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  • Optimsing Database Combinations for Literature Searches in Systematic Reviews

    Optimsing Database Combinations for Literature Searches in Systematic Reviews

    Use of multiple databases together with additional search strategies is often suggested to search relevant references for systematic reviews. (1,2) For instance, the Cochrane Handbook recommends using at least MEDLINE and Cochrane Central as well as EMBASE, when available, to search randomized controlled trials (RCTs). (3) However, using multiple databases can be strenuous and time consuming owing to the database-specific syntax of search strategies and differences of field codes and proximity operators between interfaces. Another difficulty is the different thesaurus terms between databases that may hamper translation. In addition, it is inconvenient for reviewers to screen more and possibly irrelevant titles and abstracts. Last but not least, limited access and subscriptions make the process all the more tedious and challenging. (4)

    In contrast, some studies exist in the literature that investigate the value of using multiple databases for different topics. Some of these studies report no effect on the outcome by searching more than one databases, thus proving just one database to be sufficient. (5,6) While others have reported a single database to be insufficient to retrieve all references for systematic reviews. (7) Majority of articles on this topic base their conclusions on the coverage of databases, (8) while many have failed to identify an acceptable number of databases to be searched. (9) Having said that, the presence of an article in a database does not guarantee it will be found in a search in that database. Therefore, the ideal database or a certain number of databases to be searched for relevant references for a systematic review remains unclear.(4)

    A recent prospective study has done some research in this area with an aim to determine the combination of databases to be searched for systematic reviews to obtain efficient results by means of minimizing the burden for the investigators and not the validity of the research by missing relevant references.(4)  This study recommended the biomedical searches to be performed using a combination of the following four databases, viz. EMBASE, MEDLINE (plus Epub ahead of print), Web of Science Core Collection, and Google Scholar. Use of this combination showed 93% of the systematic reviews to obtain levels of recall to be considered acceptable (> 95%). Unique results from specialized databases that closely match systematic review topics indicated their use whenever applicable, for e.g. PsycINFO for reviews in the fields of behavioural sciences and mental health or CINAHL for reviews on the topics of nursing and/or allied health.(4)

    Similarly, researchers at Erasmus University Medical Center (MC) have developed a methodology for generating comprehensive search strategies. (10) This methodology encompasses all steps of the search process, starting with a question and resulting in thorough search strategies in multiple databases. Researchers believe that this can prove to be a robust method to create high-quality, vigorous searches in multiple databases in a relatively short time frame. (10)

    Another systematic review has also showed that searching Medline alone for systematic reviews of exercise or other unconventional therapies is likely to be inadequate, while additional specialised databases along with checking reference lists and contacting experts can prove to be most effective for including all relevant papers in the review. (11)

    However, skills and experience of the searcher is an important aspect that can play a role in the efficacy of the search strategies being used. (12) Non-structured searches and searches with lower recall may even miss out relevant references. This can be solved with additional efforts like hand/cursory searching, looking up references, and contacting key players, which might add the extra references in the search.(4)

    To sum it all up, there need to be ways to optimize multiple databases and/or combinations in order to include all the relevant references in systematic reviews. Some researchers might suggest a combination of a few databases, depending on the topic/area of research in a particular systematic review. Majority of evidence available in the literature states that searching only one database may prove to be insufficient, thus leading to missing references. In addition, checking reference lists as well as contacting key experts to include all the necessary information and insights is recommended.

