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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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  • How Data Visualization is Helping in Healthcare Decision Making?

    How Data Visualization is Helping in Healthcare Decision Making?

    Health informatics systems are evolving with an aim to revolutionize health and healthcare programs worldwide. However, turning this hope-filled vision into a reality will take enormous effort from thousands of designers, analysts, software engineers, usability specialists, and medical professionals. Albeit challenging, the role of information visualization and visual analytics processes is gaining importance. These algorithms, interactive designs, and analytic processes support exploration, monitoring, insight discovery, professional collaboration, and comprehensible presentations to patients, clinicians, policy makers, and the general public.

    Data visualization is the art of representing data in a pictorial or graphical format. Analyzing patterns and trends from large data sets can be a herculean process. Data visualization helps in simplifying this process and allows decision-makers to derive analytical results from information presented visually. Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognised easier with data visualization.

    Critical building blocks of healthcare analytics are data analytics and visual analytics. Both consist of systematic approaches, techniques and tools, clinical and business resources, used to interpret and analyze high volumes of dynamic patient data, clinical records, financial and other business data, and present findings to various audiences from the clinicians, hospital administration and management, to government and the public. Visual analysis refers to “the science of analytical reasoning facilitated by interactive visual interfaces”. Healthcare providers increasingly explore various visual and interactive techniques in generating and examining large graphs and charts, interactive visualizations, and 2D/3D visualization of discrete event simulation (DES) to understand complex and large datasets, identify and connections and trends, model and simulate healthcare events, and communicate and interpret the findings. Expected outcomes include more efficient and effective clinical performance monitoring and improvement, patient flow modeling and management, better patient care quality, safety and efficiency, better support for clinical costing and resource coordination, better planned growth and competitive advantage.

    Conventional visualization methods often require significant processing time, which limits high-throughput analysis. Interactive visualization systems maintain a closed loop between the user and the system and, thus, need to be very fast. Building such a system requires the development of new visualization methods; and there exists the need to design new and effective interaction techniques which are being developed by researchers. One of such techniques is ‘R’ that is being extensively used for statistical computing and graphics. Informatics for Integrating Biology and the Bedside (i2b2), an initiative sponsored by the NIH Roadmap National Centers for Biomedical Computing is another such program that provides a query tool that supplies aggregate counts and basic analyses of patient populations from clinical data warehouses (CDWs). i2b2 (i2b2 to Tableau) is effective at estimating patient cohort sizes and has an extendable architecture where plug-ins with additional features can be developed. Similarly, Hadoop is an open source program; which, according to researchers, is the most significant data processing platform for big data analytics in healthcare. It supports the processing and storage of extremely large data sets in a distributed computing environment, and will open up entire new research domains for discovery. To add to this list is Python, which is yet another solid and open scientific computing and visualization framework. Python is often known as the Swiss Army knife of programming languages–it can, among many things, support machine learning, web development, web scraping, desktop applications. While R is more narrowly focused on statistics compared to Python, it is also great at several things. First, it offers well-documented algorithms and tools for whatever you want to do in statistics. Second, it has fantastic visualization software (better than Python, it could be argued), and thirdly it is great at professional document generation for both reports and data education. Furthermore, recent developments in the R language, and in particular the Shiny package for R, have allowed R programmers to interactively show the output for R programs to Web browsers. The R and Shiny package reactively updates output in response to changing input by means of widgets (such as sliders and radio buttons) but owing to the flexible R language and combination with extension packages, the pharmacometrician has control in coding all elements of a population model, the appearance of the application’s user interface, and generated output.

    Today, data visualization solutions can be found everywhere in healthcare systems from hospital operations monitoring and patient profiling to demand projection and capacity planning. However, recent technological developments have made it easier to work with data than it has ever been in the past in case of business intelligence as well.Business Intelligence (BI) can be described as “a set of techniques and tools for the acquisition and transformation of raw data into meaningful and useful information for business analysis purpose”.

    Now, it’s not necessary to work with complex data sets that take a long time to sort and make sense of. Contemporary data dashboards allow users to access data in a visual manner that allows for faster and more accurate business decisions to be made. The payoff from joining the big-data and advanced-analytics management revolution is no longer in doubt.

    India also is witnessing healthcare analytics being rapidly adopted. Data coming from variety of sources are being recorded in different forms like patient records, patient satisfaction surveys, patient complaint registers, quality of care assessments, etc. Researchers in India feel that effective analysis of these large amounts of organizational data can lead to better decision making.

    Additionally, social media are opening remarkable possibilities for health and healthcare researchers. For the first time in history much of what we do is recorded online, and for the first time in history we have the tools to capture, analyze, and visualize these data. Moreover, health informatics databases and networks have amplified benefits with information visualization as it dramatically expands the capacity of patients, clinicians, and public health policy makers to make better decisions.

    To put them together in one sentence, “Visualizing business data (data visualization), the right way helps to find meaningful patterns in data (analytics) to analyze the business and make decisions (business intelligence) accordingly”.

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