Multiple trends are converging to create a smarter, more agile healthcare industry—one where transparency and quality sit at the heart of strategy. Technological advancement, coupled with a reimbursement landscape defined by value, is changing the paradigm of care delivery for the better.

In line with these developments, the dynamics surrounding optimal approaches to direct-to-provider pharmaceutical marketing are also rapidly changing. Traditional approaches built on door-to-door distribution of prescription information and samples are no longer sufficient. Healthcare providers are now looking to pharmaceutical companies to partner with them and provide clinical and financial information, giving physicians a 360-degree purview that will help patients better afford treatment plans and ultimately create better health outcomes.

Just as patient care has evolved with increased adoption of technology, pharmaceutical marketing must follow suit to remain competitive. The reality is that electronic health-record (EHR) systems are now mainstream and control provider workflows, demanding a minimum of 5 hours of provider time per day. So pharmaceutical companies must provide timely communication within the EHR in order to get the HCP’s attention and stay relevant.

Five years ago, this challenge may have seemed insurmountable, but today’s pharmaceutical companies are increasingly evolving strategies to directly engage with providers at the point of care. Technological advancement that brings together point-of-prescribe networks and a fragmented EHR market creates a unique communication channel to providers, allowing drug companies to build integrated marketing campaigns that deliver tangible value to the point of care.

Marketing efforts characterized by large sales forces and sizeable travel budgets are no longer sustainable as pharmaceutical companies try to identify strategies that deliver the greatest ROI. While door-to-door distribution of clinical information and samples may increase sales, it is a costly scheme that does not address the full picture of value for the provider.

As the concept of consumerism continues to take hold across the industry, providers need tools that empower patients in their care. For instance, consumers are increasingly looking to physicians to help them understand and manage hefty cost burdens introduced by high-deductible health plans (HDHPs), higher co-pays and a growing range of generic drug options.

Copay coupons and savings vouchers have been an important part of the solution to high drug costs, and many pharmaceutical companies have built these programs into their overall marketing scheme. Unfortunately, one ongoing challenge is raising awareness by getting the savings information in front of physicians. Consequently, busy providers often remain unaware of available opportunities, and billions of dollars in patient savings go unused, resulting in missed opportunities to engage new customers.

Growing interdependence in the payer, providers and pharmaceutical value chain means that physicians are looking to drug companies to partner with them to deliver more proactive care that promotes sustainable population-health improvement and lower costs. For example, consider the current industry focus on medication non-adherence—an avoidable problem that is connected to billions in healthcare costs annually due to poor outcomes and disease progression. Industry research points to high drug costs as a primary contributing factor to medication non-adherence. As such, providers need to empower patients with ways to afford their medications to improve the outlook. They also need access to methods for addressing alarming rates of health illiteracy and transparency into whether patients are following through with drug therapies.

A recent Surescripts report revealed that 85% of providers now use e-prescribing, making this area of a physician’s workflow a prime target for pharmaceutical marketing efforts. In tandem with this statistic, a survey by Atlantic Information Services revealed that 80% of physicians were either “more likely” or “much more likely” to prescribe a drug with a copay card, and 71% want to know about drug discount copay/voucher programs.

The business case for embedding patient savings opportunities into physician e-prescribing workflows is an easy one to make, especially since e-prescribing is driven by mandates and now mainstream across the industry. When pharmaceutical companies can deliver, track and report on this kind of content—and much more—across a large point-of-prescribe promotional network, the value proposition for physicians significantly increase. Notably, research points to improved medication adherence when patients can identify ways of lowering their copayments.

Pharma companies can elevate price transparency for providers and patients by providing financial messaging in the workflow, enabling physicians to show true costs to patients when a brand is prescribed.

During a patient encounter, physicians search for a brand name drug within the EHR, and in real-time, receive information on available offerings for a specific patient. The coupon or voucher is then printed, texted or emailed for the patient to present to a retail pharmacy provider, improving medication affordability, encouraging adherence and fostering better clinical outcomes. In tandem, the coupon or voucher is embedded in the electronic prescription and sent directly to the pharmacy.

The delivery of customized content that helps physicians better educate their patients on how to take their medications presents another opportunity to add value. Busy physicians can access needed materials during the patient encounter workflow and empower patients with the knowledge needed to successfully comply with medication regimens. In addition to savings information, the intelligent point-of-care technology can also alert physicians about recommended tests or procedures related to a patient’s condition, prompting additional action if needed.

Integrated content marketing can also provide a critical piece of information that physicians are often missing: confirmation that a patient picked up their prescription or refill. From the standpoint of proactive care that improves population health, this single insight can vastly improve the outlook and be the determining factor for whether a patient with a chronic condition ends up in the emergency room. Armed with this important insight, physicians can get ahead of the medication non-adherence problem through proactive outreach to patients.

OptimizeRx’s digital health care messaging platform is an example of a solution helping to improve critical communication between pharma companies and healthcare providers. Providers can be alerted to available financial programs right at the point-of-care. In 2017 alone, more than $2 billion in e-prescriptions were transmitted from doctor to pharmacy. Today, most hospital systems require that all medications be e-prescribed.

As personalized medicine grows in popularity and specialty treatments are more commonly prescribed, platforms like OptimizeRx that exist at the point-of-care will be a powerful enabler of communication between the pharma industry and healthcare professionals.

More than half of all ambulatory-health providers are accessible through the EHR systems that OptimizeRx is currently integrated with. Typically, a 2:1 ROI is generated for marketing resources invested in the platform.

Pharmaceutical marketing is no longer about just “selling pills.” Providers are increasingly looking to companies that are willing to collaborate with them to improve care quality and help them improve their own brand. In the digital age of healthcare, this requires a strategy that meets physicians where they are and delivers value to the point of care. Forward-looking companies must consider how to incorporate next-generation strategies that optimize this kind of exposure.

Will Febbo is CEO of OptimizeRx. For the past 18 years, Febbo has been a serial entreprenuer in companies that combine health services and technology. Currently he is also a faculty member of MIT linQ, a collaboration initiative for international training and development programs. Febbo holds a BA degree from Dickinson College.

Articles

Integrating pharma communications with electronic health-record systems brings value to patients and providers

Connecting with physicians—and patients—at the point of care

Efforts to develop innovative, lifesaving medicines are always fraught with peril, and the clinical and commercial success of any investigational therapy is never guaranteed. Today, all stakeholders in the healthcare spectrum — pharmaceutical developers, payers, regulators, physicians and patients — are putting their money on the collection and analysis of many different types of real-world evidence (RWE) as a key enabling strategy, to close critical gaps in knowledge, give physicians and patients broader access to therapies, and help payers realize the actual value of those therapies in improving health and reducing costs.