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    References

    1. Levay P, Raynor M, Tuvey D. The contributions of MEDLINE, other bibliographic databases and various search techniques to NICE public health guidance. Evid Based Libr Inf Pract 2015; 10:50–68.
    2. Beyer FR, Wright K. Can we prioritise which databases to search? A case study using a systematic review of frozen shoulder management. Health Inf Libr J 2013; 30:49–58.
    3. Higgins JPT, Green S. Cochrane handbook for systematic reviews of interventions: The Cochrane Collaboration, London, United Kingdom. 2011.
    4. Bramer WM, Rethlefsen ML, Kleijnen J, et al. Optimal database combinations for literature searches in systematic reviews: a prospective exploratory study. Syst Rev 2017; 6(1):245.
    5. Aagaard T, Lund H, Juhl C. Optimizing literature search in systematic reviews—are MEDLINE, EMBASE and CENTRAL enough for identifying effect studies within the area of musculoskeletal disorders? BMC Med Res Methodol 2016; 16:161.
    6. Rice DB, Kloda LA, Levis B, et al. Are MEDLINE searches sufficient for systematic reviews and meta-analyses of the diagnostic accuracy of depression screening tools? A review of meta-analyses. J Psychosom Res 2016; 87:7–13.
    7. Bramer WM, Giustini D, Kramer BMR. Comparing the coverage, recall, and precision of searches for 120 systematic reviews in Embase, MEDLINE, and Google Scholar: a prospective study. Syst Rev 2016; 5:39.
    8. Hartling L, Featherstone R, Nuspl M, Shave K, Dryden DM, Vandermeer B. The contribution of databases to the results of systematic reviews: a cross-sectional study. BMC Med Res Methodol 2016; 16:1–13.
    9. Ross-White A, Godfrey C. Is there an optimum number needed to retrieve to justify inclusion of a database in a systematic review search? Health Inf Libr J 2017; 33:217–24.
    10. Bramer WM, de Jonge GB, Rethlefsen ML, et al. A systematic approach to searching: an efficient and complete method to develop literature searches. J Med Libr Assoc 2018; 106(4):531–541.
    11. Stevinson C, Lawlor DA. Searching multiple databases for systematic reviews: added value or diminishing returns? Complement Ther Med 2004; 12(4):228-32.
    12. Rethlefsen ML, Farrell AM, Osterhaus Trzasko LC, et al. Librarian co-authors correlated with higher quality reported search strategies in general internal medicine systematic reviews. J Clin Epidemiol 2015; 68:617–626.

    Written by: Ms. Tanvi Laghate

    MarksMan Healthcare

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  • How to Encourage Healthcare Data Sharing?

    How to Encourage Healthcare Data Sharing?

    Last few years have seen data as well as data exchange emerging as the new currency in healthcare. Data sharing is a powerful force that is transforming conventional relationships in the healthcare marketplace as the global healthcare platform moves from being volume-based to quality-based. (1) Around 30% of the stored global data is generated within the healthcare industry. Also, a single patient normally generates about 80 MB of data every year in the form of imaging and electronic medical records (EMRs). The abundance of such data has substantial clinical, financial as well as operational value for the healthcare industry. (2) Moreover, such data could enable new value pathways, which would be worth more than $300 billion annually in reduced costs alone. (3)

    However, at present, the essential value of these data has not been recognized to the fullest by the industry. Also, this value is realized only when the raw data is converted into knowledge that would lead the change in practice. It is also explained by more inclusive data sharing and insights from within the hospital or healthcare organization, health insurance partners and community stakeholders; and most importantly, by tailored partnering with individual patients to better understand chronic conditions, enhance adherence and compliance, boost self-care, and avoid costlier treatments at costlier sites of care within the hospital’s overall population base.2

    Data is the basis for healthcare and medical research, therefore data sharing expedites the progress of research. Data sharing in research is widely discussed in the literature. Conversely, there are seemingly no evidence-based incentives that promote data sharing. In order to fully utilize the power of data and data sharing, providers, payers, and purchasers must be willing to work together to share cost and quality data across the entire healthcare system; instead of treating data as an exclusive asset. Patients routinely receive care and services from different providers, health systems, and health plans. In such instances, health data may not be consistent; which can create gaps in coverage leading to uneven, uncoordinated care of poor quality and high costs.1

    Furthermore, in spite of numerous benefits, such as addressing emergencies on the global public health platform, data sharing is still not a common research practice. For example, the severe acute respiratory syndrome (SARS) disease was controlled within only 4 months after its appearance by a WHO-coordinated effort, which focused on extensive data sharing. Nevertheless, several studies have demonstrated as low rates of data sharing as 4.5% [as seen in the British Medical Journal (BMJ)] in the field of health care. The global spending on health and medical research is 85% of the total expenditure, out of which an estimated $170 billion is lost every year, leading to questions about the authenticity of scientific knowledge. Open data sharing should be vital to understand the source of ever expanding base of scientific knowledge. Open data will most certainly reduce waste in case of time, costs, and patient burden; eventually strengthening scientific knowledge by guaranteeing research integrity. (4)

    The increasing gap between healthcare costs and outcomes can be attributed to poor management of research insights, poor usage of available evidence, and poor capture of care experience as well as valuable data, all leading to lost opportunities as well as resources, and potential harm to patients. To bridge this gap, the research and operational arms of healthcare can be used effectively to effectively harness data and encourage data sharing. (5)

    Many approaches can be applied to encourage data sharing. While organisations are likely to favour an ‘opt-out’ model, expecting an opt-in approach based on active patient consent to be impractical that might yield low participation rate, patients must be conversant about the projected uses and benefits of sharing their data for research; which will generate awareness in data sharing and reduce the number of patients opting out. (6)

