JMIR Publications

JMIR Medical Informatics

Clinical informatics, decision support for health professionals, electronic health records, and ehealth infrastructures.


Journal Description

JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.

Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2015: 4.532), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.

JMIR Medical Informatics journal features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs (ready for deposit in PubMed Central/PubMed). The site is optimized for mobile and iPad use.

JMIR Medical Informatics adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR, the worlds' leading medical journal in health sciences / health services research and health informatics (


Recent Articles:

  • VR Oculus Headset. Source: Pixabay; Copyright: Florian Pircher; URL:; License: Public Domain (CC0).

    Virtual Reality as an Adjunct Home Therapy in Chronic Pain Management: An Exploratory Study


    Background: Virtual reality (VR) therapy has been successfully used as an adjunct therapy for the management of acute pain in adults and children, and evidence of potential efficacy in other health applications is growing. However, minimal research exists on the value of VR as an intervention for chronic pain. Objective: This case series examined the value of VR to be used as an adjunctive therapy for chronic pain patients in their own homes. Methods: An exploratory approach using a case series and personal interviews was used. Ten chronic pain patients received VR therapy for 30 min on alternate days for 1 month. Pre- and postexposure (immediately afterwards, 3 h, and at 24 h) pain assessment was recorded using the Numerical Rating Scale (NRS), and weekly using the Brief Pain Inventory (BPI) and Self-completed Leeds Assessment of Neuropathic Symptoms and Signs pain scale (S-LANSS). Terminal semistructured personal interviews with the patients were also undertaken. Results: Of the 8 patients who completed the study, 5 of them reported that pain was reduced during the VR experience but no overall treatment difference in pain scores postexposure was observed. VR was not associated with any serious adverse events, although 60% of patients reported some cybersickness during some of the experiences. Conclusions: Of note is that the majority of these study participants reported a reduction in pain while using the VR but with highly individualized responses. One patient also reported some short-term improved mobility following VR use. Some evidence was found for the short-term efficacy of VR in chronic pain but no evidence for persistent benefits.

  • An example screenshot of IDEAL-X’s interface (montage). Source: The Authors / Imgur /; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    Effective Information Extraction Framework for Heterogeneous Clinical Reports Using Online Machine Learning and Controlled Vocabularies


    Background: Extracting structured data from narrated medical reports is challenged by the complexity of heterogeneous structures and vocabularies and often requires significant manual effort. Traditional machine-based approaches lack the capability to take user feedbacks for improving the extraction algorithm in real time. Objective: Our goal was to provide a generic information extraction framework that can support diverse clinical reports and enables a dynamic interaction between a human and a machine that produces highly accurate results. Methods: A clinical information extraction system IDEAL-X has been built on top of online machine learning. It processes one document at a time, and user interactions are recorded as feedbacks to update the learning model in real time. The updated model is used to predict values for extraction in subsequent documents. Once prediction accuracy reaches a user-acceptable threshold, the remaining documents may be batch processed. A customizable controlled vocabulary may be used to support extraction. Results: Three datasets were used for experiments based on report styles: 100 cardiac catheterization procedure reports, 100 coronary angiographic reports, and 100 integrated reports—each combines history and physical report, discharge summary, outpatient clinic notes, outpatient clinic letter, and inpatient discharge medication report. Data extraction was performed by 3 methods: online machine learning, controlled vocabularies, and a combination of these. The system delivers results with F1 scores greater than 95%. Conclusions: IDEAL-X adopts a unique online machine learning–based approach combined with controlled vocabularies to support data extraction for clinical reports. The system can quickly learn and improve, thus it is highly adaptable.

  • Doctor with patient accessing patient records. Source:; Copyright: Peoplecreations; URL:; License: Creative Commons Attribution (CC-BY).

