...
...

Healthcare Data Applications | Barriers to Adoption

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • Healthcare
  • Competitive Differentiation
  • Emerging Technologies


Publication | Update: Sep 2020

Biggest roadblocks for widespread use of big data include lack of data standardization, siloing, lack of accessibility, the need for a clinical data warehouse, and privacy/security concerns.

Healthcare IT has historically mirrored the enterprise IT sector, albeit on a multi-year lag. Because of this, we look to the evolution of big data within the enterprise sector to frame our outlook for big-data adoption in healthcare.

According to Stephanie Demko, Citi’s U.S. Healthcare Technology Analyst, the rise of enterprise big data has gained momentum in recent years given the elasticity of the cloud and the dramatic reduction of computing and storage costs paired with significant gains in processing power. However, this opportunity in itself is still in the very early innings, with a significant focus on data cleanup and usability. This suggests a long road ahead for big data to truly penetrate the healthcare sector given similar data siloing/usability issues within the healthcare sector as well as a slower shift to the cloud.

Additionally, healthcare IT faces several roadblocks that are unique to the space, given the competitive dynamics between the holders (and often times, vendors) of healthcare data. While in the near term, one of the biggest opportunities in enterprise big data is the consolidation/preparation of data for consumption by artificial intelligence and machine learning, this may get pushed off by healthcare IT vendors that are sensitive around releasing their data, which they view as key to their value.

Data Presents the Biggest Challenge

As Eric Schmidt, former Executive Chairman of Google, noted in his 2018 HIMSS (Healthcare Information and Management Systems Society Conference) conference speech this past March, several steps are required in order for providers and healthcare IT vendors to make use of their newly electronified data. To pave a path to improved healthcare cost/outcomes, vendors need to (1) move healthcare data stores to the cloud, (2) de-silo the data to create robust data sets, and (3) ultimately apply machine-learning models to improve predictive analytics and diagnoses. 

Echoing Schmidt’s keynote, we believe the biggest roadblocks facing widespread use of big data for artificial intelligent/machine learning applications are (1) lack of data standardization, (2) the current siloing of medical data, (3) lack of accessibility, (4) need for a ‘clinical data warehouse,’ and (5) privacy and security concerns.

Data Issue 1: Cleanup 

The lack of standardization across healthcare data presents the largest initial challenge to adopting big-data solutions. The vast amount of data generated and collected by a multitude of agents in healthcare today come in many different forms, both structured and unstructured — from insurance claims to physician notes within the medical record, images from patient scans, conversations about health in social media, and information from wearables and other monitoring devices.

For the most part, we believe healthcare data are compiled in SQL-based relational databases because these are well suited for discretely codified billing and clinical transactions data. However, this format presents limitations in both the volume and the velocity of data processing, while rapid growth in unstructured health data could create the need for hybrid (relational and NoSQL) databases.

Further, the data-collecting community is equally heterogeneous, making the extraction and integration of the data a real challenge. Providers, payers, employers, disease-management companies, wellness facilities and programs, personalized-genetic-testing companies, social media, and patients themselves all collect data. Even standardizing for end market and form, the data standards presented in Meaningful Use were more open to interpretation than a standardized protocol, creating a lack of standardization even across EHR systems. Integration of data will require collaboration and leadership from both the public and private sectors. 

Data Issue 2: Siloing 

Medical data are spread across many sources governed by different states, hospitals, and administrative departments and information silos exist across both private and public sectors. Even within organizations themselves, multiple sources of data — such as clinical, financial, and operational data — are kept separated.

The issue is further compounded by each data system’s unique key identifiers, validation rules, and format. With medical data siloed in a multitude of verticals, the result is difficulty in data aggregation when attempting to create a complete data set to analyze a patient or a population. The integration of these data sources would require developing a new infrastructure where all data providers collaborate with each other.

Data Issue 3: Accessibility

Stemming from the siloing of medical data, the ability to create full data sets for one patient or a population to work with is limited by the lack of accessibility across different source of data. According to an athenahealth survey, while 79% of doctors believe that having all available patient data in one place is critical to their jobs, only 14% could access EMR information across different departments, patient care centers, etc., even within the same hospital. While recent regulation looks to improve upon data sharing, our channel checks have shown vendor hesitation and proactive friction in data sharing.

In order to increase interoperability among hospitals, physicians, and other relevant parties, the industry is slowly shifting to a new technology known as FHIR (Faster

Healthcare Interoperability Resources). FHIR creates standards for different data elements so that developers can build application programming interfaces (APIs) that can be used to access datasets from different systems.

Data Issue 4: Warehousing

Finding a place to warehouse the huge amount of data in healthcare is a challenge. Assuming the data get standardized and become de-siloed and accessible for use, the challenge standing in the way is the need for a clinical data warehouse to host the vast amount of data (projected data size 2,310 exabytes by 2020). Only once this data are curated into usable data sets can they then be used for sophisticated analysis with a rich API. The data warehouse would require two tiers of data, with the first tier being primary data stores sourced from EHRs, supplemented by a second tier comprising unstructured data collected from everywhere else.

Data Issue 5: Privacy/Security

Safeguarding data is key and heightens the cost of data vs. other industries. Given the enormity of total population medical data both in value and volume, large data stores are at a high risk of tampering and theft. This is particularly vital as a leak of identified health data is irreversible, unlike the leaking of a more dynamic data asset such as a consumer’s credit card information.

