Big data-Driven Healthcare Project Financing Prof. Roberto Moro Visconti Department of Business Administration, Largo A. Gemelli 1 – 20123 Milano, Italy Università Cattolica del Sacro Cuore
[email protected] www.morovisconti.com
ABSTRACT Healthcare investments are faced by the need to match growing expenses, due to ageing population trends, with public budget constraints. Infrastructural PF packages are by now popular and effective, although they are rigid and long-termed. Big data-driven value chains add unprecedented information to project financing (PF) and public private partnerships (PPPs), especially in healthcare investments. Big data and Internet of Health sensors, currently adopted in telemedicine, can be applied even to PF strategies, providing useful information to data-driven business plans. Public and Private Partners interact through networking big data and interoperable databases, boosting value co-creation, improving Value for Money, and reducing risk. Policy makers can conveniently use networked big data to enrich their feasibility plans, whereas private managers may extract precious information from public healthcare databases. Big data can also help shortening supply chain passages, boosting economic marginality and easing the sustainable planning of smart healthcare investments.
Keywords: healthcare informatics; networks; Internet of Health; business planning; interoperability; Public Private Partnership; value chain; data mining; predictive analytics; healthcare management.
1. Introduction While big data are increasingly associated with telemedicine and e-health through Internet of Things (IoT) sensors and networking digital platforms, their relationship with healthcare infrastructural investments is still pioneering. Connections with big data are important since healthcare PF investments, together with their business plans, are strongly data-driven. Input information increasingly depends on big data feeding. Trendy evidence shows that the rapid growth of health data, the change of the medical paradigm, and the social needs for reducing medical costs promote the use of big data in healthcare, with underexploited strategic implications (Wang et al., 2016). The convergence of IT and healthcare has become a direct driver of the growth of bio and medical data analysis, such as health screening data, genome analysis, and clinical data, through electronic health records, health information technology, and e-health (Kim and Park, 2016). The healthcare sector is one of the most promising areas for big data (Raghupati and Raghupati, 2014). 1
Digitalized big data provide many benefits to healthcare organizations through disease prediction and surveillance, population health management, and patient care improvement. Big data stimulate innovation, cost and risk reduction, and productivity gains. Current research on big data in healthcare is highly interdisciplinary involving methodologies from engineering, computing, behaviour science, information science, social science, management science, as well as many different areas of medicine and public health (Li et al., 2016). Economic and financial implications, such as those concerning PF investments, are still under-investigated. This paper is inspired by current scenarios related to telemedicine and input data from heterogeneous sources, through value-adding data fusion and interoperability. Since data are networked through digital platforms, they are particularly fit for interaction among private and public stakeholders. Such an interaction passes through the various milestones of the PF timesheet, from its inception up to construction and management. The paper is organized as follows: after an introduction about big data-driven healthcare issues, value chains are examined, together with their networking extensions (consistent with PPP stakeholders). The impact of big data on business planning is analysed, considering feasibility studies and revenue planning of healthcare operations. Interoperability and data fusion from heterogeneous sources are then addressed, examining their impact on public databases that fuel data gathering and delivery. This topic is innovative and can bring to new opportunities in healthcare planning, reshaping PF business models. 2. Big-data driven healthcare Big data-driven healthcare PF is a cutting-edge topic that combines complementary aspects concerning: 1. Big data; 2. Healthcare information technology; 3. Digital networks where data are exchanged; 4. PF / PPP investments. These key topics can be illustrated as follows: 1) Big data represent any gathering from multiple sources of large-volume information sets, so expensive, fast changing and complex that it gets to be hard to process utilizing customary information (Chen and Zhang, 2014; Chen et al. 2014; Miller, 2014; Morabito, 2015). Big data produce massive amounts of information in real time that can be usefully used for effective planning and monitoring. Adoption of big data is consistent with their value chain which starts from creation (data capture) and follows with storage, processing (data mining), visualization, and consumption (sharing); 2) Healthcare information is the practice of acquiring, analysing and protecting sensitive digital and traditional medical information vital to providing quality patient care. Health information technology (with electronic health records or biomedical data increasingly sourced by remote patient monitoring with home healthcare and smart devices) provides real-time massive (big) data. 3) Data are exchanged through networking digital platforms. Connected databases store and share selected information among PPP stakeholders. 4) Infrastructural healthcare investments are concerned with the construction of public hospitals, with either traditional procurement or PF schemes with PPP agreements. Healthcare PF is the financing of long-term hospital infrastructures based upon a complex financial structure where project debt and equity are used to finance the project, rather than to reward project sponsors. Healthcare is highly networked and systemic industry, with a practical impact on projects (Moro Visconti, 2014b). PPPs represent the natural stakeholder framework of PF investments, with interacting public and private actors. 2