    Another approach that can possibly boost data sharing would be the use of incentives. A recent systematic review has identified strategies that would facilitate data sharing practices among researchers. These strategies include the introduction to data systems, such as electronic laboratory notebooks and databases for data deposition in order to integrate a credit system through data linkage; group collaborations to use data attribution as an incentive; association among groups by means of workshops and agendas for data sharing; implementation of data sharing policies; and campaigns to promote data sharing. These strategies emphasize on the need of rewards to increase the rate of data sharing and the only form of incentive that has been successfully used is via data attribution and advertising on websites. Therefore, studies assessing the attribution efficacy and advertising as a form of credit are crucial. (4)

    There are innumerable benefits of openness in research, such as verification of research findings, progress in health and medicine, increase in new insights as well as in research value, reduction in research waste, and promotion of transparency in research findings. However, there’s a lack of evidence-based incentives for researchers that hinders data sharing even in today’s evidence-based world. We have tried to suggest ways to encourage data sharing through the use of incentives. Using strategies like implementation of data systems can be adopted even by journals to use as reward for promoting reproducible and sharable research. (4,7)

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    References

    1. Steele G. The culture of data sharing has to change. September, 2016. 
    2. Huesch MD, Mosher TJ. Using it of losing it? The case of data scientists inside healthcare. May, 2017. 
    3. Kayyali B, Knott D, Van Kuiken S. The big-data revolution in US healthcare: Accelerating value and innovation. McKinsey. April, 2013. 
    4. Rowhani-Farid A, Allen M, Barnett AG, et al. What incentives increase data sharing in health and medical research? A systematic review. Research Integrity and Peer Review 2017; 2:4.
    5. Lee CH, Yoon H-J. Medical big data: promise and challenges. Kidney Research and Clinical Practice 2017; 36(1):3-11.
    6. New JP, Leather D, Bakerly ND, et al. Putting patients in control of data from electronic health records. BMJ 2018; 360:j5554
    7. Ioannidis JA, Khoury MJ. Assessing value in biomedical research: The PQRST of appraisal and reward. JAMA 2014; 312(5):483–4.

    Written by: Ms. Tanvi Laghate

    MarksMan Healthcare

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  • How to Improve Healthcare Outcomes with Key Analytic Tools?

    How to Improve Healthcare Outcomes with Key Analytic Tools?

    Healthcare outcomes are defined as the changes observed and/or recorded in health status of individual or population patient/s usually due to an intervention, measures or specific healthcare investment. (1) The goal is to save the lives, shorten hospital stays and build healthier communities relying on preventative measures. (2) The fundamental steps of improving outcomes are measuring, reporting and analysing the outcomes. The efficient synthesis, organization and analysis of healthcare data offer the healthcare providers and other healthcare stakeholders with systematic and insightful treatment, measures and diagnosis. This may lead to higher patient care quality and better outcomes at lower costs.

    Healthcare industries generate a huge amount of information known as ‘big data’, driven by record keeping, compliance and regulatory requirement, potential to improve healthcare deliveries, and digitalization of historic data. (3) It include the clinical data from hospitals, clinics, pharmacies, pathological laboratories, diagnostic/imaging reports, healthcare insurances, and administrative data; individual patient data in electronic patient records (EPR) during various phases of clinical trials; pre-clinical data; hospitalization frequency data; research articles and reviews in scientific and medical journal; and information from various healthcare data resources; social media posts on different platforms; and less patient-specific information such as emergency care, news feed and healthcare magazines. (4) As per reports the data of U.S. alone may reach 1024 gigabyte soon. (3) There is need of rapidly transforming the volumes of aggregated healthcare data to value-based healthcare. 

    The analysis and assessment of huge healthcare data can be performed using advance platforms and tools with ability to handle structured, semi-structured or unstructured data. The data from random sources need to connect, match, cleanse and prepared for processing using three main steps of extract, transform and load. (4) The key platforms and tools to handle ‘big data’ are the Hadoop Distributed File System, MapReduce, PIG and PIG Latin, Hive, Jaql, Zookeeper, HBase, Cassandra, Oozie, Lucene, Avro, Mahout. (3) The analytic tools combine knowledge and data driven insights for identifying risks-factor and augmentation. These analytic tools have important applications for queries, reports, online analytical processing (OLAP) and data mining. (3) These analytic tools can search and analyse massive quantity of information from past treatments, latest published researches and healthcare databases to predict outcomes for individual patient. (5)

    Data analytic tools benefit all the components of healthcare system to improve healthcare outcomes. These components are healthcare service providers, patients, payers, stakeholders and managements. (6) Healthcare providers can develop new strategies and plan to care for patients such as reduce unnecessary hospitalizations and expenses. The patients at greatest risk of readmission can be identified and get guidance on follow ups for efficient resource utilization to save a huge amount of money spent each year on unnecessary hospitalization.

