• Is there a Flip Side of Electronic Medical Records?

    Is there a Flip Side of Electronic Medical Records?

    Maintaining a record of patients’ medical charts required a lot of physical labour before electronic medical/health records (EMRs/EHRs) became a routine.  This physical work provided a greater chance of human error and overlooked information and eventually, irregular maintenance of records. There was a fair chance of 5 out of 15 charts for a clinic day to be unavailable at any given time, which ultimately led to wasted time, space, motion and frequent defects to care. (1)

    Electronic medical records, in contrast, have eliminated the manual labour in maintaining patient charts, thus making data available at all times. Moreover, clinicians can remotely access patient files in a secure way. This also reduces the storage and inventory, thereby freeing up physical space within the hospital or office, allowing the relocation of human resources. This ultimately eliminates unnecessary movement and batch delivery in order to improve the flow of patients and information. As a consequence, all of this leads to the reduction in waste as well as improved quality of care for the patient. (1)

    However, even though EMRs are a part of nearly all physician practices in the west, implementing them has been quite challenging. In a recent report on EMR use, Medscape surveyed 15,285 US physicians across over 25 specialties, asking them questions about usage, specific system ratings, and vendor satisfaction. (2) Physicians elaborated on the impact of EMRs on practice operations and patient encounters. Largely, physicians opined that they faced a challenge with more screen time and less direct clinical face time with patients. Almost half of the respondents did not believe that EHRs improved documentation or for that matter, anything else at all. (3,4)

    Another survey by The Doctors Company in more than 3,400 US physicians found EMR systems to be falling short of provider expectations, reducing the joys of practicing medicine. Majority of these physicians reported EMR systems to have negatively impacted on the patient-provider relationship, clinical workflows, and clinical productivity. Sixty-one percent of physicians reported that value-based care and reimbursement would negatively impact their practice; while 63 percent of respondents opined it would negatively affect their earnings. (5) This general dissatisfaction among physicians with EMRs and value-based care resonates with the findings of a recent JAMIA study, which assessed provider satisfaction through 4 survey rounds during the phased implementation of EMRs. Findings from this study showed that provider dissatisfaction with EMRs and difficulties incorporating EMR technology into patient care may negatively impact patient satisfaction. (6)

    While EMRs have ample benefits, such as better accessibility to patient data, increased charge capture and enhanced preventative health, the inherent problems in embracing this technology cannot be overlooked. If an EMR is not supported with well-thought processes, hospitals may invest in complicated and expensive technologies that create more waste in a system already troubled with ineffectiveness. Implementation of EMRs may contribute to several disadvantages, such as lack of interoperability between technologies; increased costs of setting up as well as maintenance; drop in physician productivity; delays in documentation; constant need for updates and lack of accountability for doing so; and so on. Extensive use of EMRs can also lead to privacy violations. In addition, auto-population of data for new records can also result in inaccurate new records along with other technological errors. (1)

    Having said that, while advantages of EMRs to the physician, hospital or physicians’ office and patient alike are considerable, their disadvantages can often prevail over their benefits. To avoid these issues and curb the possible errors, hospitals and healthcare systems must perform a thorough evaluation of the EMR system before purchase and implementation. (1)

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    References

    1. Palma G. Electronic Health Records: The good, the Bad and the Ugly. October, 2013.
    2. Peckham C, Kane L. Medscape EHR report 2016: Physicians rate top EHRs. Medscape Business of Medicine. August, 2016.
    3. Selanikio J. Physician Satisfaction with EHRs: It’s Even Worse Than You Think. January, 2017.
    4. Sinsky C, Colligan L, Li L, et al. Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties. Ann Intern Med 2016; 165(11):753-760.
    5. Monica K. 61% of Physicians Say EHR Systems Reduce Clinical Efficiency. October, 2018.
    6. Meyerhoefer C, Sherer S, Deily M, et al. Provider and patient satisfaction with the integration of ambulatory and hospital EHR systems. J Am Med Inform Assoc 2018; 25(8):1054-1063.
  • Forecasting the Analytics Landscape to Anticipate Future Patient Requirements

    Forecasting the Analytics Landscape to Anticipate Future Patient Requirements

    There is heaps-full of data existing in the healthcare domain that has been generated historically, by means of record keeping, compliance & regulatory requirements, and patient care. (1) The current trend suggests faster digitization of this large amount of data, known as ‘Big Data’, that have been stored as hard copies over time. Big Data can facilitate a wide range of medical and healthcare functions, such as clinical decision support, disease surveillance, and population health management; with primary goals of providing better quality of healthcare delivery as well as reducing the costs. (2,3)

    Big data are often identified by 5V’s in terms of Volume, Velocity, Variety, Value, and Veracity. The patient data collected often amount to peta or zeta bytes in volume. The speed and rate at which data is received from the patients explains the velocity. The miscellaneous data sets classified as the structured, semi-structured and unstructured data sets like clinical reports, EHRs, and radiological images represent variety; whereas veracity is when the true and reliable data sets with accessible and genuine data are provided. The collected data are transformed to provide meaningful understanding, thus explaining their significance in 5V’s. (4)

    While multiple discussions on healthcare data are focusing excessively on probable risks and misuses of the data, there are also enormous profits from extending healthcare data usage. A whole host of use cases are available to prove the widespread value being created by data analytics, across all stakeholders of the healthcare system, including patients, healthcare professionals and providers, payers, researchers, biopharmaceuticals and medical device companies, and regulatory authorities. Patient and healthcare associations in the past, while discussing the big data applications, have focused on the potential risks linked to exploitation of personal health records (such as predictions of individual or family health risks). Progressively, however, patients are accepting the countless benefits of data analytics, while still being vigilant about possible risks linked to data misuse. (5)

