Statistical Analysis of the Workload on the LVHN Observation Unit

Abstract The Lehigh Valley Health Network (LVHN) Observation Unit, located at the Cedar Crest Campus, is claimed to be understaffed, currently operating with approximately one nurse responsible for six patients. A time study, with the objective of recommending an appropriate patient-nurse ratio for the LVHN Observation Unit confirmed the validity of this claim. Background The LVHN Observation Unit (OU) is an extension of the LVHN Emergency Department (ED) and Hospital Medicine Service. This OU is where patients are sent after ED admission if they are not sick enough to require an inpatient admission, but still require medical attention before they can be discharged [1]. These patients rarely have life threatening symptoms, but are more likely to struggle with pain or discomfort [1][2]. However, after some observation, about 12% of LVHN OU patients require inpatient admission for further treatment [2]. The OU has thus proven to be an effective tool in reducing unnecessary inpatient admissions at LVHN [2]. However, for an OU to be considered as such, patients may not stay on the unit for more than 48 hours, and documented nurse or doctor treatment is required every hour [3]. Most of these patients are present in the OU for 6-24 hours, with an average length of stay of 23.6 hours [2][3]. In other Med/Surg units, patients tend to have a length of stay closer to four days [2]. It is believed that the OU patients require more work from nurses because the nurses have 48 hours to complete work that nurses in other units have 96 hours to spread out. Also, nurses in the OU claim that they chart patients twice as much as Med/Surg nurses do [2]. For these reasons, the OU staff believes that they should be responsible for fewer patients per nurse than other Med/Surg unit nurses are. However, OU nurses are currently responsible for 6-8 patients, while Med/Surg unit nurses are responsible for 5-6 patients [2]. In the opinion of the OU staff, 4-5 patients per nurse is an appropriate patient-nurse ratio for the OU [2]. Methodology This time study was designed so that direct observation of nurses on two different medical units at the LVHN Cedar Crest Campus could be translated into meaningful time-based data. Twelve hours of observation were conducted on both the Observation Unit (5C) and a comparable Medical-Surgery Unit (7BP). A set of typical tasks (Chart 1) and procedures was created in order to differentiate between medical value added to a patient and other occupational tasks required of the nurses. Care Process Non-Care Process Other Process Patient Care (PC) Paper Work (PW-X) Start (S) Check Rates (CR) Computer Work (CW-X) End (E) Supply (SU) Nurse Consult (NC-X) Idle (I) Paper Work (PW) Doctor Consult (DC-X) Trade Off w/ Coworker (TO) Computer Work (CW) Family Consult (FC-X) Medicine Delivery (CM) Employee Consult (EC-X) Emergency Response (BR) Use Phone (UP-X) Use Phone (UP) Nurse Consult (NC) Doctor Consult (DC) Family Consult (FC) Chart 1: Nurse tasks and procedures, differentiated as either a care, non-care, or other process. The process identifier/code in parenthesis was used for simplicity when observing many processes in continuous flow. Medical value added to a patient can be defined as any operation that directly treats, concerns, or relates to one specific patient. For example, an instance of computer work adding medical value to a patient could be updating medical charts for a patient. An example of computer work that doesn’t add value to a patient (non-care process) could be responding to an email. Along with the process identifier, the start and end time of each process, and the room number of the patient involved were recorded. The observed nurses were chosen simply through assignment by the present Charge Nurse of the unit. These observations were performed on 6 nurses in the 5C unit and 4 nurses in the 7BP unit, totaling 24 observed hours and 692 observed processes on 57 patients. Elapsed time data was collected in minutes, and was retrieved from calculating the difference between the start time and the end time of each task. All of the collected data was stored in a Microsoft Excel workbook. The data was then manipulated in many ways in order to run two-sample t-tests through Minitab, a statistical analysis software. This two-sample t-test was chosen because it highlighted the differences between the 7BP and 5C data sets. Results In the 10 different observation periods on the 5C and 7BP, the 5C operated on average at a 6:1 patient-nurse ratio, comparing to the 7BP at a 5.25:1 patient-nurse ratio. In addition, 20 different t-tests were conducted, providing metrics such as mean, standard deviation, sample size, and p-values of the two data sets. These tests were designed to compare the amount of work that one nurse in each unit is responsible for. The results are as follows: Comparison Stat 5C 7BP Difference/p-value 1) Time spent between tasks SS 402 214 No Mean 0.24876 0.24299 STDV 0.84323 1.137 0.948 2) Percent of observation period spent on one patient in patient care SS 36 21 Yes Mean 11.325% 15.539% STDV 7.343% 7.034% 0.038 3) Duration of time spent per patient care interaction SS 81 41 Yes Mean 2.7058 6.3659 STDV 2.3331 4.927 4) Duration of time spent per computer work interaction SS 154 101 Yes Mean 1.1805 1.83 STDV 1.1111 1.8651 0.002 5) Duration of time spent per idle period SS 34 16 No Mean 2.5098 3.1771 STDV 2.154 2.8031 0.408 6) Duration of time spent per nurse consult interaction SS 36 36 Yes Mean 1.4444 2.8333 STDV 1.2916 1.9011 0.001 7) Percent of observation period spent on computer work SS 6 4 No Mean 24.613% 25.642% STDV 6.860% 6.625% 0.82 8) Percent of observation period spent on Patient Care SS 6 4 No Mean 29.427% 36.012% STDV 11.208% 16.401% 0.522 9) Percent of observation period spent on consult SS 6 4 No Mean 11.170% 18.438% STDV 5.603% 8.414% 0.204 10) Percent of observation period spent on Supplies SS 6 4 Yes Mean 7.578% 2.608% STDV 3.407% 2.107% 0.025 11) Percent of observation period