These topics may at first seem mostly unrelated, even if they can be combined in a logical sequence, where big data fuel healthcare information that is exchanged among PPP stakeholders (public and private actors, sponsoring banks, patients, etc.) through digital networks and shared databases. 3. Big data value chains PPP healthcare investments are based on sophisticated value and supply chains where public authorities interact with private (sub)contractors and their sponsoring banks, to address the patients’ needs. Value chains are the strategic backbone of business modelling and planning, indicating which the target corporate goals are. These chains are constantly nurtured by informative inputs and big data can improve this inflow in terms of quality, quantity, and readiness. Digital value chains are based on sequential steps where big data are captured, stored, processed and shared. Monetization is the last step, which transforms added value into cash. Potentialities of big data for value and supply chain planning are not uniform along their sequential steps and asymmetrically refer to public or private stakeholders. Big data are related to core medical services and healthcare patterns (morbidity trends; remote patient monitoring, etc.) that are traditionally mastered by the public player. Private actors are however interested in big data joint exploitation, within a cohesive PPP framework, considering the synergies between public healthcare needs and private supply functions. Big data value chains are represented by several steps. Each passage, codified by consequential software algorithms, is part of an incremental and flexible value chain up to monetization, so crucial for economic sustainability and bankability of PPP investments. This chain produces different stages of information, which can be embedded in traditional value chains that so become big data-driven. Big data value chains are based on the following sequential strategic steps, synthesized in Figure 1: 1. Creation (data capture) 2. Storage (warehousing) 3. Processing (data mining / fusion and analytics) 4. Consumption (sharing) 5. Monetization Figure 1 – Big data value chain.
Each passage adds up a value that should be shared among its contributors (private providers, intermediating public platforms, users, etc.), which participate to value co-creation. Networks (Caldarelli and Catanzaro, 2012) and value chains continuously change and adapt, together with their fuelling big data sources. Digital platforms that connect big data are a key component of valueadding supply chains. Value chains are fuelled by big data’s V-dimensions. Table 1 (see Manogaran et al., 2017) shows the main impacts of big data characteristics on healthcare issues.
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Table 1 – Impact of big data characteristics on healthcare issues Big data dimensions
Impact on healthcare
Volume
Big volumes of healthcare data include personal medical records, radiology images, clinical trial data FDA submissions, human genetics and population data genomic sequences, etc. Newer forms of big data, such as 3D imaging, genomics and biometric sensor readings, are also fueling this exponential growth. Data is accumulated in real-time and at a rapid pace. Most healthcare data have been traditionally static—paper files, x-ray films, and scripts. The velocity of proliferating data increases with data that represents regular monitoring (Raghupati, Raghupati, 2014). As more and more medical devices are designed to monitor patients and collect data, there is increasing demand to analyze that data and transmit it back to clinicians and others. This “internet of things” of healthcare will only lead to increasing velocity of big data in healthcare. Evidence-based medicine combines and analyzes a variety of structured, semistructured, and unstructured data, Electronic Medical Records, financial and operational data, clinical data, and genomic data to match treatments with outcomes, predict patients at risk for disease or readmission, and provide more efficient care; Key parameter in healthcare, corresponding to data reliability. Increased variety and high velocity hinder the ability to cleanse data before analyzing it and making decisions, magnifying the issue of data “trust”. Data integrity is defined as the validity, accuracy, reliability, timeliness, and consistency of the data. It remains the first question of recorded EHR data use in biomedical research (Balas et al., 2015). The way care is provided to any given patient depends on all kinds of factors— and the way the care is delivered and more importantly the way the data is captured may vary from time to time or place to place. Measures the spread rate of data across the network. While the concept is traditionally associated with epidemics, its application to big data is recent. Information visualization and visual analytics are connected to health informatics through representation technologies and help users to understand data. The synthesis produced by data visualization tools is a key element to transform the information revealed by big data processing, understood only by specialist scientists, into accessible knowledge. Communication by design and charts uses colors as a visual vocabulary. Characterizes the resistance to navigate in the dataset (related for instance to data flow rates, variety of data source) or complexity of data processing. It is a common feature of healthcare data. Summarizes and monetizes all the other dimensions / characteristics of big data. In PF applications, the value is spread among PPP stakeholders, improving the bankability of the overall project.
