Health care analytics

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Health care analytics is the health care analysis activities that can be undertaken as a result of data collected from four areas within healthcare; claims and cost data, pharmaceutical and research and development (R&D) data, clinical data (collected from electronic medical records (EHRs)), and patient behavior and sentiment data (patient behaviors and preferences, (retail purchases e.g. data captured in running stores). [1] Health care analytics is a growing industry in the United States, expected to grow to more than $31 billion by 2022. [2] The industry focuses on the areas of clinical analysis, financial analysis, supply chain analysis, as well as marketing, fraud and HR analysis.

Contents

Health care analytics allows for the examination of patterns in various healthcare data in order to determine how clinical care can be improved while limiting excessive spending. this can help improve the overall patient care offered in healthcare facilities.

Healthcare analytics Education in UAE

The Dubai Pharmacy College (DPCG) is a pioneer in healthcare data analytics education in the GCC region. DPC offers a Post-graduate certificate course in healthcare business data analytics for healthcare professionals to motivate the intuition to explore the concept of healthcare data analytics and apply innovations in healthcare computing technologies. The aim of the certification program is to provide a platform for interprofessional researchers to utilize the fundamental technology including software applications for intelligent data acquisition, processing, and analysis of healthcare data. [3]

Federal government role in health IT

Multiple federal entities are heavily involved in health IT. Within the executive branch, the administration itself, Centers for Medicare and Medicaid Services (CMS), and Office of the National Coordinator for Health Information Technology (ONC) each have strategic plans and are involved in determining regulation. [4] Within the legislative branch, multiple committees within the House of Representatives and Senate hold hearings and have opinions on using data and technology to reduce costs and improve outcomes in healthcare.

The ONC issued the Federal Health IT Strategic Plan 2015-2020. [5] The plan outlines the steps federal agencies will take to achieve widespread use of health information technology (health IT) and electronic health information to enhance the health IT infrastructure, to advance person-centered and self-managed health, to transform health care delivery and community health, and to foster research, scientific knowledge and innovation. [6] The plan is intended “to provide clarity in federal policies, programs, and actions and includes strategies to align program requirements, harmonize and simplify regulations, and aims to help health IT users to advance the learning health system to achieve better health.” [5]

The Strategic Plan includes several key initiatives employing multiple strategies to meet its goals. These include: (1) finalizing and implementing an interoperability roadmap; (2) protecting the privacy and security of health information; (3) identifying, prioritizing and advancing technical standards; (4) increasing user and market confidence in the safety and safe use of health IT; (5) advancing a national communication infrastructure; and (6) collaborating among all stakeholders. [5]

Challenges to address

Creating an interoperability roadmap

Dr.Aryan Chavan challenges to be addressed: (1) variation in how standards are tested and implemented; (2) variation in how health IT stakeholders interpret and implement policies and legal requirements; and (3) reluctance of health IT stakeholders to share and collaborate in ways that might foster consumer engagement. [6]

The ONC is working to develop a policy advisory for health information exchange by 2017 that will define and outline basic expectations for trading partners around health information exchange, interoperability and the exchange of information. [6] Current federal and state law only prohibits certain kinds of information blocking in limited and narrow circumstances, for example, under the Health Insurance Portability and Accountability Act (HIPAA) or the Anti-Kickback statute. [6]

Protecting privacy and security

In addition to HIPAA, many states have their own privacy laws protecting an individual’s health information. State laws that are contrary to HIPAA are generally preempted by the federal requirements unless a specific exception applies. For example, if the state law relates to identifiable health information and provides greater privacy protections, then it is not preempted by HIPAA. Since privacy laws may vary from state-to-state, it may create confusion among health IT stakeholders and make it difficult to ensure privacy compliance. [6]

Establishing common technical standards

Use of common technical standards is necessary to move electronic health information seamlessly and securely. While some clinical record content, such as laboratory results and clinical measurements are easily standardized other content, such as provider notes may be more difficult to standardize. Methods need to be identified that allow for the standardization of provider notes and other traditionally “free form text” data.

