Question

Statistical application and the interpretation of data is important in health care. Review the statistical concepts covered in this topic. In a 750-1,000 word paper, discuss the significance of statistical application in health care. Include the following:

1. Describe the application of statistics in health care. Specifically discuss its significance to quality, safety, health promotion, and leadership.
2. Consider your organization or specialty area and how you utilize statistical knowledge. Discuss how you obtain statistical data, how statistical knowledge is used in day-to-day operations and how you apply it or use it in decision making.

Three peer-reviewed, scholarly or professional references are required.

PLEASE ANSWER IT THOROUGHLY. I HAVE POSITED THIS QUESTION MORE THAN TWICE.

Answer 1

Statistical application and the interpretation of data is important in health care. Review the statistical conc

Statistics refers to both quantitative data, and the classification of such data in accordance with probability theory and the application to them of methods such as hypothesis testing.

Application of statistics in health care

Health statistics include both empirical data and estimates related to health, such as mortality, morbidity, risk factors, health service coverage, and health systems.

The production and dissemination of health statistics is a core WHO activity mandated to WHO by its Member States in its Constitution. WHO programmes compile and disseminate a broad range of statistics that play a key role in advocacy for health issues, monitoring and evaluation of health programmes and provision of technical assistance to countries

You see them all the time in the news - the number of people who were in the hospital last year, the percentage of kids who are overweight, the rate at which people are catching the flu, the average cost of a medical procedure. These are all types of health statistics which are pretty useful.

Health statistics are numbers about some aspect of health. Statistics about births, deaths, marriages, and divorces are sometimes called "vital statistics." Researchers use statistics to see patterns of diseases in groups of people. This can help in figuring out who is at risk for certain diseases, finding ways to control diseases ,deciding which diseases should be studied and whether it is having some hereditary risk factors etc.

  

By identifying statistical trends and trails, health care providers can monitor local conditions and compare them to state, national, and international trends. Health statistics provide empirical data to assist in the allocation of public and private funds and help to determine how research efforts should be focused. Whether considering disease incidence, accidents, cure rates, physician or hospital fees, malpractice, mortality, drugs, treatments, or medical devices, the primary source for statistical health data most often appears on government, international organization, or professional association web sites.

Significance of statistics to quality of health care

The Agency for Healthcare Research & Quality is an agency of the U.S. Department of Health and Human Services whose goal is to “improve the quality, safety, efficiency, and effectiveness of health care for all Americans” by aiding the decision making process. The Agency for Healthcare Research & Quality plans to do this by providing information and evidence on treatment efficacy, individual patient applicability, and cost. The range of statistical offerings is wide covering safety net monitoring, medical expenditure spending, healthcare cost and utilization, hospital statistics, and HIV and AIDS statistics. The Healthcare Cost and Utilization section provides treatment outcomes at the national, state, and local levels. Besides recommendations on topics such as vision screening for children under five years old or the relationship of fewer numbers of nursing staff to poor patient outcomes, this site also offers prevention tools for the clinician to use in selecting medical services by age and gender.

Specific statistical data on health care workers and the outcome of service helps on bringing out the need for more staff members as well as pointing out the need for more efficient health care workers.Data is arranged by geographic area, gender, or disease outbreak helps in identifying the need for allocation of new hospital in that geographic area

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And finally, although most medical professionals do not undertake formal research, they do need to read and interpret research. Medicine is ever changing. Not only are new diagnostic techniques and treatments being developed, but, every now and then, new diseases appear that one had not encountered in the past – for example – severe respiratory distress syndrome, Ebola virus infection, nonalcoholic fatty liver disease, to name a few. Hence, a few years after leaving the medical school, a medical professional's practice of medicine is often quite different from what one started with. To keep abreast of the new developments, one needs to appraise the literature on these and assess their reliability and applicability to one's patients. This requires statistical literacy.

