Privacy
Most healthcare platform companies have data governance, compliance and privacy policy. Naturally, we do too.
But at Alike Patient, we’re more excited about our federally compliant openness and sharing philosophy. It may sound counterintuitive, but it’s what drives our groundbreaking approach.
Sharing Information backed by our core principles
We believe that patients and caregivers need a safe and secured place to connect and support each other.
Information we collect
We collect shared data (information you provide about yourself to share with others like the condition you’re living with and treatments you’re trying), restricted data (like your name, email address, and date of birth), and data about how you use our platform.
Alike Patient may de-identify Restricted Data, such that it no longer contains identifying information and is no longer Restricted Data, in which case such data shall be treated by Alike Patient as Shared Data. Shared Data is all information, except Restricted Data, that Members provide about themselves when using the Platform or in other communications with Alike Patients. How we safeguard your data We deploy the best cloud based HIPAA and GDRP compliant data protection and privacy tools, to protect sensitive and private data. That includes threat intelligence and advance analytics tools that can perform cyber-hunting and processes for risk-aligned detection and incident handling. Secure, HIPAA and GDPR compliant de-identified records Working with our technology partners, we have designed cutting-edge, de- identification technology that replaces private patient information with an encrypted “token” that can’t be reverse-engineered to reveal the original information.
Furthermore, our technology can create these same patient-specific tokens in any data set, which means that now two different data sets can be combined using the patient tokens to match corresponding records without ever sharing the underlying patient information.
Our technology creates statistically de-identified data sets by removing personally-identifiable information (PII; which includes but is not necessarily limited to protected health information, or PHI) like names and medical record numbers; and modifying other values, such as turning 5-digit zip codes into 3-digit zip areas, or converting dates of birth to years of birth.
If required the technology can also support more complicated derivations of data such as replacing detailed medical codes (e.g., replacing the ICD10 code for Ebola with more general medical codes).
As the technology de-identifies a patient record, it also generates one or more tokens for that record. These tokens are based on the PII in the record. As a result, the tokens can be consistently created from any data set where the underlying PII is the same. Matching tokens can be used to link a patient’s record in one data set with a record for the same patient in a different set, without ever exposing the PII of that patient.
Tokens can be built from many different combinations of PII elements, and the specific tokens to be created will be specified during the configuration process. Multiple tokens are often created to facilitate matching. Tokens can be built based on fields such as social security number, which allows for deterministic matching; alternately, they can be constructed from fields such as name and date of birth, which in combination can be used to support probabilistic matching.
Alike Patient may de-identify Restricted Data, such that it no longer contains identifying information and is no longer Restricted Data, in which case such data shall be treated by Alike Patient as Shared Data. Shared Data is all information, except Restricted Data, that Members provide about themselves when using the Platform or in other communications with Alike Patients. How we safeguard your data We deploy the best cloud based HIPAA and GDRP compliant data protection and privacy tools, to protect sensitive and private data. That includes threat intelligence and advance analytics tools that can perform cyber-hunting and processes for risk-aligned detection and incident handling. Secure, HIPAA and GDPR compliant de-identified records Working with our technology partners, we have designed cutting-edge, de- identification technology that replaces private patient information with an encrypted “token” that can’t be reverse-engineered to reveal the original information.
Furthermore, our technology can create these same patient-specific tokens in any data set, which means that now two different data sets can be combined using the patient tokens to match corresponding records without ever sharing the underlying patient information.
Our technology creates statistically de-identified data sets by removing personally-identifiable information (PII; which includes but is not necessarily limited to protected health information, or PHI) like names and medical record numbers; and modifying other values, such as turning 5-digit zip codes into 3-digit zip areas, or converting dates of birth to years of birth.
If required the technology can also support more complicated derivations of data such as replacing detailed medical codes (e.g., replacing the ICD10 code for Ebola with more general medical codes).
As the technology de-identifies a patient record, it also generates one or more tokens for that record. These tokens are based on the PII in the record. As a result, the tokens can be consistently created from any data set where the underlying PII is the same. Matching tokens can be used to link a patient’s record in one data set with a record for the same patient in a different set, without ever exposing the PII of that patient.
Tokens can be built from many different combinations of PII elements, and the specific tokens to be created will be specified during the configuration process. Multiple tokens are often created to facilitate matching. Tokens can be built based on fields such as social security number, which allows for deterministic matching; alternately, they can be constructed from fields such as name and date of birth, which in combination can be used to support probabilistic matching.