Patient Classiifcation Tool Design And Nursing Staff Allocation In Neurological Ward In Tertiary General Hospital

Objective：The size and mix of nursing teams are critical elements of efficient healthservice management and workforce planning. The staffing level and skill mix arerecognized as central elements of the current nursing reforms in China. Thesereforms aim at supplying proper nursing staff on the basis of daily nursing workloadand patient’s acuity measurement. The objective of this project was to establish a setof methods to calculate the numbers of different layers of nursing staff. This was adescriptive study, of nursing hour measurement, patient classification and front-linenursing staff configuration study to achieve an overall goal of the effectiveness,flexible and cost-effective use of nursing resources in the neurological ward in atertiary general hospital in China.Methods：①Qualitative interview and questionnaires were used to explore thenurses perception of the nurse staffing and skill mix and to analyze the direct andindirect nursing care delivery status by comparing the nursing care delivery groupedaccording to different years of working experience, educational background andprofessional titles.②Measuring the direct and indirect nursing hours per patient perday in a neurological ward in a tertiary general hospital by convenience sampling.Establishing a patient classification tool based on the daily nursing workload andclassifies the patients into four groups (ABCD).③Expert meeting method was usedto define the qualification criteria, responsibilities, nursing care scope,proper ratiosfor different layers of nursing staff for the purpose of devising a formula of nursingresource allocation in the neurological ward in the tertiary general hospital.Results：①Nursing staff was inadequate when measured by both nurse-bed andnurse-patient ratios. There was no significant difference in the frequency ofimplementing direct nursing work by the nursing staff with different years ofexperience, education background nor professional titles.②The ANOVA analysisshowed that both the patient dependency and acuity classification cannot reflect thenursing workload adequately. Patient classification tool that combined patientdependency, acuity, number of treatment procedures, nursing assessment andnursing education was created and used to classify the patients into four (ABCD)groups. The nursing hours per patient per day for group A is120.11min,group B is111.21min, group C is99.99min and group D is37.13min.③Nursing configurationwas set as the combination of three categories: registered nurse, assistant nurse and health care workers. The staffing formula for direct nursing care is:[（120.11×NA+111.21×NB+99.99×NC+37.13×ND）/480]×1.2×1.46，The percentageof registered nurse, assistant nurse and health care workers was set at50%,15%and35%respectively. The staffing formula for indirect nursing care is:[（12.21min×NABCD）/480]×1.2×1.46. NA, NB, NC,and NDstand for the number ofpatients in each group (ABCD). NABCDis for the total number of patients in allgroups.Conclusions：It is essential to distribute nursing resource reasonably for qualitynursing care and management. The nurse allocation should consider all the factorsinfluencing nursing workload. By doing so we can calculate the needed nursinghours for patient care and configure the nurses properly to improve the quality ofnursing care thus patients satisfaction, while at the same time meet the goals of costcontainment in the hospital.