Health information technology (HIT) is being sought as one of the key elements to streamline the process of providing healthcare to improve quality and harness cost. It is hoped that HIT will lead to a more cost-efficient healthcare system than the current one. Surprisingly, there is no agreed definition of HIT in academic literature or government documentation. The Health Information Technology for Economic and Clinical Health (HITECH) Act (a provision of the American Recovery and Reinvestment Act of 2009 (ARRA) defines health information technology as “hardware, software, integrated technologies or related licenses, intellectual property, upgrades, or packaged solutions sold as services that are designed for or support the use by health care entities or patients for the electronic creation, maintenance, access, or exchange of health information.” It could refer to a broad base of information technologies used in healthcare from robotics surgery to chronic disease home monitoring devices.1 However, there is a consensus on the purpose of HIT as the use of devices for the management of information in order to ensure that it is available to the right person at the right time and place.2–4

HIT is the basis for a more patient-centered and evidence-based medicine with the real-time availability of high-quality information.5,6

Despite the various interpretations of the scope of HIT, all healthcare stakeholders agree that it is the premise on which a 21st-century healthcare system in the United States must be based.7

HIT experts concur that the U.S healthcare system must widely adopt interoperable electronic health records (EHRs) with important components such as computerized physician/provider order entry (CPOE) and e-prescriptions to build a cost-efficient healthcare system.8–11

/pdf/redefining-the-roles-of-health-information-management-66kuugq8fo.pdf

Redefining the Roles of Health Information Management Professionals in Health Information Technology

Background: EHR systems have high potential to improve healthcare delivery and management. Although structured EHR data generates information in machine-readable formats, their use for decision support still poses technical challenges for researchers due to the need to preprocess and convert data into a matrix format. During our research, we observed that clinical informatics literature does not provide guidance for researchers on how to build this matrix while avoiding potential pitfalls. Objectives: This article aims to provide researchers a roadmap of the main technical challenges of preprocessing structured EHR data and possible strategies to overcome them. Methods: Along standard data processing stages – extracting database entries, defining features, processing data, assessing feature values and integrating data elements, within an EDPAI framework –, we identified the main challenges faced by researchers and reflect on how to address those challenges based on lessons learned from our research experience and on best practices from related literature. We highlight the main potential sources of error, present strategies to approach those challenges and discuss implications of these strategies. Results: Following the EDPAI framework, researchers face five key challenges: gathering and integrating data, identifying and handling different feature types, combining features to handle redundancy and granularity, addressing data missingness, and handling multiple feature values. Strategies to address these challenges include: cross-checking identifiers for robust data retrieval and integration; applying clinical knowledge in identifying feature types, in addressing redundancy and granularity, and in accommodating multiple feature values; and investigating missing patterns adequately. Conclusions: This article contributes to literature by providing a roadmap to inform structured EHR data preprocessing. It may advise researchers on potential pitfalls and implications of methodological decisions in handling structured data, so as to avoid biases and help realize the benefits of the secondary use of EHR data.

https://www.thieme-connect.com/products/ejournals/pdf/10.4338/ACI-2016-03-SOA-0035.pdf

Preprocessing structured clinical data for predictive modeling and decision support. A roadmap to tackle the challenges.

Initial Results of the Early Auditory Referral-Primary Care (EAR-PC) Study.

Background Self-tracking through mobile health technology can augment the electronic health record (EHR) as an additional data source by providing direct patient input. This can be particularly useful in the context of enigmatic diseases and further promote patient engagement. Objectives This study aimed to investigate the additional information that can be gained through direct patient input on poorly understood diseases, beyond what is already documented in the EHR. Methods This was an observational study including two samples with a clinically confirmed endometriosis diagnosis. We analyzed data from 6,925 women with endometriosis using a research app for tracking endometriosis to assess prevalence of self-reported pain problems, between- and within-person variability in pain over time, endometriosis-affected tasks of daily function, and self-management strategies. We analyzed data from 4,389 patients identified through a large metropolitan hospital EHR to compare pain problems with the self-tracking app and to identify unique data elements that can be contributed via patient self-tracking. Results Pelvic pain was the most prevalent problem in the self-tracking sample (57.3%), followed by gastrointestinal-related (55.9%) and lower back (49.2%) pain. Unique problems that were captured by self-tracking included pain in ovaries (43.7%) and uterus (37.2%). Pain experience was highly variable both across and within participants over time. Within-person variation accounted for 58% of the total variance in pain scores, and was large in magnitude, based on the ratio of within- to between-person variability (0.92) and the intraclass correlation (0.42). Work was the most affected daily function task (49%), and there was significant within- and between-person variability in self-management effectiveness. Prevalence rates in the EHR were significantly lower, with abdominal pain being the most prevalent (36.5%). Conclusion For enigmatic diseases, patient self-tracking as an additional data source complementary to EHR can enable learning from the patient to more accurately and comprehensively evaluate patient health history and status.

/pdf/augmenting-the-clinical-data-sources-for-enigmatic-diseases-4zybpu23du.pdf

Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis.

Sharing of health data between independent organizations, each using customized Electronic Health Records (EHR) management systems or Healthcare Information System (HIS) encompasses major concerns. It requires an integrated interoperable environment or interface commonly understood by end-users of healthcare domain. The sheer number of systems, standards, procedures and system developers seriously hinders interoperability. Developers need to adhere to the standards while designing such system. Developing standardized interfaces would enable seamless communication and transfer of required EHR between disparate systems. The paper after giving a detailed overview of interoperability, identifies the challenges to Health Informatics data-exchange in interoperable environment and recommends some viable solutions in this direction. This paper provides a glance at different approaches of designing interoperable models and illustrates the healthcare models designed on such approaches. The study also includes available set of vocabularies, formats and standards specifically designed for the purpose. Considering the Health environment, protecting the confidentiality and illegal disclosure during transfer of EHR between organizations, is a serious concern. Vendors incorporate customized and vendor-dependent security attributes while designing the application, resulting in major challenge of securely sharing of data. The paper includes available security controls and gaps accruing due to interoperability and recommends the need of robust security control improving accessibility and availability of EHR at the required place by the authorized user.

Challenges and Recommendations to Healthcare Data Exchange in an Interoperable Environment

Problem list guidance in the EHR.

More than ever, the healthcare industry is making significant progress in the quest for electronic health records (EHRs), which will improve the quality and safety of patient care and achieve real efficiencies in the healthcare delivery system. Emphasis has been placed on intelligent systems that support the care process and clinical decisions as well as the creation of health information for patient care.

The strategic importance of electronic health records management.

Health IM and health IT. Frequent traveling companions.

Practice brief. HIM and Health IT. Discovering common ground in an electronic healthcare environment.

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Problem list guidance in the EHR.

The strategic importance of electronic health records management.

Health IM and health IT. Frequent traveling companions.

Practice brief. HIM and Health IT. Discovering common ground in an electronic healthcare environment.