You're facing critical healthcare decisions. How do you guarantee data accuracy and reliability?

In my experience, one can start with the updated guidelines relevant to the clinical case encountered. In particular, we have to take into consideration the applicability of these guidelines to our patient population. Sometimes, the clinical scenario can be more complex and this requires multidisciplinary management and doing a more specific search to find the most suitable answer.

Entendo que análise crítica dos dados coletados é imprescindível para a tomada de decis?o. Compreender a forma de coleta de dados e como s?o analisados fará a diferen?a quando seu dado for comparados com as diretrizes e dados de outras institui??es em um brainstorming por exemplo. Como em análise estatística, considero ter aten??o para n?o acabar manipulando dados de forma inadequada apenas para se ter um “Indicador bonito”. Certamente isso n?o agregará valor e nem garantirá melhoria em processos.

With the advent of technology, data can flow from one place to another at an unprecedented speed. However, validating the accuracy of the data poses a huge challenge for healthcare teams.To ensure the reliability of the data,one should only advocate the use of crucial data from reliable sources.It is important to have a process to fact-check or validate the presented data. The sources & reliability may slightly differ based on the subject at hand & the Subject Matter Expert references. It's important to vet the information if it seems too good to be true. For example,a pharmaceutical company or their marketing team may not be a reliable source of data about their own products. You may get the information from them but cross-check to verify.

While I agree that the data sources listed above are reliable, I dont see any reason why an organisation's own data should not be reliable. As a healthcare manager and leader it is your responsibility to collect reliable data and generate insights form it and make reliable decisions.

To guarantee data accuracy and reliability when facing critical healthcare decisions, implement a robust data management system that includes validation and verification processes. Ensure that data is collected from reliable sources and entered accurately by trained personnel. Regularly audit and review data for consistency and completeness. Use data quality tools and software to detect and correct errors or discrepancies. Implement standardized procedures for data entry and reporting to minimize variability. Educate and train staff on best practices for data management and the importance of accuracy. Establish a protocol for regularly updating and maintaining data to reflect the most current and relevant information.

Regular internal and external audits, spot checks as well as elaborate data quality and governance arrangements are critical in generating confidence in the data provided and subsequent decisions.

Using human as well as artificial intelligence to complement each other can go a long way in achieving this goal. Technology is developing at an alarming pace and I would advise a word of caution here. Rather than looking for the perfect latest tool, learn to use the tool you already have perfectly!

Staff need to clearly understand the value of good data entry. On one hand it can help improve outcomes and on the other poor data quality can cost lives. This is serious business and needs to be part of the ethos/culture of the organisation.

Improving patient and family involvement in data entry and scrutiny can go a long way in improving safety and quality of care. Good processes in this regard provide valuable insights that can play a critical role in decision making and enhancing quality and safety of care.