Significance of statistics in healthcare improvment

Medical statistics deals with applications of statistics to medicine and the health sciences, including epidemiology, public health, forensic medicine, and clinical research. Medical statistics has been a recognized branch of statistics in the United Kingdom for more than 40 years but the term has not come into general use in North America, where the wider term 'biostatistics' is more commonly used. However, "biostatistics" more commonly connotes all applications of statistics to biology. Medical statistics is a subdiscipline of statistics. "It is the science of summarizing, collecting, presenting and interpreting data in medical practice, and using them to estimate the magnitude of associations and test hypotheses. It has a central role in medical investigations. It not only provides a way of organizing information on a wider and more formal basis than relying on the exchange of anecdotes and personal experience, but also takes into account the intrinsic variation inherent in most biological processes.

For describing situations where statistics help in improving safety and quality of health care

• Incidence (epidemiology) vs. Prevalence vs. Cumulative incidence

• all types of medical test results can be true positive or false positive and true negative or false negative...these categories are hugely dependent on the prevalence of the disease being tested for; and, for example, a false positive test result is more likely when the prevalence of the disease being tested for is very low (such as a lab test for cold-weather influenza being done in June in South Carolina, USA. and giving a "positive" result).

• Transmission rate vs. force of infection

• Mortality rate vs. standardized mortality ratio vs. age-standardized mortality rate

• Pandemic vs. epidemic vs. endemic vs. syndemic

• Serial interval vs. incubation period

• Cancer cluster

• Sexual network

• Years of potential life lost

• Maternal mortality rate

• Perinatal mortality rate

• Low birth weight ratio

For assessing the effectiveness of an intervention

• Absolute risk reduction

• Control event rate

• Experimental event rate

• Number needed to harm

• Number needed to treat

• Odds ratio

• Relative risk reduction

• Relative risk

• Relative survival

Significance of statistics in improving safety in hearthcare

Medical error statistics are alarming, yet they provide us with the sense of urgency we need – to collectively work together to reduce errors and eliminate preventable deaths from taking place in our hospitals. Taking a look at the statistics most importantly pointsout considerable levels of error that exist in acute care so that we can turn these harrowing facts into opportunities for effective change and patient safety improvement together.

Significance of statistical leadership in healthcare

Statistical leadership is the use of influence to guide a multidisciplinary research team to adopt the best design or decision based on the available data.  

TheThe keys to statistical leadership are competency in listening, networking, and communication. These competencies are often referred to as soft skills because they are personal attributes that enable effective interpersonal interactions. These skills are not easy to acquire and require a commitment to ongoing professional development. When coupled with strong technical capabilities, these attributes are essential for successful collaborative leadership from a statistician working as a part of a larger multidisciplinary health care team. Working on with the statistical data bring about a leadership quality and improve the idea on reading statistical data.

Scientists in every discipline are generating data more rapidly than ever before, resulting in an increasing need for statistical skills at a time when there is decreasing visibility for the field of statistics. Resolving this paradox requires stronger statistical leadership to guide multidisciplinary teams in the design and planning of scientific research and making decisions based on data. It requires more effective communication to nonstatisticians of the value of statistics in using data to answer questions, predict outcomes, and support decision-making in the face of uncertainty. It also requires a greater appreciation of the unique capabilities of alternative quantitative disciplines such as machine learning, data science, pharmacometrics, and bioinformatics which represent an opportunity for statisticians to achieve greater impact through collaborative partnership. Examples taken from pharmaceutical drug develop moment are used to illustrate the concept of statistical leadership in a collaborative multidisciplinary team environment.

In my area of working that is the hospital different statistical data is collected for WHO specified sites and different up-to-date journals.its application is varied on mainly on

• Drug use

• Drug effectiveness

• Vaccine efficiency

• People use of insurance

• Outbreak of epidemc diseases

• Prevalence of different conditions

• Effectiveness of patient care

• Effectiveness of labour