    Design, Implementation, and Evaluation of Self-Describing Diabetes Medical Records: A Pilot Study


    Background: Each patient’s medical record consists of data specific to that patient and is therefore an appropriate source to adapt educational information content. Objectives: This study aimed to design and implement an information provision system based on the medical records of diabetic patients and to investigate the attitudes of users toward using this product. Methods: The study was organized into three phases: need analysis, design and implementation, and final evaluation. The aim of the need analysis phase was to investigate the questioning behavior of the patient in the real-world context. The design and implementation phase consisted of four stages: determining the minimum dataset for diabetes medical records, collecting and validating content, designing and implementing a diabetes electronic medical record system, and data entry. Evaluating the final system was done based on the constructs of the technology acceptance model in the two dimensions of perceived usefulness and perceived ease of use. A semistructured interview was used for this purpose. Results: Three main categories were extracted for the patient’s perceived usefulness of the system: raising the self-awareness and knowledge of patients, improving their self-care, and improving doctor-patient interaction. Both patients and physicians perceived the personalized sense of information as a unique feature of the application and believed that this feature could have a positive effect on the patient’s motivation for learning and using information in practice. Specialists believed that providing personal feedback on the patient’s lab test results along with general explanations encourages the patients to read the content more precisely. Moreover, accessing medical records and helpful notes was a new and useful experience for the patients. Conclusions: One of the key perceived benefits of providing tailored information in the context of medical records was raising patient awareness and knowledge. The results obtained from field observations and interviews have shown that patients were ready to accept the system and had a positive attitude when it was put into practice. The findings related to user attitude can be used as a guideline to design the next phase of the research (ie, investigation of system effectiveness on patient outcomes).

  • Source: Flickr; Copyright: NEC Corporation of America; URL:; License: Creative Commons Attribution (CC-BY).

    A Software Framework for Remote Patient Monitoring by Using Multi-Agent Systems Support


    Background: Although there have been significant advances in network, hardware, and software technologies, the health care environment has not taken advantage of these developments to solve many of its inherent problems. Research activities in these 3 areas make it possible to apply advanced technologies to address many of these issues such as real-time monitoring of a large number of patients, particularly where a timely response is critical. Objective: The objective of this research was to design and develop innovative technological solutions to offer a more proactive and reliable medical care environment. The short-term and primary goal was to construct IoT4Health, a flexible software framework to generate a range of Internet of things (IoT) applications, containing components such as multi-agent systems that are designed to perform Remote Patient Monitoring (RPM) activities autonomously. An investigation into its full potential to conduct such patient monitoring activities in a more proactive way is an expected future step. Methods: A framework methodology was selected to evaluate whether the RPM domain had the potential to generate customized applications that could achieve the stated goal of being responsive and flexible within the RPM domain. As a proof of concept of the software framework’s flexibility, 3 applications were developed with different implementations for each framework hot spot to demonstrate potential. Agents4Health was selected to illustrate the instantiation process and IoT4Health’s operation. To develop more concrete indicators of the responsiveness of the simulated care environment, an experiment was conducted while Agents4Health was operating, to measure the number of delays incurred in monitoring the tasks performed by agents. Results: IoT4Health’s construction can be highlighted as our contribution to the development of eHealth solutions. As a software framework, IoT4Health offers extensibility points for the generation of applications. Applications can extend the framework in the following ways: identification, collection, storage, recovery, visualization, monitoring, anomalies detection, resource notification, and dynamic reconfiguration. Based on other outcomes involving observation of the resulting applications, it was noted that its design contributed toward more proactive patient monitoring. Through these experimental systems, anomalies were detected in real time, with agents sending notifications instantly to the health providers. Conclusions: We conclude that the cost-benefit of the construction of a more generic and complex system instead of a custom-made software system demonstrated the worth of the approach, making it possible to generate applications in this domain in a more timely fashion.

  • Image of Telehealth monitoring devices. Copyright: Cathy Soreny via Optical Jukebox; URL:; License: Licensed by the authors.

    Does Telehealth Monitoring Identify Exacerbations of Chronic Obstructive Pulmonary Disease and Reduce Hospitalisations? An Analysis of System Data