    The time gap always exists between a clinical event and the information to reach healthcare decision makers which could have bring the positive outcomes. The near real-time health surveillance can be performed using the information from social media blogs, micro-blogging on social networking sites such as Twitter and Facebook, and newspaper articles. (7) These social media networks provide information on the current locations by geo-tagged alerts. Real time analytic tools bring together the disparate information from various resources to the point of patient care, where the benefit can really be life-saving. It offers healthcare system access the most up-to date information. It realigns task based on priorities of healthcare providers, stakeholders, and insurers to improve healthcare outcomes. It addresses the gaps in care, quality, risk, utilization and regulatory requirement to support the improvements in clinical and quality outcomes; and financial performances. It provides a real-time report stating the real healthcare status of a patient and suggestions on improvement of the quality, achievement of compliance and realization of full reimbursement for their services. (8)

    It is often difficult for patients and clinician to keep the track of various healthcare organization-specific programs. The analytic tools may provide clinicians the information on a right program an eligible patient may enrol at a right time to help improve care and decrease costs. (8) The healthcare providers can assess patient-specific eligibility, gaps in care, risk scores, and historical medical information at the point of care which can be easily integrated into their existing operational model.

    The analytic tools improve healthcare outcomes by reducing the efforts and time required to handle ‘big data’ and conversion of volume to value-based information. These tools help encourage quality care to the patients benefitting payers as well as investors. The analytic tools would significantly support the advancement of medical and health science.

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    References

    1. Velentgas P., Dreyer N.A., and Wu W. A. (eds) Outcome Definition and Measurement. In ‘Developing a Protocol for Observational Comparative Effectiveness research: A User’s Guide’. Rockville,MD: Agency for Healthcare Research and Quality; AHRQ Publication No. 12(13)-EHC099, 2013.
    2. Kumar P. How real time analytics improves outcomes in healthcare. Published online on ‘IBM Cloud Blog’ dated June 19, 2017.
    3. Raghupathi W. and Raghupathi V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems 2, 3
    4. Gandomi A., and Haider M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of information Management 35, 137-144.
    5. Winters-Miner L.A. (2014) Seven ways predictive analytics can improve healthcare. Medical predictive analytics have the potential to revolutionize healthcare around the world. Published online on ‘Elsevier’s Daily stories for the science, Technology and health communities’ on Oct 06, 2014.
    6. Sun J. and Reddy C.K. (2013). Big data analytics for healthcare. Published in ‘KDD 2013 Proceedings of the 19th ACM SIAM International Conference on Knowledge Discovery and Data Mining’ held at Austin, TX, pg 1525-1525.
    7. Lee K., Agrawal A., and Choudhary A. (2013) Real-time disease surveillance using Twitter data: demonstration on flu and cancer. Published in ‘KDD 2013 proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining’, held at Chicago, Illinois, USA, pg 1474-1477.
    8. Rizzo D. The power of real-time analytics at the point of care. Published online on ‘Health IT Outcomes: Guest Column’ dated Dec 14, 2015.

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  • How Patient Records Abstraction Can Help in Healthcare Decision Making?

    How Patient Records Abstraction Can Help in Healthcare Decision Making?

    Patient Records Abstraction (PRA) is a process done manually by searching through a medical record to identify data required for a particular or secondary use. It consists of direct matching of information found in the record to the data required, but also includes operations on the data such as categorizing, coding, transforming, interpreting, summarizing, and calculating. The abstraction, in the end, summarizes information about a patient for a specific secondary data use. (1) PRA typically involves reviewing patient files and abstracting (i.e., extracting) key data, which are then entered into electronic files. (2) Depending on the measure or purpose, there can be different sources for data collection such as paper medical records, electronic medical records (EMR), patient surveys, administrative databases, etc.