Velocity
Variety
Veracity
Validity
Variability
Virality Visualization
Viscosity
Value
Big data value can be extracted from shortened supply chains, expansion with innovative services targeting new markets (such as telemedicine), product and process innovation, processing speed, differentiation, and risk minimization. Data acquisition, storage, display, processing, and transfer of text, reports, voice, images, and videos between geographically separated locations represent the basis of telemedicine (Bairagi, 2016). The challenge is to link telemedicine to infrastructural healthcare investments.
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Value can, however, be destroyed by incomplete chains, with either missing steps or insufficient development. Since the potential for upgrade is theoretically unlimited, value bottlenecks are ubiquitous and need constant monitoring and fixing. Big data monetization strategies represent the last step of the value chain, which remunerates all the PPP stakeholders, boosting Value for Money and consequent bankability of the PF investment. Monetized value is the synthesis of big data V-dimensions, considering data as an asset to exploit to produce innovation and new data products and services. Use of data for the continuous improvement of internal operations (managerial intelligence), interacting with decision makers, can bring to cost and risk reductions, and customer service improvements. Misunderstood risks, tracked in real time, can be identified and minimized with big data. 4. Networked big data and PPP stakeholders Networking through intermediating digital platforms is intrinsically consistent with PPP sharing attitudes, and so is its interaction with big data and healthcare investments. Healthcare infrastructures are highly networked and timely links among core hospitals and satellite points of care are reshaping the industry. Functional coordination but also growing competition is levered by better-informed patients with increasingly sophisticated needs and choices. Big data, for instance, favour information exchange and quality comparisons for pathologies and wishes connected to aging and chronic diseases. Networks are concerned with a disordered pattern of different interactions, analysed by big data, and emphasize communications, processing massive amounts of data. Since networks are patterns of interaction, they play a fundamental role in any step of the value chains. Value chains based on traditional database become networked when they are linked to other chains, mainly through digital platforms. Networks concern big data at any stage of the value chain, and so network theory matters for value chain upgrading with connected big data: during the creation (data capture) phase, different data can be considered as nodes (vertices) that are collected and then linked among them in the processing phase. Big data consumption takes place through sharing, again linking nodes of users. Monetization is eased by optimal sharing and economic interaction among big data users; networking is for instance concerned with B2B, B2C, and C2C e-commerce applications. The option to connect value chains to form value networks is much richer in potential if it is powered by big data. Indeed, the flows of both unlinked and linked value chains are boosted by big data. Networked value chains fuelled by big data stand out as the best value maximizing option, as illustrated in Figure 2.
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Figure 2 – From traditional to big data-driven networked value chains.