    Background: The increasing prevalence and associated cost of treating chronic obstructive pulmonary disease (COPD) is unsustainable. Health care organizations are focusing on ways to support self-management and prevent hospital admissions, including telehealth-monitoring services capturing physiological and health status data. This paper reports on data captured during a pilot randomized controlled trial of telehealth-supported care within a community-based service for patients discharged from hospital following an exacerbation of their COPD. Objective: The aim was to undertake the first analysis of system data to determine whether telehealth monitoring can identify an exacerbation of COPD, providing clinicians with an opportunity to intervene with timely treatment and prevent hospital readmission. Methods: A total of 23 participants received a telehealth-supported intervention. This paper reports on the analysis of data from a telehealth monitoring system that captured data from two sources: (1) data uploaded both manually and using Bluetooth peripheral devices by the 23 participants and (2) clinical records entered as nursing notes by the clinicians. Rules embedded in the telehealth monitoring system triggered system alerts to be reviewed by remote clinicians who determined whether clinical intervention was required. We also analyzed data on the frequency and length (bed days) of hospital admissions, frequency of hospital Accident and Emergency visits that did not lead to hospital admission, and frequency and type of community health care service contacts—other than the COPD discharge service—for all participants for the duration of the intervention and 6 months postintervention. Results: Patients generated 512 alerts, 451 of which occurred during the first 42 days that all participants used the equipment. Patients generated fewer alerts over time with typically seven alerts per day within the first 10 days and four alerts per day thereafter. They also had three times more days without alerts than with alerts. Alerts were most commonly triggered by reports of being more tired, having difficulty with self-care, and blood pressure being out of range. During the 8-week intervention, and for 6-month follow-up, eight of the 23 patients were hospitalized. Hospital readmission rates (2/23, 9%) in the first 28 days of service were lower than the 20% UK norm. Conclusions: It seems that the clinical team can identify exacerbations based on both an increase in alerts and the types of system-generated alerts as evidenced by their efforts to provided treatment interventions. There was some indication that telehealth monitoring potentially delayed hospitalizations until after patients had been discharged from the service. We suggest that telehealth-supported care can fulfill an important role in enabling patients with COPD to better manage their condition and remain out of hospital, but adequate resourcing and timely response to alerts is a critical factor in supporting patients to remain at home. Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN): 68856013; (Archived by WebCite at

  • Patient and Medical Record. TOC picture created by authors from two images. Source: Pixabay. Medical Record Health Patient form, Author vjohns1580; and Hospital Labor Delivery Mom, Author Parentingupstream. Public Domain. Licensed under a CC0.

    Patient Similarity in Prediction Models Based on Health Data: A Scoping Review


    Background: Physicians and health policy makers are required to make predictions during their decision making in various medical problems. Many advances have been made in predictive modeling toward outcome prediction, but these innovations target an average patient and are insufficiently adjustable for individual patients. One developing idea in this field is individualized predictive analytics based on patient similarity. The goal of this approach is to identify patients who are similar to an index patient and derive insights from the records of similar patients to provide personalized predictions.. Objective: The aim is to summarize and review published studies describing computer-based approaches for predicting patients’ future health status based on health data and patient similarity, identify gaps, and provide a starting point for related future research. Methods: The method involved (1) conducting the review by performing automated searches in Scopus, PubMed, and ISI Web of Science, selecting relevant studies by first screening titles and abstracts then analyzing full-texts, and (2) documenting by extracting publication details and information on context, predictors, missing data, modeling algorithm, outcome, and evaluation methods into a matrix table, synthesizing data, and reporting results. Results: After duplicate removal, 1339 articles were screened in abstracts and titles and 67 were selected for full-text review. In total, 22 articles met the inclusion criteria. Within included articles, hospitals were the main source of data (n=10). Cardiovascular disease (n=7) and diabetes (n=4) were the dominant patient diseases. Most studies (n=18) used neighborhood-based approaches in devising prediction models. Two studies showed that patient similarity-based modeling outperformed population-based predictive methods. Conclusions: Interest in patient similarity-based predictive modeling for diagnosis and prognosis has been growing. In addition to raw/coded health data, wavelet transform and term frequency-inverse document frequency methods were employed to extract predictors. Selecting predictors with potential to highlight special cases and defining new patient similarity metrics were among the gaps identified in the existing literature that provide starting points for future work. Patient status prediction models based on patient similarity and health data offer exciting potential for personalizing and ultimately improving health care, leading to better patient outcomes.

  • Source: Getty images. IStock. License purchased by the author.

