Use of Data Analytics to Promote Optimum Staff-Patient Ratios

Answer

Use of Data Analytics to Promote Optimum Staff-Patient Ratios

Data analytics have important applications in transforming healthcare facilities. It is hereby proposed that healthcare data analytics can help in the development of safe staffing levels and/or optimum staff-patient ratios in a given healthcare facility. The data to be used will be retrieved from electronic healthcare records (EHRs). The aim of this transformation is to promote delivery of high-quality and safe health care and services.

Please write clearly in a manner that directly addresses the task. E.g. instead of writing it is hereby proposed, address in a manner related to the question: You have been asked to give an example. So direct approach would be something like: An example of a health care analytics discussed in this paper is….then proceed to proceed to explain your example.

Also, adopt a clearer and logical structure: since you have several paragraph, you could have just four string paragraphs for each question: The example: The data used: Types of users and: Benefits.

Emphasis is on direct approach that seem to cleverly and strategically repeat/use some words from the question:

(E.g. where you have been asked the type of data used in the example you have given. A direct writing style will be….:  For xxxx analytics, the type of data used is…. Then you proceed to explain). The tense also matter.

Healthcare analytics can be used to develop an optimum staff-patient ratio in a hospital that contributes to delivery of high-quality and safe care. Clinical data collected from electronic medical records will be used to ascertain optimum number of staff required in a hospital. Healthcare consumer-oriented data that can help inform shared decisions in determination of safe staffing ratios include patient experiences, timeliness and effectiveness of care, readmissions, hospital-based mortality rate, payment and value of care (Centers for Medicare & Medicaid Services, 2018). Clinical data can help in develop of statistics on omissions of missed care including that can be attributed to nurse staffing inadequacy (Griffiths et al., 2018). As such number of nurses in a particular area can be identified that will help addressed omission of care issues. Avoid using a phrase so many times. Repetition shows limited vocabulary and cause monotony. See the pink highlights.

Data analytics can help identify specific number of healthcare professions that are required from different specialties that will result in provision of high quality and safe care. Users who can access and use data analytical tools to promote development of shared decision in  establishment of safe staffing levels include doctors, financial department, nurses, hospital administration and/or management, the government agencies, patients, and records department.

Safe staffing level is beneficial to the hospital and the patient. First of all, it enables the hospital to eliminate redundancy in staff in which one department or specialty may be having large number of employees than necessary; whereas, other departments may understaffed. Elimination of staffing redundancies can also help in reduction of hospital costs and promotes delivery of cost-effective care. Lowering of readmission rates is one of the hospital performance measures that can be improved by safe staffing levels (Stefan et al., 2013). On the other hand, safe staffing level can help in prevention or reduction of omissions of nursing care. Omissions of nursing care include aspects such as care left undone, referred as missed cared, and or rationed care (Griffiths et al., 2018).

Improvement of quality and safety of care will lead to increased number of patients being served in the hospital per year, which can result in higher revenues. Patients will benefit from staffing changes influenced by data analytic since they will receive higher quality and safer care in the hospital compared to the past. It has been also established that there is a close association between number of nurses in a given hospital and the number of reported healthcare associated infections. Healthcare associated infections include all infections that are acquired by the patient as she/he receives treatment and care in a given healthcare facility. Hospital-associated infections can have serious safety implications on the patient (Shang, Stone, & Larson, 2015).

In conclusion, data analytics can help inform establishment of safe staffing levels and/or optimum staff-patient ratios. Useful clinical data will be retrieved from electronic patient records. Stakeholders such as doctors, financial department, nurses, hospital administration and/or management, the government agencies, patients, and records department will be involved to promote shared-decision making culture in making of staffing changes. Benefits of making this change include lower readmission and/or hospital-associated infection rates, increased patient flows to the hospitals due to improved trust, prevention of omissions of care, and basically improvement of quality and safety of care.