    PRA helps in reviewing large or small data sets and documents for information which can be helpful in the future for decision making. (3) It often involves collecting organizationally-defined, clinically relevant data elements, which don’t electronically convert, from the legacy system into the new target system. This process, therefore, makes detailed patient data instantly available in the electronic chart in a faster, accurate and cost-effective manner; (4) facilitating access to care without referring to a paper chart or an EMR. (5)

    In order to make informed decisions with the help of PRA, a tool referred to as ‘abstraction hierarchy’ (AH) is often implemented to facilitate cognitive work analysis (CWA). (6) These hierarchies can be used to develop depictions of patient care in line with biomedical knowledge, making medical problem solving easier, and act as a frame of reference. (7)

    Studies exploring different aspects of AH also suggest that, it can be useful in implementing shared decision making (SDM) in order to improve patient care through their active engagement. Implementing SDM would be an advantageous approach to care, as patient involvement in decision making can result in improved health outcomes, thus providing an enhanced ethical framework for clinicians to deliver appropriate care and improved efficiency of the health system.7

    Researchers have found a way to mine huge amounts of patient data with the widespread use of EMR for identifying the best predictors of health outcomes. Also, every EMR system possibly has only a subset of the information necessary for a particular clinical trial.  This is where PRA can come into play to provide the necessary, overall data, thus substantially saving time and energy. (8) Consistent improvement in healthcare data quality plays a vital role in planning, development, and maintenance of healthcare services. This improvement can affect clinical and administrative decision making in many ways, thereby increasing patient safety, and facilitating the efficacy of clinical care pathways. (9)

    In addition to these advancements in healthcare technologies, the concept of ‘Big Data’ is emerging, which seems to have been first derived from an IT strategic consulting group’s approach to manage volume, velocity, and variety of data. (10) Researchers believe that the EMRs that contain huge volumes of patient data in variety of domains could also be considered as ‘Big Data’.  This is owing to the reports stating the United States alone will soon witness one billion patient visits documented per year in EMR systems. (11) Besides, the amount of additional data available about medical conditions, underlying genetics, medications, and treatment approaches is high. (12)

    Furthermore, the use of ‘real-world data’ (RWD) that contributes to the ‘real-world evidence’ (RWE) is also on the rise. RWD and associated RWE may constitute valid scientific evidence depending on the characteristics of the data. For making better choices about health and health care requires the best possible evidence. Sadly, many decisions made today lack the high-quality evidence derived from randomized, controlled trials or well-designed observational studies. Therefore, rich, diverse sources of digital data such as, EMRs, claims data, consumer data, chart reviews, which can cumulatively be referred to as ‘Big Data’- are becoming widely available for research, thus facilitating data extraction with the help of robust abstraction tools. To add to this, concept of chart abstraction methodologies that will integrate physician insights is also emerging. This will ensure better understanding of- i) various ways to address common obstacles and limitations to RWD collection, and ii) the importance community physician relationships for study implementation and success. The research and health care communities, consequently, have the opportunity to support improved healthcare decision making. (13)

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    References

    1. Nahm M. Data accuracy in medical record abstraction. The University of Texas School of Health Information Sciences at Houston.
    2. Half R. What skills do you need to be an electronic medical records abstractor/auditor? July, 2016.
    3. Rasmussen D. How data abstraction is going to change healthcare forever. September, 2016.
    4. Improving patient records: Conclusions and Recommendations. The computer-based patient record: An essential technology for health care. 1997.
    5. Nahm M. Data accuracy in medical record abstraction. The University of Texas School of Health Information Sciences at Houston.
    6. St-Maurice JD, Burns CM. Modeling Patient Treatment With Medical Records: An Abstraction Hierarchy to Understand User Competencies and Needs. Eysenbach G, ed. JMIR Human Factors 2017; 4(3):e16.
    7. Hajdukiewicz JR, Vicente KJ, Doyle DJ, et al. Modeling a medical environment: an ontology for integrated medical informatics design. Int J Med Inform. 2001; 62(1):79–99.
    8. Jones G. EMR to EDC for RWE. May, 2017.
    9. Adeleke IT, Adekanye AO, Onawola KA, et al. Data quality assessment in healthcare: a 365-day chart review of inpatients’ health records at a Nigerian tertiary hospital. Journal of the American Medical Informatics Association : JAMIA. 2012; 19(6):1039-1042.
    10. Laney D. 3D Data Management: Controlling Data Volume, Velocity, and Variety. META Group; 2001.
    11. Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc 2013; 20(1):117–21.
    12. Ross MK, Wei W, Ohno-Machado L. “Big Data” and the Electronic Health Record. Yearbook of Medical Informatics 2014; 9(1):97-104.
    13. Califf RM, et al. Transforming evidence generation to support health and health care decisions. N Engl J Med 2016; 375:2395-2400

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