half page each question one reference Q1 ? All HCOs should continuously strive for QI. According to the Institute of Medi

half page each question one reference

Q1

All HCOs should continuously strive for QI. According to the Institute of Medicine, quality healthcare is defined as “the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.”  I agree with this definition of quality healthcare. The outcome of the care the patient experiences after treatment should meet or exceed their expectations.

            However, this is not always the case. Patients have negative experiences that impacts their quality of life. According to Strome (2013), value should always be defined in relation to the customer, in this case patients. If patients don’t experience a positive outcome by the care they receive, it’s not valuable to them. In order to improve the quality of care patients receive, an HCO must measure and analyze their data. Quality must be measured, monitored, and analyzed. Healthcare managers can use this data to make decisions and act to improve the quality of care patients receive.

            Healthcare is continuously in a state of transformation. Because of continuous changes that are occurring in healthcare, HIT must keep up and ensure that the data contains some value for those who use it. According to Strome, (2013) there are four main activities associated with maintaining a data system that supports the needs of the HCO. They are data modeling, data creating, data storage, and data usage. All four activities must operate synergistically to ensure that the data is high-quality and can be used by healthcare leaders to make decisions. Data governance is used within an HCO to ensure the integrity of analytics. Healthcare data governance and stewardship can evaluate data quality, identify issues, and make changes as needed (Strome, 2013). They can also maintain storage for the data and ensure that the data is used properly and is accessible to the people that need it.    

Q2

uality healthcare is more than just a popular phrase. Nowadays value-based care moving forward, the focus of inpatient care is shifting to quality and away from quantity. According to the Institute of Medicine (IOM) of the National Academy of Sciences, which defined quality health care as “safe, effective, patient-centered, timely, efficient and equitable.” In addition, the Agency for Healthcare Research and Quality (AHRQ) defines quality health care “as doing the right thing for the right patient, at the right time, in the right way to achieve the best possible results.”

As per my opinion Quality healthcare mean the use of evidence-based practice and increase patient satisfaction. (Steiner, 2017)

Value in health care is the measured improvement in a person’s health outcomes for the cost of achieving that improvement. Often, patients cannot get reliable information on the important outcomes and overall costs of their treatment options. With better information on value — outcomes, satisfaction, and costs — patients could make more confident decisions about getting the care they need while spending no more than necessary. (Niall Brennan, 2021)

If we talk about data governance, it is important because it brings meaning to an organization’s data. Data governance is about managing data and processes so data can be used as a consistent, secure, and organized asset that meets policies and standards. Data governance helps create a shared language. Moreover, it brings people together to collaborate.

There are many other reasons why data governance important

Data Governance Saves Money: Data governance reduces errors in your database, giving your business a solid database to work from and saving precious time that would otherwise be used to correct your existing data. Time saved is money saved.

Bad Data Governance is Risky: Lack of effective data governance is a security concern for 2 reasons: outside security risks associated with dirty, unstructured data, and regulatory compliance issues.