Networked bigdata-driven Value Chains
[traditional] Value Chain
Bigdata-driven Value Chain
interactivity
[traditional] Networked Value Chains
input data = bigdata
Figure 2 shows that networks leverage big data value chains, improving healthcare PPP relationships with real-time feedbacks (because of big data velocity). Application to the healthcare sector of big data-driven networked value chains is still under-investigated. Virality of networks is however well documented in epidemiology (Nowzari et al., 2016), and may be considered as an example to replicate and adapt to healthcare infrastructural issues. Big data are often sourced by IoT smart devices and sensors (such as wearables for symptom tracking), networked through healthcare digital platforms, often using B2C mobile apps or B2B databases. Geolocalization through Geographic Information Systems (GIS) is embedded in sensors, and smartphones, providing useful information in real time about positioning and patterns (relevant for emergencies, epidemic tracking, etc.). Digital connections between core hospitals and satellite locations (health centres, outpatient clinics but also patient homes, ambulances, etc.) are exponentially increasing, shaping new healthcare business models. 5. Reshaping healthcare PF input data Data are the key input for business modelling and planning and are constantly needed to nurture the investment timesheet from its inception till the end of the PF concession. The main milestones are the following: 1. Public feasibility study, discriminating among different investment and funding alternatives (such as Traditional Procurement versus PPP); 2. Competitive auction among private participants to the PF tender, and selection of the best offer; 3. Construction phase of healthcare facilities; 4. Management of the facilities along the concession period; 6
5. Termination of the concession, typically envisaging a free transfer to the public authority. While these milestones represent a standard outline of the investment track, they can be profoundly affected by big data-driven inputs. Big data can improve the accuracy of phases 1, 2 and 4. Input information for feasibility studies can benefit from big data sources, whereas private bidding is based on expectations nurtured by big data. Management phase can adjust to market responses in real time, interacting with big data. While the concession of the hospital (up to the free transfer to the public procurer, within a ProjectBuild-Operate-Transfer scheme) typically lasts some 20-25 years, there are periodical market testing windows for diagnostic equipment and other services whose useful life is consistently shorter than the concession. Market testing is affected by big data since technological advances must match epidemiological trends and healthcare updated patterns. Big data sharing between public and private partners increases cohesion and convergence of interests, reducing information asymmetries and corporate governance concerns. 6. Big data-driven business planning Big data processing (data mining) is becoming a core input component of business planning. Big data-driven business models revolutionize healthcare planning with hidden information and extract value from data that are considered a worthy asset, to be internally produced and/or purchased or shared. Although big data analytics have tremendous benefits for healthcare organizations, current research has underexplored its business value (Wang and Hajli, 2017). The value of data boosts when it is connected to formerly unrelated heterogeneous sources. This shows the importance of data fusion and big data integration (interoperability) that will be examined later. PF milestones pivot around business forecasts, and confrontation between the public feasibility study and the private business plan is a main component of the competitive auction. Cognitive big data represent a powerful, albeit under-exploited, source of information for descriptive, prescriptive, and predictive analytics (Franks, 2014; Jumi et al. 2016; Tanner, 2014; Jin et al., 2015; Hartmann et al, 2016) supporting decisions especially in data-rich industries as healthcare. This information needs to be collected, processed, and made available to users through the value chain described above. Business models, intrinsically data-centric and data-driven, are primarily connected with digital information provided by big data. Information and knowledge extracted from big data is relevant for for PF business planning in many complementary ways: its velocity allows a mark-to-market update and refresh of forecasts that flexibly adapt to changing market conditions, whereas data volume make estimates more precise and less volatile, reducing the risk of uncertainty and making massive analysis possible. With the widespread use of healthcare information systems commonly known as electronic health records, there is significant scope for improving the way healthcare is delivered thanks to big data. This has made data mining and predictive analytics an important tool for healthcare decision making (Malik et al., 2016). Public authorities and private players that embed big data and aggregate information in their PF models can gain a competitive advantage in understanding healthcare trends with scale, precision, and velocity, satisfying unmet needs. Muhtaroglu et al., 2013 show that “companies which can adapt their businesses well to leverage big data have significant advantages over those that lag this capability. The need for exploring new approaches to address the challenges of big data forces companies to shape their business models accordingly”. Big data can optimize (Walker, 2015): 7
1.
2. 3.
Sales planning and revenue streams – after comparison of massive data, private players can optimize their prices, predicting patient needs and consumer behaviour for ancillary services by segregating the relevant information of the targeted audience; “hot” and “cold” revenue planning represents a core component of PF investments, as it will be shown later; Operations – firms can improve their operational efficiency, optimize their labour force, cut operational costs, and avoid out-of-scope production during the PF management phase; Supply chain (smart logistics) – big data can foster inventory / logistic optimization and supplier coordination, shortening the supply chain and making it more resilient to external shocks. During the PF construction and management phase, relationships between the bundling private Special Purpose Vehicle and its sub-contractors can be improved.
