterns is one of the most important components of clinical process analysis. What makes the pattern discovery problem complex is that these patterns are hidden in clinical processes, are composed of variable clinical activities, and often vary significantly between patient individuals. This paper employs Latent Dirichlet Allocation (LDA) to discover treatment patterns as a probabilistic combination of clinical activities. The probability distribution derived from LDA surmises the essential features of treatment patterns, and clinical processes can be accurately described by combining different classes of distributions. The presented approach has been implemented and evaluated via real-world data sets.Journal of Medical Systems 04/2013; 37(2):9915. · 1.37 Impact Factor