A PDM application is a payer-industry-specific solution that helps payers’ IT and business teams. It also helps:
- Manage complex data combinations for in and out of network providers.
- Organize and manage required provider data by specialty types, business and legal entity structure, provider demographic information, and their affiliations to practice locations.
- Validate the demographic and contract data of providers for payer administrative purposes, such as provider directory creation, medical shopping tools and claims payments.
- Continually monitor provider data sources and alert healthcare organizations of provider data discrepancies and inaccuracies with data changes.

U.S. Healthcare Payers’ Provider Data Management Applications

3D Print Service Bureaus

3D Printing Workflow Software

4G and 5G Private Mobile Network Services

4PLs

5G Enterprise Data Services

5G Network Infrastructure for Communications Service Providers (Transitioning to CSP 5G Core Network Infrastructure Solutions ; CSP 5G RAN Infrastructure Solutions)

A/B Testing Tools

AI Avatars

AI Code Assistants

AI Offerings in CSP Network Operations

AI and Data and Analytics (D&A) Service Providers

AI in CSP Customer and Business Operations

AI-Augmented Software-Testing Tools

AI-Enabled Interview Intelligence

AI-Enabled Translation Services

AIOps Platforms

API Management

API Protection

Absence Management Software

Access Management

Account-Based Marketing Platforms

Account-Planning Tools

Accounting Practice Management Software (APMS)

Accounts Payable (AP) Recovery Audit Services

Accounts Payable Invoice Automation Solutions (Transitioning to Accounts Payable Applications)

Active Metadata Management

Ad Tech Platforms

Ad Verification Tools

Adaptive Project Management and Reporting

Advanced Analytics Service Providers for Marketing

Advanced Contract Analytics

Advanced Distribution Management Systems

Affiliate Marketing Platforms

Alternative Legal Service Providers (ALSPs)

Analytics Query Accelerators

Analytics and Business Intelligence Platforms

Analytics and Decision Intelligence Platforms in Supply Chain

Anti Money Laundering (AML) Software

Applicant Tracking Systems (ATS) (Transitioning to 'Talent Acquisition (Recruiting) Suites')

Application Composition Platform

Application Crowdtesting Services

Application Delivery Controllers

Application Development Life Cycle Management

Application Integration Platforms

Application Platforms

Application Portfolio Management Tools

Application Security Posture Management (ASPM) Tools

Application Security Testing

Application Testing Services, Worldwide

Artificial Intelligence Applications in IT Service Management

Asset Investment Planning Solutions for Energy and Utilities

Asset Leasing Software

Asset Performance Management Software

Audience Intelligence Platforms

Audit Management Solutions

Augmented Analytics

Augmented Data Quality Solutions

Augmented Reality Development Software

B2B Customer Community Platforms

B2B Gateway Software

B2B Marketing Automation Platforms

B2B Profit Optimization Software

BPM-Platform-Based Case Management Frameworks

Background Check Software

Backup as a Service

Balance Sheet Management Software

Banking Payment Hub Platforms

Bankruptcy Software

Barcode Software

Benchmarking Services for Transportation Rates and Logistics Performance

Benefits Management Software

Blockchain Advertising Platforms

Blockchain Consulting and Proof-of-Concept Development Services

Blockchain Platforms

Blockchain as a Service

Brand Advocacy Services

Brand Compliance Software

Brand Protection Software

Brand Strategy Agencies

Breach and Attack Simulation (BAS) Tools

Business Continuity Management Program Solutions

Business Process Automation Tools

Business Process Management Platforms

Business Valuation Software

Business-Outcome-Driven Enterprise Architecture Consulting

CPS Protection Platforms

CRM Customer Engagement Center

CRM and Customer Experience Implementation Services

CRM in Life Sciences

CSP 5G Core Network Infrastructure Solutions

CSP 5G RAN Infrastructure Solutions

CSP Customer Management and Experience Solutions

CSP Revenue Management and Monetization Solutions

CSP Service Design and Orchestration Solutions

CSP Service and Network Assurance Solutions

Candidate Relationship Management (CRM) Software

Capital Investment Management Software

Carbon Accounting and Management Software

Carbon Offset Platforms

Cash Flow Management Software

The right dataset is always the 98% to success. The more comprehensive, integral and noise-free the data is the less concerns you have with the model.

Bias and ethics … in both cases it comes down to humans applying the technology appropriately and ensuring the training data is appropriate:

The degree of intelligence in AI comes with quality of data used for training and its very rare to get perfect dataset in any field. So, the bais introduced due to this can lead to different results leading to difficult real life situations. So, applications of AI into fields that deal with human lives, economy, physical security, privacy like healthcare, banking, threat scanners etc. can be very concerning. The interpretation of a situation to get right context and handling within moral/ethical domain is still a big challenge with using AI.