Higher economic and monetary margins are a natural result of these optimization strategies. With big data, planning becomes more accurate and timely, reducing the risk of unexpected events (such as sudden changes in morbidity patterns) and increasing the possibility to grasp unexploited opportunities (due to technological upgrading or other disrupting events). Feedbacks from patients and visitors or customers are increasingly available in real time, producing big data that are processed and used to reengineer the business plan, adapting it to the current situation. These quality reviews (somewhat similar to TripdAdvisor) are transmitted through blogs or other social networks, creating a community of users. Big data predictive modelling can be incorporated in business planning, creating value through appropriate implementation strategies that leverage information value and clinical decision support systems. Medical IoT is a critical piece of the digital transformation of healthcare, as it allows new business models to emerge and enables changes in work processes, productivity improvements, cost containment, and enhanced customer experiences (Dimitrov, 2016). 7. Reengineering feasibility studies with big data Big data-driven business planning strategies reshape feasibility studies, adding unprecedented information. The public choice between traditional procurement and PPP is crucial for long term PF investments such as healthcare infrastructure. Feasibility studies drive this uneasy decision, based on Public Sector Comparator and Value for Money scrutiny (Moro Visconti, 2014a). Public budget constraints face ageing population needs, with consequent need of forward-looking choices. Big data improve the informative framework of feasibility studies that can be based on Political – Economic – Social – Technological – Legal – Environmental (PESTLE) considerations (Moro Visconti, 2016), consistent with Economic, Social and Environmental sustainability. While Political and Legal factors are weakly related to big data, Socio-Economic, Technologic, and Environmental issues are deeply data-driven. Several technologies, to be considered in feasibility studies, can reduce overall costs for the prevention or management of chronic illnesses. These include devices that constantly monitor health indicators, autoadminister therapies, or that track real-time health data when a patient self-administers a therapy (Dimitrov, 2016). As public choice of healthcare infrastructure is long-termed and rigid, reliance on big data can strongly contribute optimizing predictive analytic models fed by artificial intelligence, machine learning, and data mining. These models are based on information stored in public databases that are made available to private actors through interoperability and data fusion, as it will be shown later.
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The relationships between the main PF milestones and big data, considering the PPP stakeholders, are synthesized in Table 2. This table shows the dynamic and interactive nature of big data that are continuously generated along the long-termed PF investment. Table 2 - Relationships between the main PF milestones and big data Event / Milestone
Stakeholders involved
Shaping the tender, with key indicators (perimeter, concession length, etc.)
Public proponent and its consultants
Participation in the tender and pre-bankability testing
Private shareholders of the SPV and their backing banks
Adjudication of the tender
Public proponent and private participants
project phase
Public proponent and winning SPV
construction
Public paying agent, SPV, sub-contractors and sponsoring banks
Beginning of management phase
Public proponent, SPV, banks, (different) sub-contractors
Expiration of biomedical equipment contracts
Public proponent, SPV
(senior and subordinated) debt service
Termination and (free) transfer
SPV and its banks
Public procurer and SPV