Traditional clinical trials — while still the gold standard for establishing a drug’s risk/benefit profile and appropriate dosing strategy — are not able to fully elucidate how the drug, once launched, will perform under messy, real-world conditions, where patient populations are decidedly more heterogeneous than those studied in the trial. To better assess the full clinical profile and economic impact of high-cost specialty medicines over time, efforts are underway to explore and exploit various forms of real-world data that can be collected during patient care. Then, analytic, modeling and simulation tools, including artificial intelligence (AI) and machine-learning methodologies, can derive meaningful insights from those data.

But the process of using RWE is far from straightforward. Since all data are not created equal, a variety of industry stakeholders are developing standardization frameworks in an effort to establish some form of accountability and reliability, and thus foster greater confidence as RWE is being used to advance a variety of pharma and healthcare objectives.

Today, many data sources — some traditional, others emerging — are being used to improve insight during drug development. These include:

Pharmacy and specialty pharmacy dispensing data

Patient registries related to specific diseases and products

Patient-generated data (for instance, from in-home, mobile and wearable devices and sensors)

Behavioral data and insights collected from social media

Third-party aggregators of data records provide another good source of RWE. For example, IQVIA owns a comprehensive, global data portfolio representing more than 530 million de-identified patient records across 10+ markets, with more than 3,000 ready-to-use sources from EHR, hospital, pharmacy and claims data, as well as data related to genomics, mobile-health and patient-reported outcomes.

Similarly, the healthcare AI company Prognos claims to be the largest aggregator of laboratory data related to diagnostic testing in healthcare. Its database, called The Prognos Registry, contains more than 13 billion de-identified, HIPAA-compliant clinical records, representing more than 180 million patients, from more than 140 commercial diagnostic clinical-testing laboratories, according to the company. This enormous cache — which continues to grow over time — spans many different pathologies, in 50 disease states.

Today, a variety of advanced modeling, simulation and artificial intelligence capabilities are being used to analyze data from actual clinical practice to overcome the limitations of narrow or insufficient clinical trial data. Such efforts, according to Camie Britton, Senior Director, Real-world Data Services, Parexel International, include:

Mining unstructured data (for instance, from EHR clinical notes and sensors) for research purposes

Creating retrospective cohorts of patients to better understand how a drug works in a defined indication

Developing and validating algorithms based on EHR clinical notes or claims

Using such algorithms to distinguish and characterize targeted subpopulations as a means of supporting future research

“Two general approaches are emerging in terms of how drug developers are using RWE — greater use of classical, hypothesis-driven research, and the use of newer data-analytic tools including AI and machine-learning techniques that provide insight based on pattern recognition within vast data sets,” says John Doyle, SVP & GM, Real-world & Analytic Services, IQVIA, and faculty member, Department of Epidemiology, Mailman School of Public Health, Columbia University. “We should be looking at a hybrid approach, but we also need to challenge and ‘stress test’ the pattern-recognition results and inferences that are being made about causation, to ensure that they are based on sound scientific principles.”

The second annual RWE Benchmarking Survey, [1] from Deloitte’s ConvergeHealth unit, finds that 90% of global pharma companies either have or are building RWE analytical capabilities, and that applications range across the entire product lifecycle for these undertakings.

Leveraging data from off-label prescribing

Once a drug has been approved and enters the marketplace, it is often prescribed to additional patient subgroups (beyond just the narrow patient cohorts enrolled in the clinical trial, or are specified in the approved label indication). The ability to study the drug’s performance in patients using the drug off-label is important, as it will reflect how the drug actually performs among a decidedly more heterogeneous patient population, and thus can help to paint a broader picture of the drug’s full clinical potential.

In some cases, RWE findings from such patients can help the drug maker to seek and gain regulatory approval for additional label indications for that therapy, and can help to inform clinical guidelines and other decision-support tools for use in routine clinical practice.

However, unlike the tightly screened patient populations enrolled in a formal clinical trial, patients seeking treatment for any condition under real-world conditions often have one or more comorbidities, and are already taking other medications that could lead to drug-drug interactions (DDIs). Those DDIs, coupled with unpleasant side effects or adverse events, affordability issues and other patient behaviors, often conspire to reduce the patient’s adherence to therapy, and poor adherence to therapy is one of the leading factors to undermine the clinical effectiveness and health outcomes that were demonstrated in the trial.

For many specialty drugs that are aimed at diseases with critical unmet medical need — particularly in oncology and rare or orphan diseases — fast-track or conditional regulatory approval provides an accelerated pathway for regulatory approval. “Giving patients faster access to these drugs is fantastic, but fast-track approval leaves an unfinished story still to be told,” says Doyle of IQVIA. “RWE becomes the only option to round out that conditional approval, to demonstrate safety, effectiveness and tolerability, across more diverse populations in real-world clinical settings over longer time horizons.”

Patient or product registries can yield data from a sufficient number of patients to enable statistically relevant subgroup analyses, and these findings can be used to seek expanded label indications and help demonstrate the therapy’s full value proposition to payers and Health Technology Assessment (HTA) organizations. “Such examples are exciting and they are really happening today, but frankly these RWE techniques are still being grossly underutilized to validate, extend and complement the findings of these clinical trials,” he adds.

Meanwhile, Zhen Su, MD, Chief Medical Officer for North America at EMD Serono cautions: “Monitoring how the drug responds in specific sub-populations in actual clinical settings can certainly help to elucidate who responds better to treatment, enabling drug developers to negotiate for label expansion for the drug. But drug developers must also be prepared for the fact that such efforts may also reveal patients who do not end up responding well over time, and this can lead FDA to shrink the label indication for that therapy.” This happened in June, when FDA announced it was restricting the use of Merck & Co.’s Keytruda (pembrolizumab) and Roche’s Tecentriq (atezolizumab) in first-line bladder cancer to patients who express high levels of PD-L1, revising the labels for both drugs to include a requirement for testing for PD-L1 expression. Treatment continuation can then be considered for patients responding to the drugs who are not eligible for cisplatin-containing chemotherapy, according to FDA. [2]

While RWE has historically been used to monitor post-launch safety and adverse events, drug developers are increasingly incorporating RWE-driven insights further upstream to improve clinical trial design and execution. For instance, RWE can be used to assess and validate specific biomarker hypotheses, or to create a “synthetic” control arm based on RWE derived from historic or contemporaneous patients being treated in actual practice and then use that synthetic arm in lieu of an actual control arm during the clinical trial.