spent on paper work SS 6 4 Yes Mean 2.473% 0.713% STDV 1.141% 0.744% 0.021 12) Percent of observation period spent on phone SS 6 4 No Mean 7.108% 2.280% STDV 8.182% 2.615% 0.227 13) Amount of medicine checks per patient SS 36 21 No Mean 0.05556 0.04762 STDV 0.23231 0.21822 0.898 14) Amount of computer work instances per patient SS 36 21 No Mean 4.27780 4.80950 STDV 3.37730 2.71330 0.518 15) Amount of consults per patient SS 36 21 Yes Mean 0.50000 1.33333 STDV 0.77460 1.15470 0.006 16) Amount of patient care instances per patient SS 36 21 No Mean 2.22222 2.05000 STDV 1.86870 1.60510 0.719 17) Amount of paper work instances per patient SS 36 21 Yes Mean 0.47222 0.14286 STDV 0.77408 0.35857 0.034 18) Amount of supply retrieval instances per patient SS 36 21 No Mean 0.66667 0.42857 STDV 0.98561 0.74642 0.308 19) Amount of phone calls per patient SS 36 21 No Mean 0.22222 0.04762 STDV 0.48469 0.21822 0.068 20) Amount of beep responses per patient SS 36 21 No Mean 0.11111 0.19048 STDV 0.52251 0.40237 0.524 Table of results of 20 t-tests. SS=Sample Size. STDV= Standard Deviation. The difference column signifies whether or not the tests showed a conclusive statistical difference between the 5C and the 7BP in the given metric. Note: Numbered comparisons correspond to numbered list in conclusion section. Assessment The following can be summarized in regards to the work done by nurses in the 5C and 7BP units from evaluating Chart 2: Both units spend approximately the same amount of idle time in between processes. Nurses in the 7BP unit spend a higher percentage of shift time on direct patient care of any given patient than do nurses in the 5C unit. Individual periods of direct patient care in the 7BP unit take more time to conduct than they do in the 5C unit. Individual periods of computer work in the 7BP unit take more time to conduct than they do in the 5C unit. Individual idle periods in the 7BP unit are lengthier than they are in the 5C unit. Individual periods of nurse consultation in the 7BP unit take more time to conduct than they do in the 5C unit. Both units spend approximately the same percentage of shift time on computer work. Nurses in the 7BP unit spend a higher percentage of shift time on direct patient care than do nurses in the 5C unit. Nurses in the 7BP unit spend a higher percentage of shift time consulting either a doctor, nurse, patient’s family member, or other employee than do nurses in the 5C unit. Nurses in the 5C unit spend a higher percentage of shift time gathering supplies for a patient than do nurses in the 7BP unit. Nurses in the 5C unit spend a higher percentage of shift time completing patient paper work than do nurses in the 7BP unit. Nurses in the 5C unit spend a higher percentage of shift time on their work cell phone than do nurses in the 7BP unit. Nurses in both units delivered or checked any given patient’s medicine approximately as frequently as each other. This process in itself was performed rarely in both units. Nurses in both units completed any given patient’s computer work approximately as frequently as each other. Nurses in the 7BP unit consulted either a doctor, nurse, patient’s family member, or other employee in regards to any given patient more frequently than did nurses in the 5C unit. Nurses in both units conducted direct patient care on any given patient approximately as frequently as each other. Nurses in the 5C unit completed any given patient’s paper work more frequently than did nurses in the 7BP unit. This process in itself was performed rarely in both units. Nurses in the 5C unit retrieved supplies for any given patient more frequently than did nurses in the 7BP unit. Nurses in the 5C unit completed more phone calls for any given patient more frequently than did nurses in the 7BP unit. Nurses in the 7BP unit responded to patient emergencies more frequently than did nurses in the 5C unit. However, this was out of the nurses’ control as they cannot determine when a patient decides to request for immediate medical attention. These summaries reveal an encompassing conclusion about the nurses’ comparative workload in the two units. The units share a similar workload, although the work was completed in different fashions. The 5C unit’s nurses conduct many short tasks more frequently, while the 7BP unit’s nurses conduct longer, less frequent tasks. This is supported by the evidence that each floor was observed for 12 hours, but 437 processes were observed in the 5C unit, while only 250 processes were observed in the latter. However, the average process in the 5C unit only lasted 1.65 minutes while the average process in the 7BP unit lasted 2.88 minutes. Also, both floors spent similar percentages of their day idle, which suggests that both floors have similar workloads. For these reasons, it can be concluded that the patient-nurse ratios in the two units should be equivalent. It is recommended that the 5C OU operate at a 5:1 patient-nurse ratio. There were a few limitations of this time study. Firstly, twelve hours of observation time on each floor is not enough time to make conclusive decisions on how the 5C unit should be staffed. Secondly, the timing of the processes was only accounted for in minutes, depriving the study of possible levels of precision that could have been reached if accounted for in seconds. It is recommended that this study be expanded for longer periods of observation, as well as conducted using a more precise unit of measurement. References [1] Martinez, E., Reilly, B., Evans, A., & Roberts, R. (2001). The observation unit: a new interface between inpatient and outpatient care. The American Journal of Medicine, 110(4). http://dx.doi.org/10.1016/S0002-9343(00)00710-5 [2] Teets, C. (2014, June 26). [Personal interview by M. Kashkoush]. [3] Aston, G. (2012, February 1). Observation units: A tightrope act. Retrieved July 15, 2014, from Hospital and Health Networks website: http://www.hhnmag.com/display/ HHN-news-article.dhtml?dcrPath=/templatedata/HF_Common/NewsArticle/data/HHN/Magazine/2012/Feb/ 0212HHN_FEA_clinica