This security risk necessitates significant investments to safeguard the data, creating a heightened level of cost compared with other industries. Privacy concerns have also led to slower momentum in data storage evolution, with locally hosted systems still prevalent within healthcare due to perceived cloud risk.

Consumer Health Informatics (CHI) applications

When it comes to Consumer Health Informatics (CHI) applications barriers can be divided into two groups: system-level and individual level barriers. System-level barriers can further be divided into technical and healthcare system barriers. Technical barriers refer to usability, work flow issues and data security concern. Healthcare system barriers include reimbursement system and incompatibility between legacy system in healthcare institutions and patient applications. The individual level barriers are directed towards the consumer or the clinician. Consumer issues cover problems like lack of access to application, privacy concern, knowledge and limited literacy. Clinician issues affect consumer choice and with the negative attitudes of clinicians may be a barrier to consumers’ use.

The Table below summarizes the barriers faced by consumers in leveraging CHI application health self-management.

...

Source: Health Science Journal:”Barriers to Adoption of Consumer Health Informatics Applications for Health Self Management”, Kumar Laxman, Sharanie Banu Krishnan and Jaspaljeet Singh Dhillon

 

 

Framed Content - Publisher | Sponsor:
...
APU Insights
...Industry: Healthcare, Biotechnology and Pharmaceuticals
SKU code : EC142CD8-F208-E373-A1FA-2B7FAC54A6F4
Delivery Format:
HTML ...
Immediate Delivery
...Access Rights | Content Availability:
  • The Big Picture - Intelligence Center
  • The Big Picture - Platform

...

Objectives and Study Scope

This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.

The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges. The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period. The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the  market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players.  This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.

Research Process & Methodology

...

We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.

We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.

Research Portfolio Sources:

  • BBC Monitoring

  • BMI Research: Company Reports, Industry Reports, Special Reports, Industry Forecast Scenario

  • CIMB: Company Reports, Daily Market News, Economic Reports, Industry Reports, Strategy Reports, and Yearbooks

  • Dun & Bradstreet: Country Reports, Country Riskline Reports, Economic Indicators 5yr Forecast, and Industry Reports

  • EMIS: EMIS Insight and EMIS Dealwatch

  • Enerdata: Energy Data Set, Energy Market Report, Energy Prices, LNG Trade Data and World Refineries Data

  • Euromoney: China Law and Practice, Emerging Markets, International Tax Review, Latin Finance, Managing Intellectual Property, Petroleum Economist, Project Finance, and Euromoney Magazine

  • Euromonitor International: Industry Capsules, Local Company Profiles, Sector Capsules

  • Fitch Ratings: Criteria Reports, Outlook Report, Presale Report, Press Releases, Special Reports, Transition Default Study Report

  • FocusEconomics: Consensus Forecast Country Reports

  • Ken Research: Industry Reports, Regional Industry Reports and Global Industry Reports

  • MarketLine: Company Profiles and Industry Profiles

  • OECD: Economic Outlook, Economic Surveys, Energy Prices and Taxes, Main Economic Indicators, Main Science and Technology Indicators, National Accounts, Quarterly International Trade Statistics

  • Oxford Economics: Global Industry Forecasts, Country Economic Forecasts, Industry Forecast Data, and Monthly Industry Briefings

  • Progressive Digital Media: Industry Snapshots, News, Company Profiles, Energy Business Review

  • Project Syndicate: News Commentary

  • Technavio: Global Market Assessment Reports, Regional Market Assessment Reports, and Market Assessment Country Reports

  • The Economist Intelligence Unit: Country Summaries, Industry Briefings, Industry Reports and Industry Statistics

Global Business Reviews, Research Papers, Commentary & Strategy Reports

  • World Bank

  • World Trade Organization

  • The Financial Times

  • The Wall Street Journal

  • The Wall Street Transcript

  • Bloomberg

  • Standard & Poor’s Industry Surveys

  • Thomson Research

  • Thomson Street Events

  • Reuter 3000 Xtra

  • OneSource Business

  • Hoover’s

  • MGI

  • LSE

  • MIT

  • ERA

  • BBVA

  • IDC

  • IdExec

  • Moody’s

  • Factiva

  • Forrester Research

  • Computer Economics

  • Voice and Data

  • SIA / SSIR

  • Kiplinger Forecasts

  • Dialog PRO

  • LexisNexis

  • ISI Emerging Markets

  • McKinsey

  • Deloitte

  • Oliver Wyman

  • Faulkner Information Services

  • Accenture

  • Ipsos

  • Mintel

  • Statista

  • Bureau van Dijk’s Amadeus

  • EY

  • PwC

  • Berg Insight

  • ABI research

  • Pyramid Research

  • Gartner Group

  • Juniper Research

  • MarketsandMarkets

  • GSA

  • Frost and Sullivan Analysis

  • McKinsey Global Institute

  • European Mobile and Mobility Alliance

  • Open Europe

M&A and Risk Management | Regulation

  • Thomson Mergers & Acquisitions

  • MergerStat

  • Profound

  • DDAR

  • ISS Corporate Governance

  • BoardEx

  • Board Analyst

  • Securities Mosaic

  • Varonis

  • International Tax and Business Guides

  • CoreCompensation

  • CCH Research Network

...
Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.

CLICK BELOW TO LEARN MORE
...

Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.

Revenues

Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates

...

The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.

CLICK BELOW TO LEARN MORE
...

The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticities
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.

CLICK BELOW TO LEARN MORE