The ONC HIT Certification Program [7] certifies that a system meets the technological capability, functionality and security requirements adopted by HHS. ONC will assess the program on an ongoing basis “to ensure it can address and reinforce health IT applications and requirements that support federal value-based and alternative payment models.” [5]

Increasing confidence in safety and safe use of health IT

Health care consumers, providers and organizations need to feel confident that the health IT products, systems or services they are using are not only secure, safe and useful but that they can switch between products, systems or services without loss of valuable information or undue financial burden. Implementation of the Federal Health IT Strategic Plan 2015-2020, along with the 2013 HHS Health IT Patient Safety Action and Surveillance Plan and 2012 Food and Drug Administration Safety and Innovation Act will attempt to address these concerns. [5]

Developing national communications structure

A national communications infrastructure is necessary to enable the sharing of electronic health information between stakeholders, including providers, individuals and national emergency first responders. It is also necessary for delivering telehealth services or using mobile health applications. “Expanded, secure, and affordable high-speed wireless and broadband services, choice, and spectrum availability will support electronic health information sharing and use, support the communication required for care delivery, and support the continuity of health care and public health services during disasters and public health emergencies.” [5]

Stakeholder collaboration

The federal government in its role as contributor, beneficiary and collaborator “aims to encourage private-sector innovators and entrepreneurs, as well as researchers, to use government and government-funded data to create useful applications, products, services, and features that help improve health and health care.” HHS receives funds from the Patient-Centered Outcomes Research Trust Fund to build data capacity for patient-centered outcomes research. It is estimated HHS will receive over $140 million for the period between 2011 and 2019. These funds will be used “to enable a comprehensive, interoperable, and sustainable data network infrastructure to collect, link, and analyze data from multiple sources to facilitate patient-centered outcomes research.” [5]

Legislation

Meaningful Use, the Patient Protection and Affordable Care Act (ACA) and the declining cost of data storage [8] results in health data being stored, shared, and used by multiple providers, insurance companies, and research institutions. Concerns exist about how organizations gather, store, share, and use personal information, including privacy and confidentiality concerns, as well as the concerns over the quality and accuracy of data collected. Expansion of existing regulation can ensure patient privacy and guard patient safety to balance access to data and the ethical impact of exposing that data.

Balancing Interests – innovation, privacy, and patient safety

Complete freedom to access to data may not provide the best protection for patient rights. Expansive limits on the collection of data may unnecessarily limit its potential usefulness. In addition to data collection, there are concerns regarding risk of statistical errors, [9] erroneous conclusions or predictions,< [10] and misuse of results. [11] Appropriate policies could support gains in process improvements, cost reductions, personalized medicine, and population health. Additionally, providing incentives to encourage appropriate use may address some concerns but could also inadvertently incentivize the misuse of data. [12] Lastly, creating standards for IT infrastructure may encourage data sharing and use, but those standards would need to be reevaluated on a regular, ongoing basis as the fast pace of technological innovation causes standards and best practices to become quickly outdated.

Potential areas to address through legislation

Limiting data collection

The needs of healthcare providers, government agencies, health plans, and researchers for quality data must be met to ensure adequate medical care and to make improvements to the healthcare system, while still ensuring the patients right to privacy. Data collection should be limited to necessity for medical care and by patient preference beyond that care. Such limits would protect patient privacy while minimizing infrastructure costs to house data. When possible, patients should be informed about what data is collected prior to engaging in medical services. [13]

Limiting data use

Expanding availability of big data increases the risk of statistical errors, [1] erroneous conclusions and predictions, [10] and misuse of results. [14] Evidence supports use of data for process improvements, [15] [16] [17] cost reductions, [18] personalized medicine, [19] and public health. [20] Innovative uses for individual health [19] [21] can harm underserved populations. [22] Limiting use for denial and exclusion prevents use to determine eligibility for benefits or care and is harmonized with other U.S. anti-discrimination laws, such as Fair Credit Reporting Act, and is harmonized with anti-discrimination laws like the Civil Rights Act and the Genetic Information Nondiscrimination Act.

Providing incentives to encourage appropriate use

Increasing vertical integration in both public and private sector providers [23] has created massive databases of electronic health records. [24] The ACA has provided Medicare and Medicaid incentives to providers to adopt EHR's. [13] Large healthcare institutions also have internal motivation to apply healthcare analytics, largely for reducing costs by providing preventative care. [25] Policy could increase data use by incentivizing insurers and providers to increase population tracking, which improves outcomes. [12]

To enforce compliance with regulations, the government can use incentives similar to those under the ACA for Medicare and Medicaid to use electronic health records. [13]

Creating standards for the IT infrastructure

Inappropriate IT infrastructure likely limits healthcare analytics findings and their impact on clinical practice. [11] Establishing standards ensures IT infrastructure capable of housing big data balanced with addressing accessibility, ownership, and privacy. [25] New possibilities could be explored such as private clouds and “a virtual sandbox” consisting of filtered data authorized to the researchers accessing the sandbox. [11] [26] Standards promote easier coordination in information collaboration between different medical and research organizations [11] resulting in significantly improving patient care by improving communication between providers and reducing duplicity and costs.

