What are the best sources of data for identifying demand drivers and indicators in healthcare?

2 External data

External data refers to the data that you obtain from sources outside your own organization, such as public health agencies, government databases, research studies, industry reports, and social media. External data is useful because it provides you with a broader and deeper perspective of the factors and forces that influence the demand for health services in your market, environment, and society. You can use external data to understand the demographic, epidemiological, economic, political, and technological trends and changes that affect the health status, behavior, and expectations of your current and potential patients. However, external data may also have some challenges, such as availability, accessibility, compatibility, or timeliness, that may affect its usefulness and relevance. Therefore, you should always verify the source, quality, and currency of your external data before using it for demand forecasting.

3 Primary data

Primary data refers to the data that you collect directly from your target population or stakeholders, such as interviews, surveys, focus groups, or experiments. Primary data is important because it allows you to obtain specific, customized, and original information that addresses your research questions or objectives. You can use primary data to explore the needs, preferences, opinions, and experiences of your patients, providers, partners, or competitors, as well as to test your hypotheses, assumptions, or models. However, primary data may also have some drawbacks, such as cost, time, or feasibility, that may affect its scope and scale. Therefore, you should always design your primary data collection methods carefully, ethically, and rigorously before using them for demand forecasting.

4 Secondary data

Secondary data refers to the data that you use from existing sources that were collected for other purposes, such as academic publications, market research, or media reports. Secondary data is convenient because it saves you the time and effort of collecting your own data, and offers you a large amount of diverse and rich data that may not be otherwise available. You can use secondary data to complement, compare, or contrast your own data, as well as to gain insights, ideas, or benchmarks from other contexts or domains. However, secondary data may also have some risks, such as validity, reliability, or applicability, that may affect its fit and alignment with your demand forecasting goals. Therefore, you should always evaluate the purpose, method, and result of your secondary data sources before using them for demand forecasting.

5 Quantitative data

Quantitative data refers to the data that is expressed in numerical form, such as statistics, measurements, or ratings. Quantitative data is powerful because it allows you to measure, quantify, and compare the demand for health services in an objective, precise, and consistent way. You can use quantitative data to describe, summarize, or infer the characteristics, relationships, or effects of the variables that affect the demand for health services, such as age, gender, income, diagnosis, or treatment. However, quantitative data may also have some limitations, such as complexity, uncertainty, or sensitivity, that may affect its interpretation and implication. Therefore, you should always use appropriate techniques, tools, and assumptions to analyze and visualize your quantitative data before using it for demand forecasting.

6 Qualitative data

Qualitative data refers to the data that is expressed in non-numerical form, such as words, images, or sounds. Qualitative data is valuable because it allows you to understand, explain, and explore the demand for health services in a subjective, nuanced, and contextual way. You can use qualitative data to capture, represent, or illustrate the meanings, perceptions, or emotions of the people who are involved in or affected by the demand for health services, such as patients, providers, or policymakers. However, qualitative data may also have some challenges, such as subjectivity, variability, or generalizability, that may affect its credibility and transferability. Therefore, you should always use rigorous methods, criteria, and frameworks to collect and analyze your qualitative data before using it for demand forecasting.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?