Related Experiment Video
Updated: Dec 25, 2025
08:12
Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research
Published on: February 16, 2024
14.5K
'Without data, you're just another person with an opinion'.
Katarzyna Kolasa1, Wim Goettsch2, Guenka Petrova3
1Head of Department of Health Economics and Healthcare Management, Akademia Leona Kozminskiego - Health Economics and Healthcare Management, Warszawa, Poland.
Expert Review of Pharmacoeconomics & Outcomes Research
|April 7, 2020
Summary
Data analytics can prevent healthcare system bankruptcy by providing valuable insights. Advanced methods like machine learning and AI can optimize healthcare technology assessment and personalized medicine.
Area of Science:
- Healthcare Management
- Health Informatics
- Data Science
Background:
- The digital transformation has amplified the importance of data in healthcare.
- There is a need to explore data's potential to improve healthcare system financial stability.
Purpose of the Study:
- To review evidence on how increasing data can alter healthcare technology assessment.
- To explore data's role in a more holistic decision-making process and waste reduction.
Main Methods:
- Review of published examples demonstrating data utilization in healthcare.
- Analysis of data streams from electronic medical records, IoT, wearables, and mobile applications.
- Application of methodologies like Social Network Analysis (SNA), machine learning, and natural language programming.
Main Results:
- Growing data streams offer valuable insights for healthcare financial sustainability.
- Advancements in data analysis enable better assessment of healthcare technologies.
- Big Data facilitates conditional coverage schemes for personalized healthcare technologies.
Conclusions:
- Data-driven insights are crucial for preventing healthcare system bankruptcy.
- Interoperability remains a key challenge for future data-driven healthcare.
- AI-based pricing schemes represent a future frontier for payers.
Related Concept Videos
01:11How Data are Classified: Categorical Data
42.1K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
42.1K
00:59How Data are Classified: Numerical Data
36.1K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
36.1K
01:08Data Collection by Observations
14.3K
Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
14.3K
01:31Confirmation Biases
7.6K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
7.6K
01:13Data Collection by Experiments
26.8K
Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
An example of the experimental method is a public...
26.8K
00:56Nominal Level of Measurement
36.5K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal...
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal...
36.5K