“The need for clinical trials to run a control arm often creates redundancy and added time and cost for the drug developer, and for some diseases and drugs, it’s unethical to put patients into a control arm of a trial,” says Su of EMD Serono. “By comparison, the ability to create a synthetic control arm based on RWE can reduce drug-development costs and enable better treatment paradigms. This approach is especially helpful to improve and streamline the regulatory approval process for investigational therapies that are intended to address unmet medical need in rare diseases.” Su cites the example of Bavencio (avelumab), an immunotherapy developed by EMD Serono and parent company Merck KGaA for the treatment of metastatic Merkel cell carcinoma (MCC), saying: “Bavencio successfully used real-world data as a reference for efficacy, because it’s a rare disease and thus trial enrollment was challenging.”

Similarly, RWE is also making its mark further downstream, to support regulatory submissions and/or later label expansions (for instance, to pursue additional indications, additional patients cohorts or additional clinical endpoints), and to inform negotiations with payers related to formulary placement, drug pricing and the negotiation of value-based contracts or outcomes-based agreements that tie reimbursement rates more explicitly to the performance of the therapy under messy real-world conditions.

“More than ever, drug manufacturers know the trial won’t provide the final evidence package — it’s just the first part of the entire package — and they know that the strategic use of RWE can enable a deeper understanding of value,” says Sarah Alwardt, PhD, VP of HI and HEOR Operations at McKesson, adding: “Since everyone understands that clinical trial findings are limited, there’s a huge appetite for these types of retrospective studies using RWE. It’s a change in mindset that’s very exciting.”

The expanded use of RWE is already helping different stakeholders at different points throughout the biopharmaceutical product lifecycle to expand and calibrate their understanding of the actual clinical effectiveness, long-term health outcomes, cost impact and risk/benefit ratios for various therapies, says Britton of Parexel International, who notes that the growing list of RWE-informed efforts includes:

Identifying new drug targets and better biomarkers

Finding new uses for existing and late-stage products

Identifying undiagnosed, underdiagnosed and misdiagnosed illness

Identifying sites with higher enrollment probability and capability to aid clinical trial recruitment

Predicting patients who have higher risk for adverse events

Predicting medication compliance (or lack thereof) in defined patient cohorts (which can inform mitigation efforts)

Using AI and machine-learning techniques to recognize trends and patterns in social media and healthcare sensor data (to inform clinical study development and define study objectives, patient-relevant endpoints and study procedures)

“Postmarketing studies using RWE are more common now than ever,” notes Michael Fronstin, GM, Real World Evidence, Kantar Health. “Early signal detection through observational studies of newly approved medications is critical to minimizing the potentially broad impact of an unforeseen risk, should a new drug prove to be unsafe within certain patient cohorts.”

Eli Lilly’s new compound, Engality (galcanezumab-gnlm), is expected to receive FDA approval by the end of 2018 for the treatment of migraine headaches. To further understand the burden of migraine and the barriers that preventive and acute treatment options face, Eli Lilly recently announced a study dubbed the ObserVational Survey of the Epidemiology, tReatment and Care Of MigrainE (OVERCOME). According to the company, this two-year study will be the largest of its kind in migraine, gathering real-world data from 40,000 patients. [3]

By design, the patient population enrolled in any given clinical trial is limited, with targeted patient cohorts handpicked using strict inclusion and exclusion criteria, and enrollment tending to favor younger, healthier patients compared to the entire population in that disease category. Similarly, trial investigators are typically given formal training and required to follow consistent protocols for drug administration and data collection. Collectively, these factors help to greatly reduce variability as the clinical trials are carried out, and thus allow the investigational therapy to show its best self, in a trial population for whom co-morbidities and drug adherence issues have been eliminated or minimized.

By contrast, “when you have someone who is not being studied and poked and prodded in a highly controlled clinical trial, their outcomes will be different,” says Alwardt of McKesson. “By studying data from realworld prescribing, you can dig into which factors are helping to drive improved outcomes in specific patient sub-populations. In other instances, the outcomes will be worse and you can try to identify and reconcile the issues (such as co-morbidities, drug-drug interactions, dosing, complex-administration or adherence issues) that may be to blame.”

“Historically, clinical trials have been primarily focused on efficacy and safety, but in recent times clinical effectiveness has risen as a key differentiator to be studied in the trial,” adds Kantar Health’s Fronstin. “Effectiveness comes in many forms, but is often associated with patient outcomes, which may be clinical, humanistic or economic in nature.”

“Real-world data, such as patient-reported outcomes (PROs), quality of life, disease progression and epidemiology, exposure patterns and factors driving reffectiveness, can inform which parameters need to be captured in the trial design,” says Billy Amzal, MSc, MPA, PhD, SVP, Real World Decision & Data Analytics, Analytica Laser, a Certara company. “Patient registries and social media through opt-in surveys offer a good source of such patient preference data.”

“The patient’s perspective and preferences, captured through PROs, play a particularly important part of demonstrating value in rare diseases and childhood disorders, and focus groups involving patients and other stakeholders such as nurses and physicians, patient association members and caretakers) can help to elicit what types of attributes the drug maker should be looking for when designing the trial, adds Sumeet Bakshi, MBBS, MBA, VP, Real World Data Solutions at Analytica Laser. However, he adds “We find a lot of drug manufacturers coming to us very late in the design phase of a clinical trial, asking for us to identify the most relevant PROs.”

“To incorporate PROs into trials, drug developers are analyzing real-world data to identify specific patient populations that may be experiencing incremental burden or negative health outcomes as a result of their disease,” adds Fronstin of Kantar Health. “Using this insight, patient-reported outcomes can help to shape the trial design.”

Similarly, while the drug’s ability to demonstrate a favorable risk/benefit profile within this limited patient cohort is sufficient to earn regulatory approval, this traditional approach often leaves more questions than it answers, in terms of whether the approved drug can be used in other patient cohorts that were not involved in the trials (such as pregnant women, elderly or pediatric patients, those with renal or hepatic impairment, those with unstudied co-morbidities and so on). In the absence of trial-derived data, physicians are then left to prescribe drugs off-label. This raises potential clinical and ethical concerns for physicians (due to a lack of comprehensive data), and often creates a situation where insurance coverage is restricted or denied, leaving patients to manage the entire cost burden of treatment.

