Patient classification system: An integrated method for measuring nursing intensity and optimizing resource allocation

A patient classification system was developed integrating a patient acuity instrument with a computerized nursing distribution method based on a linear programming model. The system was designed for real-time measurement of patient acuity (workload) and allocation of nursing personnel to optimize the utilization of resources.^ The acuity instrument was a prototype tool with eight categories of patients defined by patient severity and nursing intensity parameters. From this tool, the demand for nursing care was defined in patient points with one point equal to one hour of RN time. Validity and reliability of the instrument was determined as follows: (1) Content validity by a panel of expert nurses; (2) predictive validity through a paired t-test analysis of preshift and postshift categorization of patients; (3) initial reliability by a one month pilot of the instrument in a practice setting; and (4) interrater reliability by the Kappa statistic.^ The nursing distribution system was a linear programming model using a branch and bound technique for obtaining integer solutions. The objective function was to minimize the total number of nursing personnel used by optimally assigning the staff to meet the acuity needs of the units. A penalty weight was used as a coefficient of the objective function variables to define priorities for allocation of staff.^ The demand constraints were requirements to meet the total acuity points needed for each unit and to have a minimum number of RNs on each unit. Supply constraints were: (1) total availability of each type of staff and the value of that staff member (value was determined relative to that type of staff's ability to perform the job function of an RN (i.e., value for eight hours RN = 8 points, LVN = 6 points); (2) number of personnel available for floating between units.^ The capability of the model to assign staff quantitatively and qualitatively equal to the manual method was established by a thirty day comparison. Sensitivity testing demonstrated appropriate adjustment of the optimal solution to changes in penalty coefficients in the objective function and to acuity totals in the demand constraints.^ Further investigation of the model documented: correct adjustment of assignments in response to staff value changes; and cost minimization by an addition of a dollar coefficient to the objective function. ^

Environmental sustainability and the human element: Decision making in National Cancer Institute-designated cancer centers and Practice Greenhealth subscribing institutions

Background. Various aspects of sustainability have taken root in the hospital environment; however, decisions to pursue sustainable practices within the framework of a master plan are not fully developed in National Cancer Institute (NCI) -designated cancer centers and subscribing institutions to the Practice Greenhealth (PGH) listserv.^ Methods. This cross sectional study was designed to identify the organizational characteristics each study group pursed to implement sustainability practices, describe the barriers they encountered and reasons behind their choices for undertaking certain sustainability practices. A web-based questionnaire was pilot tested, and then sent out to 64 NCI-designated cancer centers and 1638 subscribing institutions to the PGH listserv.^ Results. Complete responses were received from 39 NCI-designated cancer centers and 58 subscribing institutions to the PGH listserv. NCI-designated cancer centers reported greater progress in integrating sustainability criteria into design and construction projects than hospitals of institutions subscribing to the PHG listserv (p-value = <0.05). Statistically significant differences were also identified between these two study groups in undertaking work life options, conducting energy usage assessments, developing energy conservation and optimization plans, implementing solid waste and hazardous waste minimization programs, using energy efficient vehicles and reporting sustainability progress to external stakeholders. NCI-designated cancer centers were further along in implementing these programs (p-value = <0.05). In comparing the self-identified NCI-designated cancer centers to centers that indicated they were both and NCI and PGH, the later had made greater progress in using their collective buying power to pursue sustainable purchasing practices within the medical community (p-value = <0.05). In both study groups, recycling programs were well developed.^ Conclusions. Employee involvement was viewed as the most important reason for both study groups to pursue recycling initiatives and incorporated environmental criteria into purchasing decisions. A written sustainability commitment did not readily translate into a high percentage that had developed a sustainability master plan. Coordination of sustainability programs through a designated sustainability professional was not being undertaken by a large number of institutions within each study group. This may be due to the current economic downturn or management's attention to the emerging health care legislation being debated in congress. ^ Lifecycle assessments, an element of a carbon footprint, are seen as emerging areas of opportunity for health care institutions that can be used to evaluate the total lifecycle costs of products and services.^

BIOMEDICAL LANGUAGE UNDERSTANDING AND EXTRACTION (BLUE-TEXT): A MINIMAL SYNTACTIC, SEMANTIC METHOD

Clinical text understanding (CTU) is of interest to health informatics because critical clinical information frequently represented as unconstrained text in electronic health records are extensively used by human experts to guide clinical practice, decision making, and to document delivery of care, but are largely unusable by information systems for queries and computations. Recent initiatives advocating for translational research call for generation of technologies that can integrate structured clinical data with unstructured data, provide a unified interface to all data, and contextualize clinical information for reuse in multidisciplinary and collaborative environment envisioned by CTSA program. This implies that technologies for the processing and interpretation of clinical text should be evaluated not only in terms of their validity and reliability in their intended environment, but also in light of their interoperability, and ability to support information integration and contextualization in a distributed and dynamic environment. This vision adds a new layer of information representation requirements that needs to be accounted for when conceptualizing implementation or acquisition of clinical text processing tools and technologies for multidisciplinary research. On the other hand, electronic health records frequently contain unconstrained clinical text with high variability in use of terms and documentation practices, and without commitmentto grammatical or syntactic structure of the language (e.g. Triage notes, physician and nurse notes, chief complaints, etc). This hinders performance of natural language processing technologies which typically rely heavily on the syntax of language and grammatical structure of the text. This document introduces our method to transform unconstrained clinical text found in electronic health information systems to a formal (computationally understandable) representation that is suitable for querying, integration, contextualization and reuse, and is resilient to the grammatical and syntactic irregularities of the clinical text. We present our design rationale, method, and results of evaluation in processing chief complaints and triage notes from 8 different emergency departments in Houston Texas. At the end, we will discuss significance of our contribution in enabling use of clinical text in a practical bio-surveillance setting.