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Big data inputs Comparison between PPP/PF and traditional procurement, using market data in real time. Feeding of big data for PESTLE framework (especially Economic – Social – Technological – Environmental data). Risk / return convenience (IRRproject; NPVproject and, residually IRRequity, NPVequity) based on big data-driven business planning. Contractual (tender) Value for Money must be properly compared to preliminary Value for Money, confirming public convenience of the PPP choice. Big data enrich comparisons with new information. Public to private risk transfer or sharing depends on the architectural shaping and implementation Construction risk must be entirely private; contractual payments must be consistent with bankability and debt scheduling. SPV shareholders may change (possible exit of constructors, entry of maintenance operators or financial intermediaries, etc.), due to subjective Value for Money; performance monitoring starts. Useful life of biomedical equipment follows contractual market testing, with periodical tenders and strong qualitative/quantitative Value for Money implications Senior debt commands a priority over (riskier) subordinated debt and so it expires before; Value for Money of the SPV, its share- and debt- holders, and banks is affected by proper debt services; protective covenants may apply, together with constant monitoring. Big data-driven business planning improves income statement forecasting, with a timely impact on debt servicing. When the concession terminates, debt is typically already reimbursed and there may be room for residual dividends, impacting on private Value for Money
8. Hot and cold revenue planning Economic and financial business planning is a key component of the private bid package. Forecast of revenues and costs represents the core part of the income statement of private competitors. Budgeting of revenues, costs, and cash in/out flows depends on the investment framework designed by the feasibility plan. It has been shown that big data can contribute shaping this plan through current healthcare scenarios nurtured by massive and variegated data sets. “Hot” revenues derive from commercial activities (no-core services) represented by accommodation, laundry, ICT, parking, etc. They depend on activities that are ancillary to core healthcare functions, increasingly sensitive to big data. “Cold” revenues are concerned with remuneration for shadow services (availability payment for the management of the concession). Market risk mainly belongs to the private part for hot revenues and to the public for cold ones. An empirical analysis of the main types of services granted to the concessionaire in Italian healthcare PF investments (Finlombarda, 2013), analogically referable to other countries and industries, is reported in Table 3, together with a description of possible interactions with big data. Table 3 - Type of services granted to the concessionaire Type of services
Pay-for services to the public
Facility maintenance
No-core support services
Healthcare support services
impact of big data
Operation of commercial areas Operation of bars / restaurants Operation of hotels / accommodation for relatives Guest-room Operation of staff nursery Operation of car parks Facility management and maintenance Equipment maintenance Gardening Maintenance of technology plant Power/heating supplies Canteen and catering (staff and patients) Laundry Cleaning services (indoor and outdoor) Security guards Waste disposal Computing (fleet management) Reception, casualty, cash desk, CUP Information system Maintenance of biomedical equipment Supply of medical gases Laboratory service Diagnostic services Operation of the operating theatres Services for low care hospitalization
Big data are concerned with access information on different services (number of visitors …) and detailed segmentation of data, related and ancillary to core healthcare functions. Marketing strategies drive management of commercial activities. IoT sensors drive facility management and maintenance. Chief Digital Officers transform the way facilities are managed. Sensors in intelligent buildings are cheaper and smarter.
Support services are increasingly integrated within "intelligent" and data-driven supply chains.
IT systems, laboratory and diagnostic services increasingly depend on big data inputs. Market testing of diagnostic equipment is also related to big data.
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Ancillary services can be foreseen in the PF investment perimeter (the wider, the higher the expected revenues for the private player, with lower disbursements from the public side and higher bankability chances).
Other services Other services
Diagnosis / Care
Healthcare activities represent the core of the hospital strategies and mission, typically mastered by the public procurer. IoT and big data feed these activities in real time and get them connected with ancillary (nonmedical) services.