    The State of Open Source Electronic Health Record Projects: A Software Anthropology Study


    Background: Electronic health records (EHR) are a key tool in managing and storing patients’ information. Currently, there are over 50 open source EHR systems available. Functionality and usability are important factors for determining the success of any system. These factors are often a direct reflection of the domain knowledge and developers’ motivations. However, few published studies have focused on the characteristics of free and open source software (F/OSS) EHR systems and none to date have discussed the motivation, knowledge background, and demographic characteristics of the developers involved in open source EHR projects. Objective: This study analyzed the characteristics of prevailing F/OSS EHR systems and aimed to provide an understanding of the motivation, knowledge background, and characteristics of the developers. Methods: This study identified F/OSS EHR projects on SourceForge and other websites from May to July 2014. Projects were classified and characterized by license type, downloads, programming languages, spoken languages, project age, development status, supporting materials, top downloads by country, and whether they were “certified” EHRs. Health care F/OSS developers were also surveyed using an online survey. Results: At the time of the assessment, we uncovered 54 open source EHR projects, but only four of them had been successfully certified under the Office of the National Coordinator for Health Information Technology (ONC Health IT) Certification Program. In the majority of cases, the open source EHR software was downloaded by users in the United States (64.07%, 148,666/232,034), underscoring that there is a significant interest in EHR open source applications in the United States. A survey of EHR open source developers was conducted and a total of 103 developers responded to the online questionnaire. The majority of EHR F/OSS developers (65.3%, 66/101) are participating in F/OSS projects as part of a paid activity and only 25.7% (26/101) of EHR F/OSS developers are, or have been, health care providers in their careers. In addition, 45% (45/99) of developers do not work in the health care field. Conclusion: The research presented in this study highlights some challenges that may be hindering the future of health care F/OSS. A minority of developers have been health care professionals, and only 55% (54/99) work in the health care field. This undoubtedly limits the ability of functional design of F/OSS EHR systems from being a competitive advantage over prevailing commercial EHR systems. Open source software seems to be a significant interest to many; however, given that only four F/OSS EHR systems are ONC-certified, this interest is unlikely to yield significant adoption of these systems in the United States. Although the Health Information Technology for Economic and Clinical Health (HITECH) act was responsible for a substantial infusion of capital into the EHR marketplace, the lack of a corporate entity in most F/OSS EHR projects translates to a marginal capacity to market the respective F/OSS system and to navigate certification. This likely has further disadvantaged F/OSS EHR adoption in the United States.

  • Clinician and patient viewing EMR data (Adobe stock photo).

    Progress in the Enhanced Use of Electronic Medical Records: Data From the Ontario Experience


    Background: This paper describes a change management strategy, including a self-assessment survey tool and electronic medical record (EMR) maturity model (EMM), developed to support the adoption and implementation of EMRs among community-based physicians in the province of Ontario, Canada. Objective: The aim of our study was to present an analysis of progress in EMR use in the province of Ontario based on data from surveys completed by over 4000 EMR users. Methods: The EMM and the EMR progress report (EPR) survey tool clarify levels of capability and expected benefits of improved use. Maturity is assessed on a 6-point scale (0-5) for 25 functions, across 7 functional areas, ranging from basic to more advanced. A total of 4214 clinicians completed EPR surveys between April 2013 and March 2016. Univariate and multivariate descriptive statistics were calculated to describe the survey results. Results: Physicians reported continual improvement over years of use, perceiving that the longer they used their EMR, the better patient care they provided. Those with at least two years of experience reported the greatest progress. Conclusions: From our analyses at this stage we identified: (1) a direct correlation between years of EMR use and EMR maturity as measured in our model, (2) a similar positive correlation between years of EMR use and the perception that these systems improve clinical care in at least four patient-centered areas, and (3) evidence of ongoing improvement even in advanced years of use. Future analyses will be supplemented by qualitative and quantitative data collected from field staff engagements as part of the new EMR practice enhancement program (EPEP).

  • Image sourced from and owned by the authors.

    Checking Questionable Entry of Personally Identifiable Information Encrypted by One-Way Hash Transformation


