Implementation – Digital Medicine Society (DiMe)

Are you looking to begin or advance your sensor data integration journey? Six criteria are essential to your success: data collection, transmission, processing, security, privacy, and quality. Together, these key areas provide the building blocks for a successful sensor data integration strategy so you can make better decisions faster in health care and research.

The following considerations and best practices can help you balance these criteria, optimizing your performance and impact of your work with sensor-generated data on patients’ lives.

ART Criteria

For sensor data to drive better decisions faster in healthcare and research, sensor-generated data must be accessible, relevant, and trustworthy (ART). The following criteria have critical impact on the delivery of ART sensor data suitable for clinical decision-making.

Data collection

Beginning with data acquisition – the process of measuring physical world conditions and phenomena such as electricity, sound, temperature and pressure – data collection is the ongoing process of accumulating sensor data and metadata at each step of the data lifecycle. Data collection is critical to ensuring that the necessary contextual information about the data and its management over time is available to use the sensor data for clinical decision-making.

Data transmission

For sensor data and its accompanying metadata to contribute to a data ecosystem driving clinical decision-making, the processes by which these data are transmitted must be considered.

Data processing

Sensor-generated data is not clinically interpretable at the point of collection. For example, the electric currents on the skin captured by an ECG must be processed into heart rate before a person can understand the clinical relevance of the data. Substantial data processing is required to transform the signals captured by sensors and the high velocity flows of data they generate into information suitable for clinical decision-making.

Data privacy

The protection of personal, sensor-generated data from those who should not have access to it and the ability of individuals to determine who can access their personal information is not only required by laws and regulations in some instances, but also fundamental to establishing trust in a health data ecosystem that relies on sensor-generated data for clinical decision-making.

Data security

The practice of protecting sensor-generated data, and the systems that store and process these data, from unauthorized access, corruption, or theft throughout its entire lifecycle is an essential component of establishing sensor-generated data as a viable source of information to support clinical decision-making.

Data quality

The Institute of Medicine defines high quality data as data strong enough to support conclusions and interpretations equivalent to those derived from error-free data (IOM). Sensor-generated data must be high quality to be useful for clinical decision-making, noting that this bar will vary with the nature of the decision. Sensor data quality is determined by the completeness, validity, uniqueness, consistency, timeliness, and accuracy of the data.

Download ART Criteria

Considerations and Best Practices

Do you want to learn more about how to implement the ART Criteria in your decision-making? Review short ‘cheat sheets’ of key considerations and best practices aligned with each criterion.

Data Collection

Data Transmission

Data Processing

Data Privacy

Data Security

Data Quality

‘ART’ Criteria Prioritization Tool

Each time you use sensor data to inform a clinical decision, the context is different. Use this tool to help optimize your approach to sensor data integrations to meet the needs of your systems and users.

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