“There are conversations happening today that have never happened before, and in clinical trial design, there is increased appetite among both drug developers and regulators to better understand that RWE can help improve trial design,” says Alwardt of McKesson. For instance, she notes that “In oncology in particular, one of the clinical trial endpoints is almost always progression-free survival (PFS), but PFS is really hard to measure in the real world due to variation from doctor to doctor in terms of how they evaluate and capture such data.” Instead, getting more relevant, real-world endpoints into the clinical trial design will help to create a more powerful trial baseline and more realistic expectations for how the drug will perform once it enters the market.

Innovative value-based contracting or risk-sharing agreements — formally called risk-based contracts or outcomes-based agreements — have been explored for a growing number of high-cost specialty medications. Such innovative contracting arrangements provide a mechanism for biopharma companies and health insurers to both put more skin in the game when it comes to managing the high cost or risk of today’s specialty therapies. Such arrangements aim to tie contracted drug prices and reimbursement rates more closely to actual clinical outcomes that are demonstrated by collecting and analyzing RWE from clinical practice. “The whole idea is to manage uncertainty at the time the manufacturers are committing to a given clinical performance for their drug and payers deciding for a price and reimbursement,” says Amzal of Analytica Laser.

According to Deloitte, 14 of the 20 companies polled in its 2nd annual RWE Benchmarking Survey are currently engaged in value-based contracting, although many industry observers note that drug companies and payers have tended to remain tight-lipped about publicizing such agreements, hoping to preserve their own competitive advantage. Such secrecy thwarts the sharing of lessons learned, and undermine efforts to create a consistent framework or best practices to improve the data collection and expectations and to guide the complex negotiations that are needed to create a win-win for both sides through these innovative contracting schemes.

While interest in and use of RWE throughout pharma and healthcare continues to grow, many industry observers have expressed concern and frustration over the inherent variability and inconsistency of the RWE data sources that are increasingly being pressed into service. Such challenges, according to Britton of Parexel include:

Heterogeneous data and lack of interoperability

Data incompleteness, inconsistency and inaccuracy

Data ownership and data-use agreement status

Data privacy regulation / patient-level authorization / ongoing patient consent management for RWD usage

Lack of standard unique patient identifiers to enable linking across data sources

Multiple data sources and the need for ‘big data’ aggregation and ‘data lake infrastructure’

Shortage of biomedical and informatics expertise to successfully leverage modeling, simulation and AI opportunities

Similarly, Bakshi of Analytica Laser notes investigators must be aware of the limitations of the available data, and design any studies accordingly. “Claims data was set up to manage insurance claims — it was never intended to be used for conducting comparative effectiveness studies, but many are doing that today, so you have to identify the gaps and limitations.”

Many are calling for some type of industry standards or guidelines, to establish some criteria for data consistency.

“The lack of comprehensiveness, consistent quality and standardization threatens to undermine trust and limit the full potential of using RWE to advance many objectives in pharma,” says Doyle of IQVIA. “Today with so many parallel efforts, the use of RWE is being democratized, which should prompt research design innovation, but how do you validate and pressure-test the various approaches for validity and reliability? I am eager to see what type of guidance FDA ultimately issues with regard to RWE guidance.” He notes that FDA’s 2017 guidance issued on the use of RWE in the field of medical devices [4] provides a lot of hints about FDA’s thinking on this topic.

Additional industry-wide initiatives are already underway to address some of these challenges. For instance, in 2017, the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the International Society for Pharmacoepidemiology (ISPE) created a joint task force which is working to establish consistent methodologies and procedural practices to improve the consistency and reliability of using RWE [5]

Similarly, the GetReal Initiative (www.imi-getreal.eu), established in 2013 by an EU public-private consortium consisting of pharmaceutical companies, academia, HTA agencies, regulators and patient organizations, is a working to develop a variety of tools and resources for RWE.

Second Annual Real-World Evidence (RWE) Benchmarking Study, 2018, www.deloitte.com/insights

https://www.asco.org/advocacy-policy/asco-in-action/fda-limits-use-tecentriq-and-keytruda-some-urothelial-cancer-patients

https://investor.lilly.com/news-releases/news-release-details/lilly-announces-initiation-observational-survey-epidemiology

FDA, Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices, issued August 31, 2017; https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm513027.pdf

Berger, Marc., et al., Good Practices for Real-World Data Studies of Treatment and/or Comparative Effectiveness: Recommendations from the Joint ISPOR-ISPE Special Task Force on Real-World Evidence in Healthcare Decisionmaking,

The widening scope of RWE data available from patients is changing both clinical and post-approval practices for drug development

Real-World Evidence (RWE): Advancing Both Clinical and Commercial Objectives

 Fig. 1. In CareMetx’ setup, its IT system both pulls in and pushes out data from pharmacies, HCPs and payers, delivering results to the patient and the manufacturer in a compliant manner so that the patient’s journey can be monitored. Credit: CareMetx[/caption]

As the pharmaceutical industry moves toward supporting precision medicine and providing patient specific treatments, the importance of contextualized data to support clinical decision has increased. Additionally, as new reimbursement and payment models continue to be explored with the increase in gene therapy treatments, it is no longer enough to know that a product has been dispensed to a patient. Manufacturers require better insight throughout the patient journey and are increasingly looking for a single ‘source of truth’ for the patient data and are requiring direct access to the data to ensure data quality and accuracy.

At the intersection of the patient’s treatment and reimbursement, the Patient Services Hub will maintain its importance as the central repository of data. The types of data collected, the methods of collection and ultimately the ability to understand what has been collected is increasingly the most critical role of the Hub.

Various types of data can be collected throughout the patient journey. In general, the data is either transactional and describes the various events that occur throughout the patient journey, or master data and describes the ‘who’ and ‘what’ such as the Patient, Product, etc. Before exploring the methods of collection, we must understand what can be collected.

Patient Demographics and Location refers to data associated with the patient details including name, gender, date of birth, Social Security Number, address, phone number, etc. When collecting, storing and transmitting or communicating this information to outside parties, it is important to take into consideration the HIPAA PHI (protected health information) guidelines as well as PII (personally identifiable information) guidelines.

Patient Insurance data indicates the patient’s Medical and Pharmacy benefit information. This may include not only the Primary insurance information, but also Secondary and supplemental insurance information. Because patients can change insurance carriers, this information may also indicate whether the insurance is still active.

Patient Communication Preferences have become important data points. No longer are we communicating only by phone or mail. In fact, some patients prefer to communicate by other digital means such as text or chat because their disease makes it difficult to communicate otherwise. Obtaining a patient’s preference for which method works best for them helps reduce response time and provides an overall better experience throughout the patient journey.