Healthcare activities (patients’ treatment)
Value for Money and PF bankability strongly depend on accurate revenue and cost planning. Big data may help in man complementary ways, through: 1. Better forecasting input data to be included in the private business plan for the auction, based on the public feasibility plan; 2. Mark to market feedback of revenues during the management phase of the concession; 3. Improving margins, driven by higher revenues (due to broader opportunities, new markets, etc.) and lower costs (due to better resource consumption, etc.); 4. Consequent improvement of cash flows, with better debt service cover ratios (operating cash flows divided by cumulated debt); 5. Lower risk, due to better predictive ability and timely resilience to external shocks. 9. Interoperability and data fusion with public databases The main bottleneck for comprehensive application of big data to the PF value chain and to business plan budgeting is given by the difficulty of connecting heterogeneous data sources (structured, semi-structured, unstructured) deriving from different areas (medical, commercial, technical, etc.). Big healthcare data typically include heterogeneous, multi-spectral, incomplete, and imprecise observations (e.g., diagnosis, demographics, treatment, prevention of disease, illness, injury, and physical and mental impairments) derived from different sources using incongruent sampling (Dinov, 2016). This dissimilar information feed interoperable databases through data fusion, the process of integration of multiple data that brings to interoperability (the ability of different databases to exchange harmonized information). Data fusion and interoperability of heterogeneous data sets stand out as key value adding strategies in healthcare PF since they connect public and private data, improving PPPs through a reduction of information asymmetries. Data fusion and consequent interoperability can build scalable databases, utilizing multiform apps through digital networks. Digital apps and healthcare databases are a toolbox for universal connectors to be mastered by the public entity, to avoid commercial abuses and confidentiality infringements. Knowledge search engines mine databases transferring experience and deploying data visualization. The meta database is populated automatically with healthcare and commercial data, and it is connected in real time to third party applications. The public meta databases should ideally coordinate the whole PF process, from its inception (feasibility study) to the tender, adjudication, construction, management and free transfer at the end of the concession. Each milestone is populated by different information, fuelled by current big data. Whereas revenue planning for the private player mainly rotates around “no core -healthcare” activities (that do not concern treatments and medical choices), the public counterpart typically retains a golden share over key healthcare strategies (concerning the hospital specialization, number of beds, etc.). Both are concerned with big data, albeit with different intensity. 11
“Core” healthcare data, increasingly cloud-based, are typically more easily influenced by big data than other information concerning ancillary services. This link between core healthcare activities and ancillary services is a further under-investigated aspect: even if it is evident and well documented that the latter depend on the former (e.g. the size of the hospital commands over the attendance of patients and visitors in commercial spaces), the joint impact of big data on healthcare and ancillary services is still obscure. 10. Conclusion Patients, either hospitalized or linked through sensors to M-heath applications, generate continuous data (demographic, historical, illness-related, etc.) that are processed in real time with deep medical, social, and economic implications. The challenge is to exploit these data beyond traditional e-health applications. In the age of information, data-driven knowledge reshapes business models, especially if long termed and intrinsically rigid such as those concerning PF healthcare investments. Tailor-made therapies, and not one-size-fits-all interventions, represent a new frontier for medicine, difficult to match with standardized PF healthcare investments. With their fine-tuning capabilities, big data can help, opening markets for niche products, and atomizing intervention for personalized needs. Patients with their current needs are the primary target of healthcare investments and the pivotal stakeholders of any PPP. Data-driven strategies so have to acknowledge and collect these needs. In healthcare infrastructural investments, medical data are mixed with the economic information deriving from ancillary commercial activities; in both cases, big data are an unmissable opportunity for predictive analytics improvements. Meta databases, mastered by the public actor, accordingly connect heterogeneous data sources. It has been shown that the potentialities of big data in healthcare PF / PPP investments are huge, even if largely underexploited. There are however important criticalities concerning privacy issues, being healthcare data sensitive and confidential (Fox and Vaidyanatham, 2016). Another concern is represented by cyber security, due to the potential harm that IT intrusion may cause to complex structures that increasingly depend on vulnerable IT platforms. Healthcare investments can be integrated into smart city projects, nurtured by IoT sensors that fuel big data, looking for long termed economic, social, and environmental sustainability. Medical IoT can revolutionize the future of pharma with “beyond-the-pill” businesses, digitalizing medical products and healthcare processes. The impact on the PF design of new healthcare facilities is likely to be meaningful, albeit still pioneering. Being big data available in massive terms from different sources and in real time, they are likely to have a remarkable impact on healthcare PF planning and management, with continuous feedbacks and fine tuning that reduces risk and improves Value for Money and resilience to external shocks. Value co-creation is still a neglected research matter, especially considering the hidden potentials of big data such as their networking attitudes, useful for predictive analytics within collaborative PPP stakeholders. Rising health expenditure and shrinking budget availability inevitably brings to data-driven cost cutting. Further interdisciplinary research is needed in this complex and rapidly evolving field, reflecting the impact of digital technology on healthcare issues and the managerial and strategic aspects of big data. Insights from this paper may be conveniently extended to other industries, considering the versatile nature of heterogeneous big data and the networking attitudes of healthcare service.
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Big Data Driven Healthcare Project Financing – Roberto Moro Visconti
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