¿Cómo puede asegurarse de que sus datos sean representativos del mundo real en el quirófano?

A good definition of the problem, the perimeter of action, the targeted results is necessary to any operational research approach. You start by defining the problem in hand, in some cases it may be useful to break it down to sub-problems that are easier to solve. Then formulating the hypothesis that are assumed in your problem solving approach together with the objectives, constraints and significant criteria for evaluation. This helps you have a clear idea on what kind of data you need, you will be producing and how to evaluate its relevance.

It helps to have an "operational expert" to work with. Someone who has years of experience living and working on the problem. These people have an intuitive sense of what is "real" and what is not. Unfortunately, most OR analysts tend to not like these characters because they seem to only ever say "I can't tell you why, but that ain't right" or "Everybody knows that". In most cases, the trick is finding what the expert thinks "everybody knows" and applying OR analysis to that. And if it "ain't right", go back and check your assumptions.

Problem formulation should considered the most critical stage since it affects, in large part, the subsequent course of action. Care must be undertaken to ensure that problem formulation reduces the probability of solving the wrong problem in the subsequent course of action.

In order to ensure data is being used “correctly” in any model you have to understand the model, how the data is used, what is explicit, what is implied. What data is used in some aggregate form? Does the model use "rheostats" to adjust output? Can you compare model output to historical data? In campaign modeling, despite my best attempts to ensure the data was properly used, we always caveated our results as being comparative and not predictive.

Starting with the end in mind is very important! Begin with a clear vision of your objectives helps to define the perimeter, the targeted results, the direction to take and the data you need to collect

Relevant data is usually never located in a single place. OR scientists or analysts tend to put the blame on data not being ready to use. However, it is a fundamental need of developing skills to mine right data and being able to process it, till it is made useful.

Choose appropriate data sources and methods: Primary data: Collected directly by you or your team Offers detailed control but can be time-consuming and expensive. Secondary data: Existing data from databases, reports, or external sources. Faster and cheaper but may lack detail or accuracy. Qualitative data: Descriptive, subjective, or categorical (e.g., customer feedback). Provides insights into motivations and behaviors. Quantitative data: Numerical, objective, or continuous (e.g., sales figures). Enables statistical analysis and model building. Experimental data: Generated through controlled experiments or simulations. Offers high control but may not perfectly reflect real-world conditions.

Use a fast feedback and replacement mechanism. Every data consist of biases. Know these biases and explain them to your team before eliminating them. Consult the academics as well as industry veterans in the field. If possible, try to learn how a nation keep track of its monetary mechanisms. That's a really challenging problem and lot can be learned from these sources

The selection of data is a critical stage, where it is necessary to ensure that the data adhere to the parameters defined in "Problem Structuring". Multi-Criteria Decision Analysis (MCDA) is a valuable tool in this process. The choice of suitable methods for the selection and analysis of data is fundamental and may even involve the creation of a custom analysis model based on the decision-makers' preferences.

Defining the problem correctly is important for the following reasons: It is necessary to produce correct solutions. Misdefining the problem can lead to the solution making the problem worse instead of solving it. It ensures the efficient use of time and resources. Correctly defining the problem ensures that the information and resources needed for the solution are used in the right way. It increases the feasibility of the solution. It ensures that the conditions required for the implementation of the solution are met.

The key point to remember at this stage is that no matter how good your OR model is, if your data is full of inconsistencies, the results will reflect that as well and will not be accurate or reliable. The principle of garbage in garbage out applies. Thus the need to carefully clean your data and be sure its significant to the problem you are trying to solve

Once again biases play a big role here. Many new practitioners do not realize the meaning of normalization of the data. In many cases try to go really basic. Check skewness, correlations, aic/bic tests, check for specific errors like heteroscedastic errors etc.

One thing i found when i analyze data, 70 to 80% of my time was spent on data cleaning. Most especially if the data came from other department/s or staff.I made sure i kept the original data in order for me to compare from my new clean data, in case needed.

Distinguishing between data preparation and data filtering is essential. Preparation involves selecting data according to previously defined criteria, ensuring that the data presentation is clear, without altering the outcomes. Filtering, on the other hand, suggests that the selection criteria were not adequately defined, impacting the results and analyses directly.

Data cleaning: Check for missing values, outliers, and inconsistencies. Data validation: Verify data accuracy against reliable sources. Data bias: Identify and address potential biases in data collection or selection.

The following measures can be taken to prevent noise factors: Gaining information from different sources to develop a multifaceted perspective. Evaluating the problem from different angles. Using critical thinking skills to find the real causes of the problem.

Understand your audience first before creating fancy visualizations and summarization. Sometimes simple visualizations help people understand the data better than complex graphs.

Visualization of data has always been a useful way of highlighting the similarities and differences as well as focusing on the targets we aim for. It can simplify and guide decision making and also fuel fruitful discussions during analysis meetings

Always keep the audience in mind. Use their perspective and skill levels to understand data.Be simple as possible. You cannot go beyond three dimension yet it is your everyday job to see and engineer through multidimensional data. Being really simple and explaining the complex data to an average person will take high levels of effort. Practice on these skills.

Visualization plays a pivotal role in bolstering the decision-making process as it bestows a plethora of intuitive insights into the intricate web of data, thereby enabling them to make well-informed decisions that are grounded in a deep understanding of the underlying information. The power of visualization lies in its iterative, which allows for ongoing refinement of data representation, paving the way for continuous improvement in the way information is portrayed. This iterative process encompasses a range of activities, from the meticulous honing of hypotheses to the thorough exploration of data, all the way to the subtle tweaks and modifications made to models based on the valuable insights gained from the visualized information.

Use feedback loops as much as possible. If possible, use your engineering skills to make them self maintainable. Specially when you design systems for manufacturing and encounter multi staged kanbans, synchronous assembly lines, buffers in transfer lines, keep these feedbacks very simple. And never check 2 things in one loop.

The validation of results is essential to ensure coherent and well-founded analyses. It is important that these definitions and validation processes be established before obtaining the data, to minimize biases and ensure the academic and scientific integrity of the data used.

Also incorporate real-world complexities: Consider dynamic environments, stochastic factors, and uncertainty in real-world systems. Utilize robust optimization models that can handle data uncertainties and variations. Employ sensitivity analysis to understand how model results change with data fluctuations.

Data validation is important for a number of reasons, including: It helps to ensure that data is accurate and reliable. This is essential for making informed decisions and taking corrective action. It helps to identify and correct errors. This can help to improve the quality of data and reduce the risk of errors in downstream processes. It helps to ensure that data is compliant with regulations. This is important for organizations that operate in regulated industries. Data validation can be performed manually or automatically.

Ethical considerations for data exploration and visualization must be emphasized. It is essential to keep in mind data privacy, security, and potential biases in the data. Transparent communication about limitations and uncertainties in the data ensures that stakeholders have a realistic view of the insights derived from exploration and visualization processes.

I think these are good points to make sure one’s analysis provides well characterized results in answering an analytic question. However, I think the AI missed the boat with this structure for how to ensure the data that support the analysis is representative of the real world. I think the topics should have been more like Objective data and measurement error Subjective data, SMEs, and bias Sampling and uncertainty Synthetic data and data cleansing Appropriate model fidelity to contextualize data for the problem statement at hand