Healthcare Provider refers to the data associated with the provider details including name, NPI number, specialty, state license, Tax ID, location, etc. Additional information related to the relationship of the provider to the patient (i.e. prescribing provider, treating provider, specialist, etc.) may also be included to indicate the full scope of the patient’s care network.

Healthcare Facility Information refers to the name, address, NPI and other relevant data associated with the specific location where the healthcare provider and staff interacted with the patient. These can be sites of care or physicians’ offices.

Specialty Pharmacy data refers to the list of pharmacies that are in network. Data may also indicate which pharmacies dispense commercial or non-commercial (i.e. sample, bridge, PAP) type of product.

Patient Contact and Family refers to the name and contact information associated with individuals involved in the patient’s care network. These could be patient family members, guardians or other contacts. It may also indicate which specific individual is legally authorized to make decisions on the patient’s behalf. This is useful especially in cases when the patient is underage.

Patient Diagnosis indicates the patient’s diagnosis information. This could include primary as well as secondary diagnosis, along with information on when the patient was diagnosed.

Patient HIPAA Consent indicates whether the patient has consented to allow their information to be shared to third parties or even with the manufacturer. As the needs for precision medicine increases, it will be important to educate patients about the benefits of sharing data. Depending on state law, consents also expire after a certain period of time. So there will also be a need to reobtain consent on a regular basis to ensure that data can continue to be shared and maintained.

Prescription Information indicates the inscription and subscription along with the patient for which the prescription is written, and provider who is prescribing the medication.

Patient Clinical Data, if collected throughout the patient journey, can help determine if the patient is adherent and also be used in outcomes-based reimbursement and payment models.

As genetic-based treatments come into the market, Patient Qualitative Data which demonstrates the patient quality of life, may be used to support outcomes-based reimbursement and payment models.

Benefit Verification and Reimbursement Information includes overall benefit coverage for the patient as well as information specifically related to the product coverage. For example: is the product covered under the patient’s plan? what is the amount covered? is prior authorization required? which dispensing pharmacy will provide the lowest cost to the patient? This information is useful not only to direct the patient to the appropriate pharmacy for dispense, and determine if additional financial assistance is needed, but also for the manufacturer to determine where to direct their Payer Market Access efforts.

Product Inventory and Dispense Data is used to indicate if the product has been dispensed or if there was any issue at the pharmacy in trying to dispense the product.

Financial Assistance Information can cover various forms of financial assistance programs including Copay and Patient Assistance programs. It is useful to indicate how many patients qualified and how many took advantage of the program (i.e. dispensed the product) so that inventory and financial support can be taken into consideration.

The types of data described above can be collected and shared through various means by the hub. It is important to note that any type of data that is to be reportable for further analysis would need to be compiled into a database for processing.

Communicating via phone, mail or fax will require some form of manual data entry if further data analyses is required. As electronic forms of phone, fax or mail are used, certain meta data associated with the transaction would be stored (e.g. size, date of transmission, individual that initiated the transmission).

Data collected via web or mobile application includes electronic interfaces such as portals, EHR systems and other user interfaces that display or allow users to input data.

Data feeds allow data to flow from one application to another in a structured manner without manual intervention. These methods are typically used to also transfer data for the purposes of data aggregation. There are two types of data feeds. SFTP (Secure File Transfer Protocol) is a means of transferring files through upload or download between two computers or servers in a secure manner. Web Services are used when transferring data in real time. These typical protocols include REST API or SOAP API.

In the case of the CareMetx hub, data flows in and out of the hub using all of the above transmission methodologies.

Driving outcomes to support precision medicine

With the ever-increasing data needs to obtain patient level analysis, simply collecting and aggregating the data is not enough. With the rise in patient specific treatments, manufacturers will require a closer and more intimate knowledge of the patient and their journey. The focus and effort put into data aggregation and collection should always keep in mind the overall objective, which is to facilitate the full journey from patient identification and product administration to monitoring patient outcomes.

To ensure that the data retains its relevancy, integrity and quality, additional items need to be taken into consideration:

Was the data transformed or altered prior to receipt? If so, why and how?

At what point in the patient journey is the data being received?

What is the purpose, intent and priority of the data in relation to the patient journey as well as the rest of the data inputs?

Is there consistency in the reference data used to ensure comparisons between data across various systems are made appropriately? For example, something as simple as considering a patient to be Active on product in one system could be as soon as they are dispensed medication, whereas in another system they are Active when they call in to request assistance. Or it could be something associated with how Insurance Plans are categorized across systems?

Understanding the above will assist in adjusting the data aggregation and cleansing rules as reporting and analytics needs, as well as data sources and types, change.

As the legal and regulatory framework improves to allow data to be shared securely across different parties, and transmission standards and methods become widely accepted, manufacturers will be able to obtain additional data that can be used to further support their goal to provide patient specific treatment.

The hub will continue to play a central role in the collection, aggregation and integrity of data in order to help manufacturers navigate through changes in the patient journey.

Tanvi Kachhy, Vice President at CareMetx (www.CareMetx.com), leads the effort in advising and assisting clients in creating operational and technical solutions for their complex specialty therapies. She has over 15 years’ experience in developing solutions for the med device, digital health and telecommunications industries. Tanvi holds a BS electrical engineering degree from Rensselaer Polytechnic University.

Patient-support Hub services are a vital repository of the patient journey

Precision medicine and the importance of data

 sat down with Matt Wallach, president, shortly after the company’s annual US R&D Summit, to talk about data, industry IT practices, and where it hopes the industry will evolve. Here’s what he had to say.

1. Let’s start with where Veeva started, in CRM. Back in the mid-2000s, Veeva came out with one of the first pharma-specific, cloud-based CRM systems for sales reps. How has this application developed since then, and what does it signal for pharma’s future IT development?

So, as of today, the majority of pharma sales reps around the world use Veeva CRM software. I think we have helped to enable pharma companies to adopt more digital communication channels, such as interactive content on an iPad, email, and video conferencing. And we’ve been able to do that just as part of the CRM system to make it easy for sales reps to adopt.

One of the things that’s very different from when we started the company 11 and a half years ago, is that cloud computing is now well accepted, well established, and I’m quite sure we won’t see another on-premise software installation on the commercial side of life sciences. The life sciences industry has moved to the cloud for commercial systems entirely and is much better off as a result. IT teams are not spending as much time keeping the lights on and managing servers and data centers. Instead, they’re adding value to the business by helping to enhance those systems based on feedback coming in from end users.

