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All-payer, population-level hospital discharge data have been used to identify health disparities across racial/ethnic and other demographic groups. However, researchers are often unable to identify unique patients in the data sets if a unique patient identifier is not provided. The lack of the unique patient identifier can result in biased estimates of research outcomes using discharge data. This could then mislead the researchers, public, or policy-makers who utilize such biased results. This study examined estimation bias of health disparities due to rehospitalizations considering diabetes-related preventable hospitalizations using 6 years of state-level data from Hawai'i Health Information Corporation. Different analyses methods showed different probabilities of having multiple visits by age, race/ethnicity and payer subgroups. Charge analysis results also showed that ignoring the multiple visits could result in significance error. For a patient with multiple hospitalizations, rehospitalizations are often dependent upon the discharge status of previous visits, and the independence assumption of the multiple visits may not be appropriate. Ignoring the multiple visits in population-level analyses could result in severe health disparities significance errors. In this hospitalization charge analysis, the Chinese group was not significantly different than the White group (relative risk ratio - RR: [95% CI]: 0.93 [0.80, 1.08]), while the difference was signficant (RR [95% CI]: 0.86 [0.77,0.96]) when the multiple visits were ignored. [ABSTRACT FROM AUTHOR]

Abstract Background South Africa’s National Health Laboratory Service (NHLS), the only clinical laboratory service in the country’s public health sector, is an important resource for monitoring public health programmes. Objectives We describe NHLS data quality, particularly patient demographics among infants, and the effect this has on linking multiple test results to a single patient. Methods Retrospective descriptive analysis of NHLS data from 1st January 2017—1st September 2020 was performed. A validated probabilistic record-linking algorithm linked multiple results to individual patients in lieu of a unique patient identifier. Paediatric HIV PCR data was used to illustrate the effect on monitoring and evaluating a public health programme. Descriptive statistics including medians, proportions and inter quartile ranges are reported, with Chi-square univariate tests for independence used to determine association between variables. Results During the period analysed, 485 300 007 tests, 98 217 642 encounters and 35 771 846 patients met criteria for analysis. Overall, 15.80% (n = 15 515 380) of all encounters had a registered national identity (ID) number, 2.11% (n = 2 069 785) were registered without a given name, 63.15% (n = 62 020 107) were registered to women and 32.89% (n = 32 304 329) of all folder numbers were listed as either the patient’s date of birth or unknown. For infants tested at

Population mobility makes patient-tracking and care linkage in the South African Development Community (SADC) challenging. Case-based surveillance (CBS) through individual-level clinical data linked with a unique patient-identifier (UPI) is recommended. We conducted a mixed-methods landscape analysis of UPI and CBS implementation within selected SADC countries, this included: (1) SADC UPI implementation literature review; (2) assessment of UPI and CBS implementation for high HIV-prevalence SADC countries; (3) UPI implementation case-study in selected South African primary healthcare (PHC) facilities. Research into CBS and UPI implementation for the SADC region is lacking. Existing patient-identification methods often fail and limit patient-tracking. Paper-based records and poor integration between health-information systems further restrict patient-tracking. Most countries were in the early-middle stages of CBS and faced UPI challenges. Our South African case-study found that the UPI often goes uncaptured. Difficulties tracking patients across prevention and care cascades will continue until a functional and reliable UPI is available. [ABSTRACT FROM AUTHOR]

Background: South Africa's National Health Laboratory Service (NHLS), the only clinical laboratory service in the country's public health sector, is an important resource for monitoring public health programmes.Objectives: We describe NHLS data quality, particularly patient demographics among infants, and the effect this has on linking multiple test results to a single patient.Methods: Retrospective descriptive analysis of NHLS data from 1st January 2017-1st September 2020 was performed. A validated probabilistic record-linking algorithm linked multiple results to individual patients in lieu of a unique patient identifier. Paediatric HIV PCR data was used to illustrate the effect on monitoring and evaluating a public health programme. Descriptive statistics including medians, proportions and inter quartile ranges are reported, with Chi-square univariate tests for independence used to determine association between variables.Results: During the period analysed, 485 300 007 tests, 98 217 642 encounters and 35 771 846 patients met criteria for analysis. Overall, 15.80% (n = 15 515 380) of all encounters had a registered national identity (ID) number, 2.11% (n = 2 069 785) were registered without a given name, 63.15% (n = 62 020 107) were registered to women and 32.89% (n = 32 304 329) of all folder numbers were listed as either the patient's date of birth or unknown. For infants tested at < 7 days of age (n = 2 565 329), 0.099% (n = 2 534) had an associated ID number and 48.87% (n = 1 253 620) were registered without a given name. Encounters with a given name were linked to a subsequent encounter 40.78% (n = 14 180 409 of 34 775 617) of the time, significantly more often than the 21.85% (n = 217 660 of 996 229) of encounters registered with a baby-derivative name (p-value < 0.001).Conclusion: Unavailability and poor capturing of patient demographics, especially among infants and children, affects the ability to accurately monitor routine health programmes. A unique national patient identifier, other than the national ID number, is urgently required and must be available at birth if South Africa is to accurately monitor programmes such as the Prevention of Mother-to-Child Transmission of HIV. [ABSTRACT FROM AUTHOR]