Minimum standards are necessary to balance privacy and accessibility. [11] Standardization helps improve patient care by facilitating research collaboration and easier communication between medical providers. [11] The research can yield preventive care concepts that can reduce patient caseload and avoid long-term medical costs.

Related Research Articles

<span class="mw-page-title-main">Health informatics</span> Computational approaches to health care

Health informatics is the study and implementation of computer structures and algorithms to improve communication, understanding, and management of medical information. It can be viewed as branch of engineering and applied science.

Medical privacy, or health privacy, is the practice of maintaining the security and confidentiality of patient records. It involves both the conversational discretion of health care providers and the security of medical records. The terms can also refer to the physical privacy of patients from other patients and providers while in a medical facility, and to modesty in medical settings. Modern concerns include the degree of disclosure to insurance companies, employers, and other third parties. The advent of electronic medical records (EMR) and patient care management systems (PCMS) have raised new concerns about privacy, balanced with efforts to reduce duplication of services and medical errors.

<span class="mw-page-title-main">Health Insurance Portability and Accountability Act</span> United States federal law concerning health information

The Health Insurance Portability and Accountability Act of 1996 is a United States Act of Congress enacted by the 104th United States Congress and signed into law by President Bill Clinton on August 21, 1996. It aimed to alter the transfer of healthcare information, stipulated the guidelines by which personally identifiable information maintained by the healthcare and healthcare insurance industries should be protected from fraud and theft, and addressed some limitations on healthcare insurance coverage. It generally prohibits healthcare providers and businesses called covered entities from disclosing protected information to anyone other than a patient and the patient's authorized representatives without their consent. The bill does not restrict patients from receiving information about themselves. Furthermore, it does not prohibit patients from voluntarily sharing their health information however they choose, nor does it require confidentiality where a patient discloses medical information to family members, friends or other individuals not employees of a covered entity.

<span class="mw-page-title-main">Medical record</span> Medical term

The terms medical record, health record and medical chart are used somewhat interchangeably to describe the systematic documentation of a single patient's medical history and care across time within one particular health care provider's jurisdiction. A medical record includes a variety of types of "notes" entered over time by healthcare professionals, recording observations and administration of drugs and therapies, orders for the administration of drugs and therapies, test results, X-rays, reports, etc. The maintenance of complete and accurate medical records is a requirement of health care providers and is generally enforced as a licensing or certification prerequisite.

<span class="mw-page-title-main">Electronic health record</span> Digital collection of patient and population electronically stored health information

An electronic health record (EHR) is the systematized collection of patient and population electronically stored health information in a digital format. These records can be shared across different health care settings. Records are shared through network-connected, enterprise-wide information systems or other information networks and exchanges. EHRs may include a range of data, including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics like age and weight, and billing information.

Health technology is defined by the World Health Organization as the "application of organized knowledge and skills in the form of devices, medicines, vaccines, procedures, and systems developed to solve a health problem and improve quality of lives". This includes pharmaceuticals, devices, procedures, and organizational systems used in the healthcare industry, as well as computer-supported information systems. In the United States, these technologies involve standardized physical objects, as well as traditional and designed social means and methods to treat or care for patients.

Health information exchange (HIE) is the mobilization of health care information electronically across organizations within a region, community or hospital system. Participants in data exchange are called in the aggregate Health Information Networks (HIN). In practice, the term HIE may also refer to the health information organization (HIO) that facilitates the exchange.

A Regional Health Information Organization, also called a Health Information Exchange Organization, is a multistakeholder organization created to facilitate a health information exchange (HIE) – the transfer of healthcare information electronically across organizations – among stakeholders of that region's healthcare system. The ultimate objective is to improve the safety, quality, and efficiency of healthcare as well as access to healthcare through the efficient application of health information technology. RHIOs are also intended to support secondary use of clinical data for research as well as institution/provider quality assessment and improvement. RHIO stakeholders include smaller clinics, hospitals, medical societies, major employers and payers.