Another fundamental change is that the industry is having success in moving towards more digital channels. There’s a lot more information that the sales reps have to consume. And for many companies, it’s simply too much information for any mere mortal to consume, to understand, and to make insights from. And so, like many other industries, pharma companies are starting to use different forms of AI to inform the reps about what they should do. And the better integrated their systems are, the faster they get new streams of data included, like who wrote a prescription, or who attended a conference, or asked a medical question. The better that data collection is, then the more accurate the machine can interpret it and give suggestions to the reps.

I think that of the top 50 pharma companies, at least half are doing some kind of artificial intelligence or certainly some advanced analytics around giving suggestions to reps about what they should do. Very few have made global decisions and global implementations, because it’s still very early in the whole AI life cycle, but I do expect that that’s something that will continue.

A third change is the idea of orchestrating the sales rep engagement such that other specialists, like nurse educators or medical science liaisons, can be involved in physician interactions. There are some who say that traditional CRM is “dead,” and being replaced by this orchestration. Instead, what I would say, is Veeva customers have been orchestrating their engagement with healthcare providers, nurse educators, nurses, reimbursement specialists, and others for 10 years now. The notion that pharmaceutical reps don’t already know how to collaborate in the field is naïve.

Before Veeva began making software, if a pharma company had five different types of representatives in the field – nurse educators, reimbursement specialists, primary care sales reps, specialty sales reps, medical scientific liaisons – they might have likewise had five different systems. Veeva created a system that worked for all of those groups, which enabled orchestrated customer engagement. That’s why I don’t think that this is the next generation of CRM. I think that this is how Veeva customers use the CRM today – one system everyone can see for better coordinated engagement.

I think there are some parallels between where CRM was in 2007 and where clinical systems are in 2018. However, the level of complexity is quite a bit different: The biggest difference is the number of different companies that need to collaborate with each other in clinical. In CRM, it’s basically a pharma company running their sales force. Occasionally, they’ll have a co-promote partner, but primarily, they’re selling their own products, they control their own data, they control their own content, and it’s kind of a contained enterprise system for a pharma company.

With clinical trials, they are generally paid for by the pharma company, executed by a clinical research organization, a CRO, and they’re actually done at sites by clinical investigators. In order to streamline clinical trials, you can’t solve just one of those three. You can’t have a better system for the sponsor, and expect that things are going to get better.

So, if we take EDC [electronic data capture] for example, the way that EDC has been done over the past 20 years, it was a data collection tool that made it easier for the sites to capture data electronically, instead of capturing it on paper. However, EDC systems didn’t do anything to make it easier for pharma companies to cleanse the data and get it ready for submission. And they didn’t do anything to help the sponsors combine the EDC data with lab data, images or other information. So, in order to really streamline clinical trials the way we did in CRM, we need to solve and improve processes for the sponsors, CROs, and the sites.

We have a vision of the future where, if we can have a significant number of sponsors, CROs, and sites using Veeva Vault to run their own operations, then we can connect them using software on the Vault platform to automatically move documents around. Among other things, this will dramatically decrease the cycle times of many of the tasks that get done over and over and over, on every clinical trial.

3. One of the key attributes of clinical research IT is how diverse its providers are. There seems to be multiple vendors for dozens of different steps in clinical research and regulatory submissions. From trial design to patient reporting to trial master files, FDA submission processes, and so forth. Interoperability is a mirage. And Veeva wants to bring all of that together, under one platform? Under one umbrella?

Yes! All of the software platforms that companies use to run clinical trials, but not all of the data. So, there’s still other systems that will be required to capture and manage images, to capture and manage lab data, mobile health, all the data coming off of the Apple watches, for example. We’re not going to manage all of that data. Companies will interpret that data, create a document or an insight, and then load that into Veeva. So, Veeva’s not trying to replace every single clinical system, but we do want to become the repository, and kind of the cleaning area, to get all the data ready for submission. Much of this resides in our Veeva Vault offering.

This is very ambitious, but it feels like we have the industry rooting for us. The feedback that we get from customers is that they want us to be ambitious because they’ve seen us achieve some very big things so far in the adoption of Vault across their business. And they ask us constantly what can we do. And so they do like our bold vision for a clinical trial landscape that is dramatically simplified as a result of the industry adopting Vault as a platform.

There are three main parts of Veeva’s roadmap for Veeva Development Cloud – our suite of applications for research and development. One is Vault Safety, which is for the capture, tracking, reporting, and analysis of adverse events. This product is slated to become available the beginning of 2019. We’ll be investing in that product for many years to come.

The second is Vault CDMS [Clinical Data Management System], which is an expanded view of what we’re doing around clinical data management. So, rather than just have a better EDC system, now we have announced that we will have a full blown CDMS system. And so that is what I was just describing, where in addition to a more modern EDC with coding capability, we also will be releasing Vault Data Workbench – available in the middle of 2019, which is a data lake that is used to clean and manage all of the different data sources with EDC being just one of these sources.

Finally, the third is enabling a clinical network to connect sponsors, CROs, and sites working on clinical trials, so that we can eliminate the need for a lot of the manual work of moving documents, and data across those three different parties.

I do not expect pushback from CROs. And the reason is because CROs, I believe, genuinely care about the efficiency of trials. And in the last 20 years, many parts of running clinical trials have not gotten better. And one of the things that has held the industry back has been a lot of different software vendors, and platforms, and technologies competing with each other without kind of a bigger goal in mind.

So, many of the CROs that we’ve spoken to are very supportive of standards. In fact, Align Clinical CRO is a group of leading CROs and Veeva coming together to develop common technology standards to help streamline the industry and improve collaboration. Most of the large CROs are members. So as long as we remain a good partner to CROs, and a good partner to sponsors and research sites, I think that they will adopt the platform. The CROs will continue to differentiate on their scaling capability, and their overall level of service, versus on a chosen technology.

5. In clinical research and elsewhere, the issue of data quality keeps coming up. You’ve talked about the cleansing processes built into Veeva applications—is that how Veeva addresses the data quality issue?

We always knew that Veeva Vault would be used in a regulated environment to help with compliance. And so we built from the beginning a very detailed audit trail, so that our customers can track, and manage the information around everything that happens to a piece of data, or to a document from the moment it is entered into the system, for its life cycle.

So, every time someone views a document, or a piece of data. Every time they change it, every time they add something, every time they delete something from the system. Every time they email a document to someone through the system, all of these things are tracked throughout the system. What that enables is companies to stay tightly in compliance with all kinds of regulations. And when the authorities do come to audit them, they’re able to prove exactly where their data has been, who has had access to it, what they did with it. And that’s been an important compliance tool for our customers.