Background: Knowledge about rare diseases is often lacking due to the low prevalence of each disease. A challenge to increasing knowledge about rare diseases is the plethora of data silos being generated, which create barriers to information sharing, collaboration and coordination between researchers. The inability to share and reuse research data contributes to inefficient data collection, increased patient burden and possibly decelerates the path to better treatments. A unique, universal identifier would have the ability to link the same patient across research datasets and eliminate data silos. Methods: We present CRID (clinical research ID), a unique, universal, patient-generated identifier that can be used to facilitate collaborative rare disease clinical research. The identifier consists of 8 random alphanumeric characters, and can uniquely identify a patient. The CRID website [https://TheCRID.org] is available in multiple languages to support patients globally, and is installed on a secure, cloud-based server platform. All patient data are encrypted in-transit, at rest and within the application. We field-tested the CRID identifier across 6 rare disease research studies to demonstrate its ability to connect a patient across multiple research studies. Results: The CRID website allows a patient to create their unique identifier quickly and share it with researchers easily. Field tested across multiple disease research studies, this identifier has helped to reduce the burden on the patient by reusing their documents and transposing form data from the patient's original study into subsequent studies. Conclusion: A unique, universal, patient-generated identifier can be used to facilitate collaborative rare disease clinical research. With patients’ data connected using the CRID identifier and shared across study protocols, better insights can be gained into how a disease develops that is not possible with inaccessible data silos.

All-payer, population-level hospital discharge data have been used to identify health disparities across racial/ethnic and other demographic groups. However, researchers are often unable to identify unique patients in the data sets if a unique patient identifier is not provided. The lack of the unique patient identifier can result in biased estimates of research outcomes using discharge data. This could then mislead the researchers, public, or policy-makers who utilize such biased results. This study examined estimation bias of health disparities due to rehospitalizations considering diabetes-related preventable hospitalizations using 6 years of state-level data from Hawai'i Health Information Corporation. Different analyses methods showed different probabilities of having multiple visits by age, race/ethnicity and payer subgroups. Charge analysis results also showed that ignoring the multiple visits could result in significance error. For a patient with multiple hospitalizations, rehospitalizations are often dependent upon the discharge status of previous visits, and the independence assumption of the multiple visits may not be appropriate. Ignoring the multiple visits in population-level analyses could result in severe health disparities significance errors. In this hospitalization charge analysis, the Chinese group was not significantly different than the White group (relative risk ratio - RR: [95% CI]: 0.93 [0.80, 1.08]), while the difference was signficant (RR [95% CI]: 0.86 [0.77,0.96]) when the multiple visits were ignored., Competing Interests: The author does not identify a conflict of interest., (©Copyright 2023 by University Health Partners of Hawai‘i (UHP Hawai‘i).)

A basic requirement to provide patient-centric care is to have identifiers serve to link all patient data across multiple systems together to provide a coherent service. In most hospitals today, the lack of a unique patient identifier that works across multiple hospital information systems such as HIS, LIS, PACS, and others means that healthcare providers typically rely on a patient’s name, date of birth, and other personal information to identify their medical records in the electronic health record systems. However, this information is not guaranteed to be unique to an individual. It also takes considerable amount of time to check on this or more information and requires much human intervention. As a result, providers often have a difficult time properly identifying patients, thus increase the risk of associating medical information with a wrong patient. [ABSTRACT FROM AUTHOR]

Background: It has been suggested that inaccuracies in cancer registries are distorting UK survival statistics. This study compared the Northern Ireland Cancer Registry (NICR) database of living patients, with independent data held by Northern Ireland's General Practitioners (GPs) to compare and validate the recorded diagnoses and dates held by the registry. Methods: All 387 GP practice managers were invited to participate. 100 practices (25.84%) responded. Comparisons were made for 17,102 patients, equivalent to 29.08% of the living patients (58,798) extracted from the NICR between 1993 and 2010. Results: There were no significant differences (p > 0.05) between the responding and nonresponding GP patient profiles for age, marital status or deprivation score. However, the responding GPs included more female patients (p = 0.02). NICR data accuracy was high, 0.08% of GP cancer patients (n = 15) were not included in registry records and 0.02% (n = 2) had a diagnosis date which varied more than 2 weeks from GP records (3 weeks and 5 months). The NICR had recorded two different tumour types and three different tumour statuses (benign vs. malignant) to the GPs. Conclusion: This comparison demonstrates a high level of accuracy within the NICR and that the survival statistics based on this data can be relied upon. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: : The objective of this paper is to clarify the major concepts related to architecture and design of patient identity management software systems so that an implementor looking to solve a specific integration problem in the context of a Master Patient Index (MPI) and a deduplication engine can address the relevant issues. Methods: : The ideas presented are illustrated in the context of a reference use case from Integrating the Health Enterprise Patient Identifier Cross-referencing (IHE PIX) profile. Sound software engineering principles using the latest design paradigm of model driven architecture (MDA) are applied to define different views of the architecture. Results: : The main contribution of the paper is a clear software architecture roadmap for implementors of patient identity management systems. Conceptual design in terms of static and dynamic views of the interfaces is provided as an example of platform independent model. This makes the roadmap applicable to any specific solutions of MPI, deduplication library or software platform. Conclusions: : Stakeholders in need of integration of MPIs and deduplication engines can evaluate vendor specific solutions and software platform technologies in terms of fundamental concepts and can make informed decisions that preserve investment. This also allows freedom from vendor lock-in and the ability to kick-start integration efforts based on a solid architecture. [Copyright &y& Elsevier]

