Data-driven healthcare transformation: where do B2B go from here?

More data seemingly creates more data security problems. But in reality, a data-driven approach has all the protections at its core, including those related to the exchange of patient information, the integrity of medical applications, access to healthcare applications, etc.

Some of the measures organizations must take for an adequate level of data protection include: 

• Monitoring and assessing regulatory compliance 

• Defining data management policies and keeping them up-to-date

• Introducing “secure by design” data systems

• Training employees to avoid healthcare data security threats

Individuals willing to take on a more active role in their care is another factor leading to the rise of data-driven healthcare. Interestingly, patient concerns make as big of an impact as patient needs.

For example, concerns about the safety of personally identifiable information put pressure to apply de-identification methods, encrypt data, etc. Also, businesses are now demanded to communicate the benefits of sharing data with patients. Otherwise, people are not comfortable allowing access to said data.

Access to a broader health technology changes how B2B companies operate. Particularly, the “cloudification” of the healthcare sector and the growing adoption of medical wearables have expanded the use of electronic health records. Here is how:

• Cloud-based platforms improved the level of communication between care teams.

• Wearables made it possible to introduce data-based remote medical monitoring and predicting. This relates to everything from consumer devices to medical-grade devices. 

Next-gen innovations also include AI and precision medicine, as well as the use of machine learning algorithms.

With respect to big data, some of the data-driven healthcare transformation trends are: 

• Admission rate predictions - Based on past data, organizations can estimate how busy the facility will be at a particular time. And by using future admission rates, they can allocate staff and resources adequately.

• Preventive care - It allows the staff to identify recurring patients and offer preventive plans. 

• Detection of data inconsistencies - Big data analytics can help flag inconsistencies and gaps in patients’ records. The system can also alert care teams about possible errors.

• IoT - The data collected from IoT sensors, like glucose levels or blood pressure, can transform medical processes, potentially replacing a doctor visit with a phone call. 

• Fraud and waste prevention - Finally, big data can tackle deliberate healthcare fraud and unintentional input errors that staff make.

Since any change affecting patient treatment has to go through several approvals, a lot can change from the original idea to its official implementation. And it’s management’s responsibility to cover the entire process—from the first emergence of an idea to a new standard of diagnostics or treatment.

Innovations can be small (e.g., new ways of sterilizing tools) or substantial (e.g., a new paradigm of treating diseases). Managing teams usually decide whether these changes will take place or not, often being guided by the size of the investment.

As volume-based care is getting replaced with value-based care, healthcare providers are incentivized to offer more value for a lower price. Those who participate early will be able to enjoy greater customer satisfaction and market share.

This transformation starts by identifying a segment of patients with a consistent set of needs. Then, a multidisciplinary team of caregivers will design and deliver a comprehensive treatment solution. As health outcomes improve, organizations will be better equipped to serve patients through expanded value-based partnerships.

Different types of public-private partnerships include:

• Partial privatization of state-owned businesses 

• Private finance initiatives

• Commercial use of government assets

The available data will allow providers to re-examine the way these partnerships have been undertaken. Moreover, it’ll help them decide how to deliver appropriate, imaginative, holistic, and beneficial partnerships.

Also, the sector needs to address ethical concerns about the legislative framework, data ownership, and commercialization of research results.

In some ways, the healthcare industry is exceptionally receptive to new technology, especially regarding medication, diagnostics, drug delivery, devices, and everything else that makes care less costly, disruptive, and painful.

But in terms of digital technology, its role has only recently taken center stage. Healthcare business methods have changed, and companies can no longer afford to lag in innovation. Someone else will if a company fails to connect the many islands of information and leverage it for its benefit.

In addition to competitiveness, the sheer desire to offer a better service propels the healthcare industry to become more open to new tech.

Companies that are about to adopt a data-driven approach must put particular emphasis on the following strategic components: 

1. A data-driven culture - Putting data at the forefront of the corporate culture can start with educating participants. A cultural evolution within the company won’t happen overnight, so management must be willing to put time and effort into showing how data solves critical problems and how to make it a part of decision-making, not an afterthought. 

2. Data-driven experiences - This strategy involves gathering data that will answer questions about the customer. It also means using the answers to make meaningful changes rather than just learning about customer needs.

3. Data-driven products - This strategy takes a step back from the customer and looks at the bigger picture. It implies analyzing market interests, trends, and patterns to create products that are necessary for your niche. 

The track record of data-driven healthcare transformation features many success stories. We’ve collected five cases of data helping health facilities:

• Penn Medicine and its Palliative Connect trigger system to make health predictions.

• Texas Children’s Hospital and its Health Catalyst Data Operating System to improve scheduling, submission, and the use of hospital space.

• Lucile Packard Children’s Hospital and its HP IDOL search and analytics engine to support decisions of medical workers and administration.

• Envision Physician Services and its Power BI-based system to predict admissions.

• Ysbyty Gwynedd and its Early Warning Score monitoring system

From the patient’s perspective, these insights lead to increased transparency (including outcomes and pricing information) and a more active role in the decision-making process—a provider’s perspective results in converging data silos and care continuity.

It is centered around the idea of using accurate, up-to-date data to guide and determine the decision-making process.

Here are the characteristics that describe data-driven healthcare organizations: a single source of “data truth,” a data governance strategy, system-wide data literacy, and a robust cybersecurity framework.

Here is the recipe for an efficient strategic initiative:

• Properly collecting data

• Keeping scalability and interoperability in mind

• Closely monitoring human-algorithm interactions

It modifies business processes, culture, and customer experiences on a large scale. On a closer scale, it has introduced sweeping changes in areas like patient flow, staffing, scheduling, and supply chain management.