This is one of the areas where our foundation in cloud technology is good from an economies-of-scale perspective. The level of rigor that Veeva is able to invest in things like data quality and security is way beyond what any one of our customers would do on their own because we are safeguarding the industry’s data. And so we are willing and able, and really required to spend a lot more to do that, than what any one company would spend to safeguard their own data.

Regarding data cleansing—there are multiple types of data cleansing from a clinical perspective. And it’s basically looking for mistakes in the way that data was entered. It’s looking for bias in the data that may be a result of location, or other medications that people are taking. Or the patients that were selected for the trial. And it’s also looking for things like fraud.

Some of the cleansing is manual, and some of it is a lot of statistical analysis to really try to figure out, “Is the data clean and can it be trusted before it is submitted to the authorities.” Then, of course, once it is submitted to the authorities, they do a lot of that same type of analysis to make double sure, triple sure, that the data can be trusted, and that the new drug is safe and effective.

6. Given the range of applications that Veeva is involved in, do you have a perspective on how the industry’s data management will blend clinical and commercial? Some industry commentators say that this is already happening, being driven by the push for real-world evidence.

I think the industry is still in its infancy in truly connecting clinical and commercial data. Some of that is because of the regulations that exist, but some of that has also been because it was never required to be successful in many therapeutic areas. Now, everything is changing, and the rate of change is accelerating rapidly because of the high percentage of specialty drugs that are being developed and commercialized.

Increasingly today, there are overlaps of common information needed in R&D and commercial, such as identified patient data. For example, if you’re developing a drug for an orphan disease that has only two or three thousand patients, as the company developing that and commercializing that drug, you actually have to know the names and the addresses and the insurance companies of all of those patients. That forces the connection between clinical and commercial, and I think that as this happens more companies will learn to connect these areas more seamlessly and more efficiently, and it may expand into other drug areas beyond orphan drugs.

A Conversation with Matt Wallach, Veeva Systems

At this point, the state of the art for managing commercial-operations data in the US life sciences industry is pretty clear.

It is cloud-based, to accommodate the vast increase in the volume of data (and their sources, which are themselves often cloud-based). The relatively low cost of data storage allows for as much data tracking to be enabled as users could want, especially for purposes of audit trails, regulatory submissions and the like.

It is a platform with various specialized applications, so that data can flow relatively effortlessly from one function (say, master data on prescribers) to another (say, rep call plans)

It allows for dashboards to be designed and implemented—sometimes with the user’s own efforts—to provide management-level views of what’s going on in the organization.

Interoperability is claimed almost across the board by IT vendors: Whatever system they are offering, the claim is made that it can be connected to legacy software or other applications. How well this is carried out in reality is an open question, both because of how the software is written and because of how well vendors can carry through on their claims, so buyer beware.

Year after year, the pharma industry gets hit with new regulatory frameworks that place demands on data-collection and -collation systems, which luckily are in place (most of the time) as the regulations come into force. Pharma marketing organizations had some heavy lifting a couple years ago as aggregate-spending rules came into force, requiring the industry to tote up—by HCP—how much money or “transfers of value” are spent by that company annually. Now, the Open Payments System, where HHS publishes these annual reports, has been humming along relatively quietly.

In fact, the 2018 Open Payments report, summarizing spending and activity for calendar-year 2017, showed the first decline in aggregate spending since the reporting started in 2013: In 2017, industry spent $8.4 billion as recorded in 11.54 million transactions, as compared to $8.81 billion and 12.4 million transactions in the prior year. There are undoubtedly a number of factors causing the decline, such as the number of product launches or new indications for approved drugs—but one factor might be the higher scrutiny the industry is getting, with debates on drug pricing and reimbursement policies rising in intensity.

A more recent development affects sample accountability and reconciliation, monitoring the industry’s longstanding practice of handing out free samples of drugs to prescribers. Starting last year, the state of Ohio requires a license as a “terminal distributor of dangerous drugs” (TDDD) when certain classes of prescribers receive most types of Rx drugs. (The law came into being in part as a response to the ongoing opioid crisis, which has hit Ohio particularly hard, but was written to pertain to most Rx drugs.) In red highlighted letters, the instructions from the Ohio Board of Pharmacy states: “This includes drug samples.”

Providers of sample accountability systems have had to adjust their data-collection systems, which already track DEA licenses of prescribers, and record signatures when prescribers receive a sample (a requirement under the Prescription Drug Marketing Act). “Our Titanium platform, which includes sample accountability and practitioner validation, was written to include TDDD” by auto-detecting an Ohio address and then sending out a notification for the license information, says John Cunningham, EVP at QPharma. “On their own, pharma manufacturers are struggling with the TDDD requirement, piecing together a solution that requires a lot of manual intervention.”

The Titanium suite is one example of how IT systems have grown to encompass more sales operations functions. Separate modules handle KOL engagement, call center interactions, product launch processes and more; CRM is but one of many other modules. QPharma asserts that the integrated suite (for which individual modules can be acquired by pharma clients) allows for rapid data dissemination between functions, so that, for example, sales reps’ calls can be informed by prescriber’s sampling history.

Controlling sales and marketing practices remains something of a blood sport between the industry and state or federal regulators. The industry has been hit with fines ranging from millions to billions of dollars; new, tighter controls always seem to be in the offing (most of which require additional reporting by industry, and become a burdensome data-management task); at the same time, software systems get more sophisticated in what data can be extracted for interacting with patients, monitoring prescribing practices and anticipating changing market conditions.

“Today’s software systems are both an ‘offensive’ tool, to improve market position, and a ‘defensive’ tool, to maintain compliant practices,” says Ramon Chen, chief product officer at Reltio. Reltio has been offering the Reltio Cloud, a master data-management (MDM) system for, among other things, compiling trustable data on physicians and their prescribing and communication preferences.

While there are still pharma companies operating with on-premises software, necessitating IT staffs to maintain the hardware of data centers and communication networks, much of sales, marketing and commercial operations has moved to the cloud. Amazon Web Services, Microsoft Azure, Google Cloud and others offer on-demand data storage capacity, and are differentiating their cloud offerings with a variety of software applications or programming environments that enable easier use of large datasets.

To a considerable degree, Veeva pioneered the commercial-pharma march into the cloud, and Matt Wallach, Veeva president, touts the fact that Veeva adapted to modern cloud-based IT processes from its beginning, as opposed to switching on-premises applications to a hosted environment, and then into the cloud.

