Developing data-driven clinical pathways using electronic health records: The cases of total laparoscopic hysterectomy and rotator cuff tears

Applying sequence analysis to uncover 'real-world' clinical pathways from routinely collected data: a systematic review

OBJECTIVES

This systematic review aims to elucidate the methodological practices and reporting standards associated with sequence analysis (SA) for the identification of clinical pathways in real-world scenarios, using routinely collected data.

STUDY DESIGN AND SETTING

We conducted a methodological systematic review, searching five medical and health databases: MEDLINE, PsycINFO, CINAHL, EMBASE and Web of Science. The search encompassed articles from the inception of these databases up to February 28, 2023. The search strategy comprised two distinctive sets of search terms, specifically focused on sequence analysis and clinical pathways.

RESULTS

19 studies met the eligibility criteria for this systematic review. Nearly 60% of the included studies were published in or after 2021, with a significant proportion originating from Canada (n = 7) and France (n = 5). 90% of the studies adhered to the fundamental SA steps. The optimal matching (OM) method emerged as the most frequently employed dissimilarity measure (63%), while agglomerative hierarchical clustering using Ward's linkage was the preferred clustering algorithm (53%). However, it is imperative to underline that a majority of the studies inadequately reported key methodological decisions pertaining to SA.

CONCLUSION

This review underscores the necessity for enhanced transparency in reporting both data management procedures and key methodological choices within SA processes. The development of reporting guidelines and a robust appraisal tool tailored to assess the quality of SA would be invaluable for researchers in this field.

Unraveling a blockchain-based framework towards patient empowerment: A scoping review envisioning future smart health technologies

The COVID-19 pandemic shows us how crucial patient empowerment can be in the healthcare ecosystem. Now, we know that scientific advancement, technology integration, and patient empowerment need to be orchestrated to realize future smart health technologies. In that effort, this paper unravels the Good (advantages), Bad (challenges/limitations), and Ugly (lacking patient empowerment) of the blockchain technology integration in the Electronic Health Record (EHR) paradigm in the existing healthcare landscape. Our study addresses four methodically-tailored and patient-centric Research Questions, primarily examining 138 relevant scientific papers. This scoping review also explores how the pervasiveness of blockchain technology can help to empower patients in terms of access, awareness, and control. Finally, this scoping review leverages the insights gleaned from this study and contributes to the body of knowledge by proposing a patient-centric blockchain-based framework. This work will envision orchestrating three essential elements with harmony: scientific advancement (Healthcare and EHR), technology integration (Blockchain Technology), and patient empowerment (access, awareness, and control).

Exploring the potential of OMOP common data model for process mining in healthcare

BACKGROUND AND OBJECTIVE

Recently, Electronic Health Records (EHR) are increasingly being converted to Common Data Models (CDMs), a database schema designed to provide standardized vocabularies to facilitate collaborative observational research. To date, however, rare attempts exist to leverage CDM data for healthcare process mining, a technique to derive process-related knowledge (e.g., process model) from event logs. This paper presents a method to extract, construct, and analyze event logs from the Observational Medical Outcomes Partnership (OMOP) CDM for process mining and demonstrates CDM-based healthcare process mining with several real-life study cases while answering frequently posed questions in process mining, in the CDM environment.

METHODS

We propose a method to extract, construct, and analyze event logs from the OMOP CDM for process types including inpatient, outpatient, emergency room processes, and patient journey. Using the proposed method, we extract the retrospective data of several surgical procedure cases (i.e., Total Laparoscopic Hysterectomy (TLH), Total Hip Replacement (THR), Coronary Bypass (CB), Transcatheter Aortic Valve Implantation (TAVI), Pancreaticoduodenectomy (PD)) from the CDM of a Korean tertiary hospital. Patient data are extracted for each of the operations and analyzed using several process mining techniques.

RESULTS

Using process mining, the clinical pathways, outpatient process models, emergency room process models, and patient journeys are demonstrated using the extracted logs. The result shows CDM's usability as a novel and valuable data source for healthcare process analysis, yet with a few considerations. We found that CDM should be complemented by different internal and external data sources to address the administrative and operational aspects of healthcare processes, particularly for outpatient and ER process analyses.

CONCLUSION

To the best of our knowledge, we are the first to exploit CDM for healthcare process mining. Specifically, we provide a step-by-step guidance by demonstrating process analysis from locating relevant CDM tables to visualizing results using process mining tools. The proposed method can be widely applicable across different institutions. This work can contribute to bringing a process mining perspective to the existing CDM users in the changing Hospital Information Systems (HIS) environment and also to facilitating CDM-based studies in the process mining research community.

Perspectives of Patients, Health Care Professionals, and Developers Toward Blockchain-Based Health Information Exchange: Qualitative Study

Background

Although the electronic health record system adoption rate has reached 96% in the United States, implementation and usage of health information exchange (HIE) is still lagging behind. Blockchain has come into the spotlight as a technology to solve this problem. However, there have been no studies assessing the perspectives of different stakeholders regarding blockchain-based patient-centered HIE.

Objective

The objective of this study was to analyze the awareness among patients, health care professionals, and information technology developers toward blockchain-based HIE, and compare their different perspectives related to the platform using a qualitative research methodology.

Methods

In this qualitative study, we applied grounded theory and the Promoting Action on Research Implementation in the Health Service (PARiHS) framework. We interviewed 7 patients, 7 physicians, and 7 developers, for a total of 21 interviewees.

Results

Regarding the leakage of health information, the patient group did not have concerns in contrast to the physician and developer groups. Physicians were particularly concerned about the fact that errors in the data cannot be easily fixed due to the nature of blockchain technology. Patients were not against the idea of providing information for clinical trials or research institutions. They wished to be provided with the results of clinical research rather than being compensated for providing data. The developers emphasized that blockchain must be technically mature before it can be applied to the health care scene, and standards of medical information to be exchanged must first be established.

Conclusions

The three groups’ perceptions of blockchain were generally positive about the idea of patients having the control of sharing their own health information. However, they were skeptical about the cooperation among various institutions and implementation for data standardization in the establishment process, in addition to how the service will be employed in practice. Taking these factors into consideration during planning, development, and operation of a platform will contribute to establishing practical treatment plans and tracking in a more convenient manner for both patients and physicians. Furthermore, it will help expand the related research and health management industry based on blockchain.