When dealing with medical image management, there is a need to ensure information authenticity and dependability. Being able to verify the information belongs to the correct patient and is issued from the right source is a major concern. Verification can help to reduce the risk of errors when identifying documents in daily practice or when sending a patient's Electronic Health Record. At the same time, patient privacy issues may appear during the verification process when the verifier accesses patient data without appropriate authorization. In this paper we discuss the combination of watermarking with different identifiers ranging from DICOM standard UID to an Anonymous European Patient Identifier in order to improve medical image protection in terms of authenticity and maintainability. [ABSTRACT FROM AUTHOR]

The French ministry of Health is setting up the Personal Medical Record (PMR). This innovative tool has long been expected by French Health Authorities, Associations of Patients, other Health's associations, those defending Individual Liberties and the French National Data Protection Authority. The PMR will lead to improvements in many areas such as Diagnosis (Research and monitoring) Healthcare (Management of emergencies, urgent situations, Temporal health monitoring and evaluation), Therapy (Cohorts of patients for Clinical trials and epidemiological studies). The PMR will foster safe healthcare management, clinical research and epidemiological studies. Nevertheless, it raises many important questions regarding duplicates and the quality, precision and coherence of the linkage with other health data coming from different sources. The currently planned identifying process raises many questions with regard to its ability to deal with potential duplicates and to perform data linkage with other health data sources. Through this article, using the electronic health records, we develop and propose an identification process to improve the French PMR. Our proposed unique patient identifier will guarantee the security, confidentiality and privacy of the personal data, and will prove to be particularly useful for health planning, health policies and research as well as clinical and epidemiological studies. Finally, it will certainly be interoperable with other European health information systems. We propose here an alternative identification procedure that would allow France to broaden the scope of its PMR project by making it possible to contribute to public health research and policy while increasing interoperability with European health information systems and preserving the confidentiality of the data. [ABSTRACT FROM AUTHOR]

Abstract: The French Ministry of Health is setting up the personal medical record (PMR or DMP for Dossier Médical Personnel in French). This innovating tool is highly expected and will be extremely useful for the therapeutic follow-up as well as for epidemiological studies on which public health policies are based. Therefore the currently planned identifying process should prevent any epidemiological use of these data. Numerous scientific organisations have alerted government powers about the threat that this impairment represents, and they wish to promote some secure procedures that exist, which have already proved their efficiency at the national and international level. [Copyright &y& Elsevier]

The French ministry of Health is setting up the personal medical record (PMR or DMP for Dossier Medical Personnel in French). This innovating tool is highly expected and will be extremely useful for the therapeutic follow-up as well as for epidemiological studies on which public health policies are based. Therefore the currently planned identifying process should prevent any epidemiological use of these data. Numerous scientific organisations have alerted government powers about the threat that this impairment represents, and they wish to promote some secure procedures that exist, which have already proved their efficiency at the national and international level. [ABSTRACT FROM AUTHOR]

Background: The lack of unique patient identifiers is a challenge to patient care in developing countries. Probabilistic and deterministic matching approaches remain sub-optimal. However, affordable and scalable biometric solutions have not been rigorously evaluated in these settings.Methods: We implemented and evaluated performance of an open-source facial recognition system, OpenFace, integrated within a nationally-endorsed electronic health record system in Western Kenya. Patients were first enrolled via facial images, and later matched via the system. Accuracy of facial recognition was evaluated using Sensitivity; False Acceptance Rate (FAR); False Rejection Rate (FRR); Failure to Capture Rate (FTC) and Failure to Enroll Rate (FTE). 103 patients (mean age 37.8, 49.5% female) were enrolled.Results: The system had a sensitivity of 99.0%, FAR <1%, FRR 0.00, FTC 0.00 and FTE 0.00. Wearing spectacles did not affect performance.Conclusion: An open source facial recognition system correctly and accurately identified almost all patients during the first match. [ABSTRACT FROM AUTHOR]