In a recent investor-day presentation, Veeva officers outlined its future direction, and the structure of how its cloud-based offerings will evolve.

A key next phase, according to Paul Shawah, SVP, Commercial Cloud, is to upgrade industry’s data-warehouse capabilities. Today, most companies have multiple data repositories (on-premises or cloud-based); these create or hold data that then flows into a data warehouse if an integrated data-management approach is to be taken. The problem is keeping the various applications (which are continually updating) in synch with how the data warehouse operates; a lack of synchronization means that data won’t flow into the warehouse for effective use. As shown in Fig. 2, there are many internal processes in the data warehouse that need to be coordinated.

Now, Veeva is beginning the introduction of Nitro, a packaged commercial data warehouse (and built, coincidentally, on a database infrastructure” from Amazon called Amazon Redshift). “We’re turning [data warehousing] into a standard industry product, something that’s been messy and custom with lots of services for a long, long time,” Shawah told his audience. “That will establish an ecosystem of partners and services providers that are able to all work on the same system, the same product, the same data model.”

Specific benefits of using Nitro, says the company, will be to enable the analytics and AI processes that it and others are developing to access curated data quickly and easily.

The big guns in dedicated, life-sciences IT systems for commercial operations are Veeva and IQVIA, the former with its Veeva Commercial Cloud and the latter with OCE, the Orchestrated Customer Engagement platform. Point-to-point comparisons are difficult, in part because Veeva is primarily a software developer, while IQVIA provides a host of operational and advisory services, in addition to its software offering. (And, continuing the distinction into the clinical realm, Veeva is now making a strong push into software for clinical research, while IQVIA, through its merger with Quintiles, is a major contract research organization.)

Interestingly, both companies use the SalesForce.com IT resources as the underlying foundation of their systems, and SalesForce.com (the company itself) has considerable play as a life sciences CRM provider.

An area of direct competition between the two has been over master data management (MDM) and the reference data on HCPs that both maintain—IQVIA’s OneKey database, and Veeva’s OpenData offering. Since January 2017, there has been litigation between the two, with IQVIA claiming improper use of its data, and Veeva claiming anticompetitive behavior. Based on the court docket (2:17-cv-00177-CCC-MF IQVIA Inc. et al v. Veeva Systems, Inc., in the District Court of New Jersey), the case is going hot and heavy, with claims, counterclaims and special master rulings happening almost weekly. Even with all this, the case is still at the discovery phase.

IQVIA, in its former incarnation as IMS Health, has been a longstanding provider of physicians’ master data; Veeva built up its offering over the past few years, with a couple acquisitions along the way. According to Veeva, it is able to continue offering commercially successful services to pharma clients, in part through the data stewardship services it provides. The stewardship function, in turn, highlights the dynamic and time-sensitive nature of reference data, which starts with physicians’ names, addresses and contact information, but now goes well beyond that to include affiliations (i.e., the health systems that HCPs are employed by or affiliated with and, in turn, the insurance providers those health systems use), and a host of professional activities (conference attendance, speaking engagements and so on) that HCPs participate in. Affiliations change at a fairly rapid clip; insurer policies (and their formularies) do the same. Data elements like the recently adopted Ohio TDDD simply add yet more fields to include.

Several other organizations compete in the MDM/reference data space, notably MedPro Systems, whose MedProID database is used by a number of life sciences CRM vendors, such as StayInFront and Synergistix, as well as sample-accountability providers like RxS (which, in turn, offers a dedicated sample-accountability and -management system, LinkedRx).

The AI and MDM company Reltio occupies an interesting position in all this. Reltio is one of the underlying technologies to support IQVIA’s OCE; it is also a relationship partner with MedPro. Reltio’s Chen notes that many pharma clients deal with it directly (and not just through its business partners). Reltio started out as a more sophisticated way of identifying and scoring relationships among HCPs and their affiliations; its algorithms and machine learning technology has now evolved where the output of Reltio, when applied to a dataset, results in a measurable level of “trust” (in the sense of, being accurate) in data relating to HCP characteristics. This degree of trust can then produce more actionable insights, such as a suggestion to contact a physician who might have become more active in handling a certain disease state.

Reltio has just announced an update to Reltio Cloud: Key new features include Reltio Rank and Reltio Data Quality IQ. Reltio Rank can be compared to what is commonly understood about Google Page Rank—the process of analyzing how frequently a page is linked or viewed by users, says Chen. “How often does the information on a profiled change, and how often is that record referenced by users? This tells the pharma company that, for example, certain HCPs need to have closer attention paid to them.”

Reltio Data Quality IQ builds on an existing feature of Reltio, Reltio IQ, which is a method for evaluating the quality and usefulness (to the user) of data sources. “Together, Rank and Data Quality IQ allow the pharma company to decide what information is worth spending more time cleansing and updating, based on the importance of the profile that that data define, avoiding wasted time and effort.”

AI is the leading next-generation step being taken for MDM, and indirectly for sales and commercial operations platforms. Veeva is building AI functionality into its Commercial Cloud with an offering branded as Andi, to be released in 2019. IQVIA has Ada, designed to offer recommendations to field sales reps or marketing teams, based on analytics of the stored information. SalesForce.com is in the process of implementing its AI offering, Einstein.

There are also dedicated analytics firms, not touting their AI expertise, but rather depending on skills in “network analytics” and related business-intelligence methods, to arrive at similar benefits claimed by Reltio and others. Zephyr Health is one; Shyft Analytics is another. (Shyft is now owned by Medidata, a clinical-research software firm, and Zephyr has been acquired by Anju Software, a clinical and medical-affairs software provider.)

Both claim to offer value in MDM analytics, among other applications, and in using large datasets to create clearer pictures of customer relationships, and then recommended actions to be taken in such activities as product launch. Shyft touts is value in analyzing real-world evidence (see p. 22) for drug-development purposes (undoubtedly why it became a target of Medidata). Zephyr has a relationship with Veeva, to provide recommendations for sales activities.

Amazon Web Services Seattle, WA; www.AWS.amazon.com

Microsoft Azure Redmond, WA; www.azure.microsoft.com

QPharma Morristown, NJ; www.QPharmaCorp.com

Salesforce.com San Francisco; www.SalesForce.com

Shyft Analytics Waltahm, MA; www.ShyftAnalytics.com

StayInFront Fairfield, NJ; www.StayInFront.com

Synergistix Sunrise, FL; www.Synergistix.com

Veeva Systems Pleasanton, CA; www.VeevaSystems.com

Zephyr Health San Francisco; www.ZephyrHealth.com

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