IQVIA, formerly Quintiles and IMS Health, Inc., is an American Fortune 500 and S&P 500 multinational company serving the combined industries of health information technology and clinical research. IQVIA is a provider of biopharmaceutical development, professional consulting and commercial outsourcing services, focused primarily on Phase I-IV clinical trials and associated laboratory and analytical services, including investment strategy and management consulting services. It has a network of more than 88,000 employees in more than 100 countries and a market capitalization of US$49 billion as of August 2021. As of 2023, IQVIA was reported to be one of the world's largest contract research organizations (CRO).

Protected health information (PHI) under U.S. law is any information about health status, provision of health care, or payment for health care that is created or collected by a Covered Entity, and can be linked to a specific individual. This is interpreted rather broadly and includes any part of a patient's medical record or payment history.

Health information technology (HIT) is health technology, particularly information technology, applied to health and health care. It supports health information management across computerized systems and the secure exchange of health information between consumers, providers, payers, and quality monitors. Based on a 2008 report on a small series of studies conducted at four sites that provide ambulatory care – three U.S. medical centers and one in the Netherlands, the use of electronic health records (EHRs) was viewed as the most promising tool for improving the overall quality, safety and efficiency of the health delivery system.

The Office of the National Coordinator for Health Information Technology (ONC) is a staff division of the Office of the Secretary, within the U.S. Department of Health and Human Services. ONC leads national health IT efforts, charged as the principal federal entity to coordinate nationwide efforts to implement and use the most advanced health information technology and the electronic exchange of health information.

The Health Information Technology for Economic and Clinical Health Act, abbreviated the HITECH Act, was enacted under Title XIII of the American Recovery and Reinvestment Act of 2009. Under the HITECH Act, the United States Department of Health and Human Services resolved to spend $25.9 billion to promote and expand the adoption of health information technology. The Washington Post reported the inclusion of "as much as $36.5 billion in spending to create a nationwide network of electronic health records." At the time it was enacted, it was considered "the most important piece of health care legislation to be passed in the last 20 to 30 years" and the "foundation for health care reform."

The Fast Healthcare Interoperability Resources standard is a set of rules and specifications for exchanging electronic health care data. It is designed to be flexible and adaptable, so that it can be used in a wide range of settings and with different health care information systems. The goal of FHIR is to enable the seamless and secure exchange of health care information, so that patients can receive the best possible care. The standard describes data formats and elements and an application programming interface (API) for exchanging electronic health records (EHR). The standard was created by the Health Level Seven International (HL7) health-care standards organization.

Digital health is a discipline that includes digital care programs, technologies with health, healthcare, living, and society to enhance the efficiency of healthcare delivery and to make medicine more personalized and precise. It uses information and communication technologies to facilitate understanding of health problems and challenges faced by people receiving medical treatment and social prescribing in more personalised and precise ways. The definitions of digital health and its remits overlap in many ways with those of health and medical informatics.

Medical data, including patients' identity information, health status, disease diagnosis and treatment, and biogenetic information, not only involve patients' privacy but also have a special sensitivity and important value, which may bring physical and mental distress and property loss to patients and even negatively affect social stability and national security once leaked. However, the development and application of medical AI must rely on a large amount of medical data for algorithm training, and the larger and more diverse the amount of data, the more accurate the results of its analysis and prediction will be. However, the application of big data technologies such as data collection, analysis and processing, cloud storage, and information sharing has increased the risk of data leakage. In the United States, the rate of such breaches has increased over time, with 176 million records breached by the end of 2017. There have been 245 data breaches of 10,000 or more records, 68 breaches of the healthcare data of 100,000 or more individuals, 25 breaches that affected more than half a million individuals, and 10 breaches of the personal and protected health information of more than 1 million individuals.

<span class="mw-page-title-main">Artificial intelligence in healthcare</span> Overview of the use of artificial intelligence in healthcare

Artificial intelligence in healthcare is a term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to copy human cognition in the analysis, presentation, and understanding of complex medical and health care data, or to exceed human capabilities by providing new ways to diagnose, treat, or prevent disease. Specifically, AI is the ability of computer algorithms to arrive at approximate conclusions based solely on input data.

Health data is any data "related to health conditions, reproductive outcomes, causes of death, and quality of life" for an individual or population. Health data includes clinical metrics along with environmental, socioeconomic, and behavioral information pertinent to health and wellness. A plurality of health data are collected and used when individuals interact with health care systems. This data, collected by health care providers, typically includes a record of services received, conditions of those services, and clinical outcomes or information concerning those services. Historically, most health data has been sourced from this framework. The advent of eHealth and advances in health information technology, however, have expanded the collection and use of health data—but have also engendered new security, privacy, and ethical concerns. The increasing collection and use of health data by patients is a major component of digital health.

