Data Validation. Integration of multiple datasets together can be fraught with difficulty, including inconsistent fields, missing datasets, and conflicting sets of information. The Provider solution will need rules to ensure referential integrity between datasets: ● Ensure that primary keys in one dataset are indeed unique, even compound primary keys ● Ensure that foreign keys in one file match the primary keys in another file ● Validation that all other fields are well formed, and cleaned as required In the data integration environment, it's also important that data issues can be quickly acted upon. Provider shall provide the following options: ● Automatic quarantining of data to ensure that invalid data is not ingested. Even if this is only part of a file, the invalid data is removed and the remainder quarantined ● Email alerts when data issues are identified so they can quickly be escalated us when jobs are not synchronized Data Management ● The Provider will not copy any CPS data to any media, including hard drives, flash drives, or other electronic devices, other than as expressly approved by CPS. ● Provider shall return or destroy all confidential information received from CPS, or created or received by Provider on behalf of CPS. ● In the event that Provider determines that returning or destroying the confidential information is infeasible, Provider shall notify CPS of the conditions that make return or destruction infeasible, but such plans will be approved by CPS. ● If CPS agrees that return or destruction of confidential information is infeasible; Provider shall extend the protections for such confidential information and limit further uses and disclosures of such confidential information. ● Return all data that is the property of CPS in an electronic format, via an online secure service, such as SFTP, or a shared storage facility security. ● The Solution should support the latest encryption and SSL in motion and at rest for PII (Personally identifiable information). ● Security practices regarding secure application development must be documented. ● Data exchanges with CPS shall be done in an automated fashion. Data Conversion and Validation The Provider must provide human resources to partner with the CPS Enterprise Data Team to document the proper conversion mapping and perform test validation for any/all bi-directional data exchanges, or any automation. Data Protection Data shall be protected with the latest backup technologies, and be backed up daily, with ret...
Data Validation. The MCO must require all Texas Health Steps Providers to submit claims for services paid (either on a capitated or fee-for service basis) on the CMS 1500 claim form and use the HIPAA compliant code set required by HHSC. Encounter Data will be validated by chart review of a random sample of Texas Health Steps eligible enrollees against monthly Encounter Data reported by the MCO. HHSC or its designee will conduct chart reviews to validate that all screens are performed when due and as reported, and that reported data is accurate and timely. Substantial deviation between reported and charted Encounter Data could result in the MCO and/or Network Providers being investigated for potential Fraud, Abuse, or Waste without notice to the MCO or the Provider.
Data Validation. 1.1.3.1. Completion of sampling exercise to validate data delivered over network compared to date read from the face of the meter while installed in the field. Representative sample to be taken by FPL to meet ***.
Data Validation. 1.6.3.1. Validation of sample of meters to occur for which outage indication has been provided over the network.
Data Validation. The HMO must require all Texas Health Steps Providers to submit claims for services paid (either on a capitated or fee-for service basis) on the CMS 1500 claim form and use the HIPAA compliant code set required by HHSC. Encounter Data will be validated by chart review of a random sample of Texas Health Steps eligible enrollees against monthly Encounter Data reported by the HMO. HHSC or its designee will conduct chart reviews to validate that all screens are performed when due and as reported, and that reported data is accurate and timely. Substantial deviation between reported and charted Encounter Data could result in the HMO and/or Network Providers being investigated for potential Fraud, Abuse, or Waste without notice to the HMO or the Provider.
Data Validation. 1. The parties agree that Defendants shall retain the services of a Data Validator for purposes of verifying and reporting on a semi-annual basis Defendants’ compliance with the exit criteria identified in this Agreement. The Data Validator shall be a third party contractor of the State of Missouri that has had prior experience conducting data validation services for state child welfare agencies. The Plaintiffs agree and understand that the services of a Data Validator will have to be retained in compliance with federal and state law governing procurement of contracts. Defendants will make best efforts to complete this process within four months from the date of this Agreement.
Data Validation. In general, the NAEMS researchers invalidated measurement data if the data values were: • Unreasonably low or high when compared to normal ranges if there was supporting evidence that the data value is not correct (e.g., lagoon temperature sensor producing a reading of less than -40 ° C). • Obtained during system installation, testing or maintenance during which uncorrectable errors might be introduced. • Obtained when a sensor or instrument was proven to be malfunctioning (e.g., unstable). • Obtained during calibration or precision check of a sensor or instrument and before the sensor or instrument reached equilibrium after the check. • Obtained when the data acquisition and control hardware and/or software were not functioning correctly. Data that the NAEMS researchers deemed invalid were retained in the preprocessed data sets. However, the EPA did not use the flagged data to calculate pollutant emissions rates. For averaged data, data were invalidated to avoid errors introduced into calculated mean values due to partial-data days (e.g., only a few hours of valid data) that would result in biased time weights:
Data Validation. 1.3.3.1 Completion of dual metering exercise or alternate method to compare interval data capture at the meter and compare to meter data collected and provided over the network. Method TBD.
Data Validation. Contractor agrees to assist the State in its validation of utilization data by making available a sample of medical records and a sample of its claims data.
Data Validation. The final step in the conversion process is the data validation. Much attention will be given to data integrity during the testing phase by the program developers. The conversion assistant will also spend time testing the integrity of the information. Balances and the output of processes will be tested after the conversion. A visual inspection of different modules will be performed by choosing different records on a random base. But data validation is ultimately the responsibility of the Client.