    Data analytics, in conjunction with latest technologies, can help healthcare providers expand care pathways and services “beyond their walls”. For example, new measurement devices (such as wearable, ingestible or implantable sensors) can convey data that will prompt a provider to determine patient crisis. For instance, a provider can foresee and avert any complications like diabetic foot and evade amputation by monitoring vital signs in diabetic patients. In psychiatric or neurological patients, accurate supervision of a combination of indicators can provide better certainty of a crisis. Growing number of continuous monitoring services that rely remarkably on connected objects and data analytics are already altering care paradigm for chronic patients. Examples of these include Bioserenity solutions for epilepsy, Ginger.io for chronic conditions, several congestive heart failure programs being undertaken worldwide, or Diabeo for individualized insulin dosing.(5)

    As we can see, data analytics has the potential to put together predictive models and categorize patients based on the probable/future healthcare risk they might carry, in order to modify treatment protocols to their profile. These models are crucial in deciding the success of disease management programs. With increasing digital technologies, life sciences and healthcare are on the verge of a revolution. Continuous improvement in the global and quick analysis methods, initiated with genome sequencing, along with the increasing digitization of vast information, now creates substantial quantity of data. The exploitation and analysis of these big data create new prospects, while also helping address technological, scientific and medical challenges.

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    References

    1. Raghupathi W. Data Mining in Health Care. In: Kudyba S, editor. Healthcare Informatics: Improving Efficiency and Productivity. 2010. pp. 211–223.
    2. Burghard C. Big Data and Analytics Key to Accountable Care Success. 2012.
    3. Fernandes L, O’Connor M, Weaver V. J AHIMA. 2012. Big data, bigger outcomes; pp. 38–42.
    4. Sahoo PK, et al. Analyzing Healthcare Big Data With Prediction for Future Health Condition. IEEE 2017; 4:9786-9799.
    5. Unlocking the full potential of data analytics for the benefit of all. Healthcare Data Institute. November, 2015. 
  • 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.
  • Revolutionizing Healthcare With Internet-of Things (IOT)

    Revolutionizing Healthcare With Internet-of Things (IOT)

    The “Internet of things” (IoT) is becoming an increasingly growing topic of conversation both in the workplace and outside of it. It’s a concept that not only has the potential to impact how we live but also how we work.

    The internet of things (IoT) is the internetworking of physical devices, vehicles, buildings and other items embedded with electronics, software, sensors, actuators, and network connectivity that enable these objects to collect and exchange data. In 2013 the Global Standards Initiative on Internet of Things (IoT-GSI) defined the IoT as “the infrastructure of the information society.” The IoT allows objects to be sensed and/or controlled remotely across existing network infrastructure, creating opportunities for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy and economic benefit.

    In simple words, in IoT, devices gather and share information directly with each other and the cloud, making it possible to collect, record and analyze new data streams faster and more accurately. That suggests all sorts of interesting possibilities across a range of industries: cars that sense wear and tear and self-schedule maintenance or trains that dynamically calculate and report projected arrival times to waiting passengers.

    However, nowhere does the IoT offer greater promise than in the field of healthcare, where its principles are already being applied to improve access to care, increase the quality of care and most importantly reduce the cost of care. As the technology for collecting, analyzing and transmitting data in the IoT continues to mature, we’ll see more and more exciting new IoT-driven healthcare applications and systems emerge.

    IoT-related healthcare systems today are based on the essential definition of the IoT as a network of devices that connect directly with each other to capture and share vital data through a secure service layer (SSL) that connects to a central command and control server in the cloud. The IoT plays a significant role in a broad range of healthcare applications, from managing chronic diseases at one end of the spectrum to preventing disease at the other.

    Internet-connected devices have been introduced to patients in various forms. Whether data comes from foetal monitors, electrocardiograms, temperature monitors or blood glucose levels, tracking health information is vital for some patients. Many of these measures require follow-up interaction with a healthcare professional. This creates an opening for smarter devices to deliver more valuable data, lessening the need for direct patient-physician interaction.

    India too seems to be all geared-up for IoT with the long-predicted IoT revolution in healthcare already underway. And, the currently available technologies/devices are just the tip of the proverbial iceberg, as new use cases continue to emerge to address the urgent need for affordable, accessible care. The government has released 20 smart cities list and Vizag and Kochi are already close to launching something soon. Government launched The Centre of Excellence for IoT (IoT COE) recently for “Internet of Things” partnered along with Nasscom. There are currently enormous interactions happening over various startups in India and various meet-ups such as IoT NCR, IOT BLR, etc. helping people learn IoT. There are almost 266 startups in South zone (Hyderabad, Bangalore, and Chennai), and an immense number of IoT startups in Delhi NCR region and in Mumbai and Pune zone.

    Thus, it will not be wrong to imagine well-designed apps becoming a common facet of patient-centered care, allowing people to monitor themselves on a daily basis and note any questions or concerns to forward on to their healthcare provider. Self-monitoring is also meant to create an aspect of control for the user, and could even help to provide a deeper understanding of one’s own illness – as some people tend to get overwhelmed or confused in the clinical environment of a doctor’s surgery or hospital.

    It is predicted that by the end of the decade, the data-rich personalized analysis of health by IoT will become the norm. Individuals will be provided with tailor-made strategies to combat illness and social technologies will enable us to manage our own health. From the data generated, we will learn how to improve our wellbeing and be motivated to take control. And as the consumer takes more control in this digital-first world, the business model of the health industry will need to revolutionize, to take into account the fact that any company can now become a healthcare provider – as long as their technology is meaningful to the customer. In addition, traditional businesses will need to collaborate with smaller companies, many of which may never have been involved in the health industry before. Alliances that are able to combine the most advanced medical technology with clinical informatics and secure cloud-based platforms will triumph.

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