Mental health informatics is a branch of health or clinical informatics focused on the use of information technology (IT) and information to improve mental health. Like health informatics, mental health informatics is a multidisciplinary field that promotes care delivery, research and education as well as the technology and methodologies required to implement it.

Federal and state governments, insurance companies and other large medical institutions are heavily promoting the adoption of electronic health records. The US Congress included a formula of both incentives and penalties for EMR/EHR adoption versus continued use of paper records as part of the Health Information Technology for Economic and Clinical Health (HITECH) Act, enacted as part of the, American Recovery and Reinvestment Act of 2009.

References

  1. 1 2 Fan, Jianqing; Han, Fang; Liu, Han (2014-06-01). "Challenges of Big Data analysis". National Science Review. 1 (2): 293–314. arXiv: 1308.1479 . doi:10.1093/nsr/nwt032. ISSN   2095-5138. PMC   4236847 . PMID   25419469.
  2. ↑ "Healthcare Analytics Market to Hit $31 Billion by 2022". Healthcare Finance News. 2018-08-13. Retrieved 2019-02-05.
  3. ↑ "Dubai Pharmacy College for Girls - PG Certificate in Healthcare Business Data Analytics".
  4. ↑ "Andy Slavitt puts meaningful use on ice; Read his J.P. Morgan speech transcript". Healthcare IT News. 2016-01-13. Retrieved 2017-01-20.
  5. 1 2 3 4 5 6 7 "Federal Health IT Strategic Plan 2015-2020" (PDF). Retrieved 21 October 2016.
  6. 1 2 3 4 5 "2015 Update to Congress on the Adoption of Health Information Technology". dashboard.healthit.gov. Retrieved 2016-10-22.
  7. ↑ "ONC Health IT Certification Program | Policy Researchers & Implementers | HealthIT.gov". www.healthit.gov. Retrieved 2016-10-22.
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  10. 1 2 Austin B. Frakt; Steven D. Pizer (2016-02-16). "The Promise and Perils of Big Data in Healthcare". American Journal of Managed Care. 22 (February 2016 2).
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  13. 1 2 3 Inc., Advanced Solutions International. "Affordable Care Act Implementation and Information". www.amga.org. Retrieved 2016-10-22.{{cite web}}: |last= has generic name (help)
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  17. ↑ Janke, Alexander T.; Overbeek, Daniel L.; Kocher, Keith E.; Levy, Phillip D. (2016). "Exploring the Potential of Predictive Analytics and Big Data in Emergency Care". Annals of Emergency Medicine. 67 (2): 227–236. doi:10.1016/j.annemergmed.2015.06.024. PMID   26215667.
  18. ↑ Khalifa Mohamed; Zabani Ibrahim; Khalid Parwaiz (2016). "IOS Press Ebooks - Exploring Lab Tests Over Utilization Patterns Using Health Analytics Methods". Studies in Health Technology and Informatics. 226 (Unifying the Applications and Foundations of Biomedical and Health Informatics). doi:10.3233/978-1-61499-664-4-190.
  19. 1 2 Maglaveras, Nicos; Kilintzis, Vassilis; Koutkias, Vassilis; Chouvarda, Ioanna (2016-01-01). "Integrated Care and Connected Health Approaches Leveraging Personalised Health through Big Data Analytics". Studies in Health Technology and Informatics. 224: 117–122. ISSN   0926-9630. PMID   27225565.
  20. ↑ Razavian, Narges; Blecker, Saul; Schmidt, Ann Marie; Smith-McLallen, Aaron; Nigam, Somesh; Sontag, David (2015-12-01). "Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors". Big Data. 3 (4): 277–287. doi: 10.1089/big.2015.0020 . ISSN   2167-6461. PMID   27441408.
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  26. ↑ Marshall, Deborah A.; Burgos-Liz, Lina; Pasupathy, Kalyan S.; Padula, William V.; IJzerman, Maarten J.; Wong, Peter K.; Higashi, Mitchell K.; Engbers, Jordan; Wiebe, Samuel (2016-02-01). "Transforming Healthcare Delivery: Integrating Dynamic Simulation Modelling and Big Data in Health Economics and Outcomes Research". PharmacoEconomics. 34 (2): 115–126. doi:10.1007/s40273-015-0330-7. ISSN   1179-2027. PMID   26497003. S2CID   2759268.

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