Knowledge Discovery Models for Product Design, Assembly Planning and Manufacturing System Synthesis

The variety of products has been growing over the last few decades so that the challenges for designers and manufacturers to enhance their design and manufacturing capabilities, responsively and cost-effectively are greater than ever. The main objective of this research is to help designers and manufacturers cope with the increasing variety management challenges by exploiting the data records of existing or old products, along with appropriate Knowledge Discovery (KD) models, in order to extract the embedded knowledge in such data and use it to speed-up the development of new products. Four product development activities have been successfully addressed in this research: product design, product family formation, assembly sequencing and manufacturing system synthesis. The models and methods developed in this dissertation present a package of knowledge-based solutions that can greatly support product designers and manufacturers at various stages of the product development and manufacturing planning stages. For design retrieval; using efficient tree reconciliation algorithms found in Biological Sciences, a novel Bill of Materials (BOM) trees matching method was developed to retrieve the closest old design and discover components and structure shared with new product design. As a further application to BOM matching, an enhanced BOM matching method was also developed and used for product family formation. A new approach was introduced for assembly sequencing, based on the notion of consensus trees used in evolutionary studies, to overcome the critical limitation of individual assembly sequence retrieval methods that are not able to capture the assembly sequence data for a given new combination of components that never existed before in the same product variant. For manufacturing system synthesis; a novel Integer Programming model was developed to extract association rules between the product design domain and manufacturing domain to be used for synthesizing a manufacturing/assembly system for new products. Examples of real products were used to demonstrate and validate the developed models and comparisons with related existing methods were carried out to demonstrate the advantages of the developed models. The outcomes of this research provide efficient, and easy to implement knowledge-based solutions for facilitating cost-effective and rapid product development activities

Analysis of the top 100 most influential papers in benign prostatic hyperplasia.

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Optimum Overall Product Modularity

AbstractModularity in product architecture is beneficial to both product development and manufacturing. Several methods exist for clustering product components into modules all of which, with few exceptions, do not consider the hierarchical structure of the product. Products architecture consists of a number of hierarchical levels, which add a useful dimension to modularity analysis. Designing products architecture that maximizes modularity over all levels of the product structure (i.e. overall modularity) is the main objective of this work. Interactions between various product components are represented using a Design Structure Matrix (DSM). The product architecture is represented by product structure tree in the form of a binary rooted tree. A novel Mathematical Programming Model is developed to construct the corresponding product structure tree for a given product which ensures optimal modularity at all hierarchical levels, without prior knowledge of their number. The proposed optimal modular product architecture design method is demonstrated using a real product case study. Optimal overall modularity leads to better management of product changes and variety and more cost-effective product development and manufacturing

SIGMOD '19 Proceedings of the 2019 International Conference on Management of Data / A Scalable Index for Top-k Subtree Similarity Queries

Given a query tree Q, the top-k subtree similarity query retrieves the k subtrees in a large document tree T that are closest to Q in terms of tree edit distance. The classical solution scans the entire document, which is slow. The state-of-the-art approach precomputes an index to reduce the query time. However, the index is large (quadratic in the document size), building the index is expensive, updates are not supported, and data-specific tuning is required. We present a scalable solution for the top-k subtree similarity problem that does not assume specific data types, nor does it require any tuning. The key idea is to process promising subtrees first. A subtree is promising if it shares many labels with the query. We develop a new technique based on inverted lists that efficiently retrieves subtrees in the required order and supports incremental updates of the document. To achieve linear space, we avoid full list materialization but build relevant parts of a list on the fly. In an extensive empirical evaluation on synthetic and real-world data, our technique consistently outperforms the state-of-the-art index w.r.t. memory usage, indexing time, and the number of candidates that must be verified. In terms of query time, we clearly outperform the state of the art and achieve runtime improvements of up to four orders of magnitude.(VLID)441194