    Background: As one of the several effective solutions for personal privacy protection, a global unique identifier (GUID) is linked with hash codes that are generated from combinations of personally identifiable information (PII) by a one-way hash algorithm. On the GUID server, no PII is permitted to be stored, and only GUID and hash codes are allowed. The quality of PII entry is critical to the GUID system. Objective: The goal of our study was to explore a method of checking questionable entry of PII in this context without using or sending any portion of PII while registering a subject. Methods: According to the principle of GUID system, all possible combination patterns of PII fields were analyzed and used to generate hash codes, which were stored on the GUID server. Based on the matching rules of the GUID system, an error-checking algorithm was developed using set theory to check PII entry errors. We selected 200,000 simulated individuals with randomly-planted errors to evaluate the proposed algorithm. These errors were placed in the required PII fields or optional PII fields. The performance of the proposed algorithm was also tested in the registering system of study subjects. Results: There are 127,700 error-planted subjects, of which 114,464 (89.64%) can still be identified as the previous one and remaining 13,236 (10.36%, 13,236/127,700) are discriminated as new subjects. As expected, 100% of nonidentified subjects had errors within the required PII fields. The possibility that a subject is identified is related to the count and the type of incorrect PII field. For all identified subjects, their errors can be found by the proposed algorithm. The scope of questionable PII fields is also associated with the count and the type of the incorrect PII field. The best situation is to precisely find the exact incorrect PII fields, and the worst situation is to shrink the questionable scope only to a set of 13 PII fields. In the application, the proposed algorithm can give a hint of questionable PII entry and perform as an effective tool. Conclusions: The GUID system has high error tolerance and may correctly identify and associate a subject even with few PII field errors. Correct data entry, especially required PII fields, is critical to avoiding false splits. In the context of one-way hash transformation, the questionable input of PII may be identified by applying set theory operators based on the hash codes. The count and the type of incorrect PII fields play an important role in identifying a subject and locating questionable PII fields.

  • OVERT-MED visual interface.

    Ontology-Driven Search and Triage: Design of a Web-Based Visual Interface for MEDLINE


    Background: Diverse users need to search health and medical literature to satisfy open-ended goals such as making evidence-based decisions and updating their knowledge. However, doing so is challenging due to at least two major difficulties: (1) articulating information needs using accurate vocabulary and (2) dealing with large document sets returned from searches. Common search interfaces such as PubMed do not provide adequate support for exploratory search tasks. Objective: Our objective was to improve support for exploratory search tasks by combining two strategies in the design of an interactive visual interface by (1) using a formal ontology to help users build domain-specific knowledge and vocabulary and (2) providing multi-stage triaging support to help mitigate the information overload problem. Methods: We developed a Web-based tool, Ontology-Driven Visual Search and Triage Interface for MEDLINE (OVERT-MED), to test our design ideas. We implemented a custom searchable index of MEDLINE, which comprises approximately 25 million document citations. We chose a popular biomedical ontology, the Human Phenotype Ontology (HPO), to test our solution to the vocabulary problem. We implemented multistage triaging support in OVERT-MED, with the aid of interactive visualization techniques, to help users deal with large document sets returned from searches. Results: Formative evaluation suggests that the design features in OVERT-MED are helpful in addressing the two major difficulties described above. Using a formal ontology seems to help users articulate their information needs with more accurate vocabulary. In addition, multistage triaging combined with interactive visualizations shows promise in mitigating the information overload problem. Conclusions: Our strategies appear to be valuable in addressing the two major problems in exploratory search. Although we tested OVERT-MED with a particular ontology and document collection, we anticipate that our strategies can be transferred successfully to other contexts.

  • Predictive analytics. Image Source: Author: geralt. Copyright: CC0 Public Domain.

    Patient-Specific Predictive Modeling Using Random Forests: An Observational Study for the Critically Ill

    Authors List:


    Background: With a large-scale electronic health record repository, it is feasible to build a customized patient outcome prediction model specifically for a given patient. This approach involves identifying past patients who are similar to the present patient and using their data to train a personalized predictive model. Our previous work investigated a cosine-similarity patient similarity metric (PSM) for such patient-specific predictive modeling. Objective: The objective of the study is to investigate the random forest (RF) proximity measure as a PSM in the context of personalized mortality prediction for intensive care unit (ICU) patients. Methods: A total of 17,152 ICU admissions were extracted from the Multiparameter Intelligent Monitoring in Intensive Care II database. A number of predictor variables were extracted from the first 24 hours in the ICU. Outcome to be predicted was 30-day mortality. A patient-specific predictive model was trained for each ICU admission using an RF PSM inspired by the RF proximity measure. Death counting, logistic regression, decision tree, and RF models were studied with a hard threshold applied to RF PSM values to only include the M most similar patients in model training, where M was varied. In addition, case-specific random forests (CSRFs), which uses RF proximity for weighted bootstrapping, were trained. Results: Compared to our previous study that investigated a cosine similarity PSM, the RF PSM resulted in superior or comparable predictive performance. RF and CSRF exhibited the best performances (in terms of mean area under the receiver operating characteristic curve [95% confidence interval], RF: 0.839 [0.835-0.844]; CSRF: 0.832 [0.821-0.843]). RF and CSRF did not benefit from personalization via the use of the RF PSM, while the other models did. Conclusions: The RF PSM led to good mortality prediction performance for several predictive models, although it failed to induce improved performance in RF and CSRF. The distinction between predictor and similarity variables is an important issue arising from the present study. RFs present a promising method for patient-specific outcome prediction.

