Personal health record (PHR) is considered a crucial part in improving patient outcomes by ensuring important aspects in treatment such as continuity of care (COC), evidence- based treatment (EBT) and most importantly prevent medical errors (PME). Recently there has been more focus on preventive care or monitoring and control of patients symptoms than treatment itself. Nowadays, there are many mobile health applications and sensors such as blood pressure sensors, electrocardiogram sensors, blood glucose measuring devices, and others that are used by the patients who monitor and control their health. These apps and sensors produce personal health data that can be used for treatment purposes. If managed and handled properly, it can be considered patient-generated data. There are other types of personal health data that are available from various sources such as hospitals, doctors offices, clinics, radiology centers or any other caregivers.
Aforementioned health documents are deemed as a PHR. However, personal health data is difficult to collect and manage due to the fact that they are distributed over multiple sources (e.g. caregivers, patients themselves, clinical devices, and others) and each may describe patient problems in their own way. Such inconsistencies could lead to medical mistakes when it comes to the treatment of the patient. In case of emergency, this situation makes timely retrieval of necessary personal clinical data difficult. In addition, since the amount and types of personal clinical data continue to grow, finding relevant clinical data when needed is getting more difficult if no actions are taken to resolve such issue. Having complete and accurate patient medical history available at the time of need can improve patient outcomes by ensuring important aspects such as COC, EBT, and PME. Despite the importance of PHR, the adoption rate by the general public in the U.S. still remains low. In this study we attempt to use Personal Health Record System (PHRS) as a central point to aggregate health records of a patient from multiple sources (e.g. caregivers, patients themselves, clinical devices, and others) and to standardize personal health records (e.g. use of International Classification of Diseases (ICD- 10) and Systemized Nomenclature of Medicine Clinical Terms (SNOMED CT)) through our proof-of-concept model: Health Decision Support System (HDSS).
We started out by exploring the barriers in adopting PHRs and proposed a few approaches that can promote the adoption of PHRS by the general public so it is possible to implement continuity of care in community settings, evidence-based care, and also prevent potential medical errors. To uncover the barriers in adopting PHR, we have surveyed articles related to PHRS from 2008 to 2017 and categorized them into 6 different categories: motivation, usability, ownerships, interoperability, privacy, and security and portability.
We incorporated the survey results into our proposed PHRS, so it can help overcome some of the barriers and motivate people to adopt PHRS. In Our proposed PHRS, we aimed to manage personal health data by utilizing metadata for organizing and retrieval of clinical data. Cloud storage was chosen for easy access and sharing of health data with relevant caregivers to implement the continuity of care and evidence-based treatment. In our study, we have used Dropbox as storage for testing purposes. However, for practical use, secure cloud storage services that are Health Insurance Portability and Accountability Act (HIPAA) complaint can be used for privacy and security purposes, such as Dropbox (Business), Box, Google Drive,Microsoft OneDrive, and Carbonite. In case of emergency, we make critical medical information such as current medication and allergies available to relevant caregivers with valid license numbers only. In addition, to standardize PHR and improve health knowledge, we provide semantic guidance for using SNOMED CT to describe patient problems and for mapping SNOMED CT codes to ICD-10-CM to uncover potential diseases. As a proof of concept, we have developed two systems (prototypes): first, my clinical record system (MCRS) for organizing, managing, storing, sharing and retrieving personal health records in a timely manner; second, a health decision support system (HDSS) that can help users to use SNOMED CT codes and potential disease(s) as a diagnosis result.
|Commitee:||Acharya, Subrata, Alkharouf, Nadim, Karne, Ramesh|
|Department:||Computer and Information Sciences|
|School Location:||United States -- Maryland|
|Source:||DAI-B 80/03(E), Dissertation Abstracts International|
|Subjects:||Information Technology, Health care management|
|Keywords:||Decision support systems, Dublin Core Schema, Electronic health records, Mapping from SNOMED CT to ICD-10, Personal health records, Standard medical codes|
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