  • Health care professionals. Image sourced and copyright owned by authors.

    The Value of Electronic Medical Record Implementation in Mental Health Care: A Case Study


    Background: Electronic medical records (EMR) have been implemented in many organizations to improve the quality of care. Evidence supporting the value added to a recovery-oriented mental health facility is lacking. Objective: The goal of this project was to implement and customize a fully integrated EMR system in a specialized, recovery-oriented mental health care facility. This evaluation examined the outcomes of quality improvement initiatives driven by the EMR to determine the value that the EMR brought to the organization. Methods: The setting was a tertiary-level mental health facility in Ontario, Canada. Clinical informatics and decision support worked closely with point-of-care staff to develop workflows and documentation tools in the EMR. The primary initiatives were implementation of modules for closed loop medication administration, collaborative plan of care, clinical practice guidelines for schizophrenia, restraint minimization, the infection prevention and control surveillance status board, drug of abuse screening, and business intelligence. Results: Medication and patient scan rates have been greater than 95% since April 2014, mitigating the adverse effects of medication errors. Specifically, between April 2014 and March 2015, only 1 moderately severe and 0 severe adverse drug events occurred. The number of restraint incidents decreased 19.7%, which resulted in cost savings of more than Can $1.4 million (US $1.0 million) over 2 years. Implementation of clinical practice guidelines for schizophrenia increased adherence to evidence-based practices, standardizing care across the facility. Improved infection prevention and control surveillance reduced the number of outbreak days from 47 in the year preceding implementation of the status board to 7 days in the year following. Decision support to encourage preferential use of the cost-effective drug of abuse screen when clinically indicated resulted in organizational cost savings. Conclusions: EMR implementation allowed Ontario Shores Centre for Mental Health Sciences to use data analytics to identify and select appropriate quality improvement initiatives, supporting patient-centered, recovery-oriented practices and providing value at the clinical, organizational, and societal levels.

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  • Effect of Circumstance and Process on the Content of Chemical Components in Baishao

    Date Submitted: May 11, 2017

    Open Peer Review Period: May 13, 2017 - Jul 8, 2017

    Background: P. lactiflora and P. lactiflora var. trichocarpa are all cultured in mostly locations of Baishao in China. Mostly farmer do not differentiate. P. lactiflora and P. lactiflora var. trichoca...

    Background: P. lactiflora and P. lactiflora var. trichocarpa are all cultured in mostly locations of Baishao in China. Mostly farmer do not differentiate. P. lactiflora and P. lactiflora var. trichocarpa. The contents of paeoniflorin in Baishao in different locations are diverse. However, there are many medicinal ingredients such as gallic acid and albiflorin in Baishao in addition to paeoniflorin. Their contents are related to locations. Furthermore, the processing affects the efficacy of Baishao. Therefore, there is differene between Baishao and the raw root of P. lactiflora in chemically active ingredients. Objective: The objective of this study was to study the effect of circumstance and process on the content of chemical components in Bai Shao (Radix paeoniae A1ba). Methods: The content of gallic acid, catechin, albiflorin, paeoniflorin, benzoic acid and paeonol in raw Bai Shao in difference locations and that processed were determined with RP-HPLC. Then, the difference between the content of chemical components in raw and processed Bai Shao is analyzed. The correlation between circumstance and the content of chemical components in Bai Shao is analyzed also. Results: The result showed that there are differences between the content of chemical components in Bai Shao in different locations. Circumstance and age of Bai Shao affect the content of chemical components in Bai Shao. The content of albiflorin in Bai Shao increases along with the decrease of rainfall, integral air temperature and the increase of age. Conclusions: The circumstance, age and process affect the quality of Bai Shao. The process facilitates the realization of medicinal property and function of Bai Shao.