Primarily two things - 1) AI is built on probabilistic models that can be altered through the learning process that can be altered via data sets that can be modified. This means someone with bad intent can misuse it. 2) AI cannot think. It can simply process information that can result in biased results. Human life is not black or white. Our decisions are not black or white.

The most concerns about AI are related to:

Determining the right data set, Data security and storage, Infrastructure and Integration

I would be concerned about the completeness of the results. The results of AI depend on a set of data available at the moment. How good was the data at that moment the analysis was run? It's going to be tough job to keep it up with for AI engineers. Learning curve and employee retention is going to be challenging. In some industries e.g. Cybersecurity, data feeds change every second, the results of AI would change fast, how complete is the analysis or result of the algorithms!!

I don't have any strong concerns. Like anything, if you have bad data then you won't get meaningful results - the data issue is less of an AI problem and more just are you able to frame the problem properly and are you able to get the right (accurate) data to feed into the models for your problem.

Much the same that is concerning about other big data - who controls the data, what bias does it have, and who is ensuring the security of any type system?

AI will revolutionize the future in many sectors, the critical part of AI is to have good clean data for AI modeling, the risk is about available skill sets and validating the AI output to meet the organization requirements.

1. Companies utilising algorithms and data without understanding / having been disclosed the underlying logic and data sources --&gt; data cards will help this in the future
2. The post-hoc application of ethical considerations, instead of asking the question upfront "what are the potential consequences and should be really be doing this" --&gt; for a good discussion on this topic check out the various resources by ISO, IEEE and the OECD
3. Notwithstanding my concerns about 1. and 2. the speed and lack of understanding that underpins some of the legislation and regulation in the US, Europe and other parts of the world potentially create more problems that they solve. A decent example of how this could work can be found in the approach by the Singapore government.

My biggest concerns about AI is that jobs will be displaced and employment taxes will be lost.


A simple job may be easily replaced by AI.  The employer &amp; the customer may both be happy with the service being provided AI but we don't tax AI at the same level we tax a normal worker.

Thus AI can remove tax income (federal, state, unemployment, social security, etc.).

I am concerned about the potential for AI to be used for malicious purposes, such as creating false information or manipulating data to achieve a desired outcome. I am also concerned about the potential for AI to be used to invade our privacy, for example through the use of facial recognition technology.

The biggest concern I have is bias in the training data. We've seen it happen on several products and the impacts can be significant, both financially and morally.

First of all, rather than talking about the concerns, we'd better think about how can we make some changes to embrace the new tech because we are all standing in front of this imminent singularity. My biggest concern is that AI is now overestimated a bit. Therefore, most of us might not be able to figure out what is Sci-fi and what is realisable, which results in comparably high expectations of AI. Two suggestions for procurement: 
1. Go back to the real issues that we want to address and leave the technical part to the AI specialists; 
2. A tool-kit platform + professional service is far better than out-of-box offerings from the perspective of long-term ROI

What concerns me the most about AI isn't actually necessarily about AI itself, but about the fear of humans/businesses making decisions solely using AI as the 'source' or 'deciding factor' without vetting which variables went in to the data that AI was using in the first place.

Data Quality. Users of AI technology are seemingly oblivious, or at least less aware than they should be, that behind a good AI solution needs to be quality data. The focus on any AI solution as a product should reference back to the quality of the data and data products behind it as a visible 'flag' to support trust and accuracy on the result. Perhaps a slightly naive view when you consider the nature of AI in also 'doing this itself', but I would argue at least for now, at the beginning, a key foundational dimension of 'getting it right'.

Don't get me started about AI.
Security and breach issues, [probably] unrealistic data and search results... and the list goes on.
As many people have already stated, it can be easily weaponized without even the vendor knowledge.
AI is a very touchy and sensitive issue, it may seem like a long shot for some, but, at least to me, I consider it like the atomic bomb when it was first created. By that I mean, who knows where or when the AI-Hroshima/Nagasaki "bombing" will occur?
I will stop here...

People not knowing how to use to properly, resulting in data security issues, privacy issues, biased decision making, non-ethical usage without limits

Data that is used to direct users to the interests of the creator or company that controls the AI.

Alongside its benefits, AI brings several concerns that must be addressed. 

Ethical considerations around potential reinforcement of biases, privacy issues due to data misuse, job displacement due to automation, over-dependence that might diminish human skills, and opaque decision-making mechanisms are some of these challenges. 

While AI can transform various facets of society, these concerns necessitate prudent regulation and ethical guidelines. Ensuring transparency, promoting responsible use, and fostering a balanced human-AI coexistence is paramount as we continue to integrate AI more deeply into our lives. 

We must manage these concerns responsibly to realize AI's full potential.

Overall what concerns me most about AI, and call me old fashioned ;-), is the impact on human interaction. YES 1) it will be a great enabler to work more efficient, benefit from data, predictive analytics etc so the avoid that people are to become masters in Excel or other applications if AI can generate data 24/7 and when you need. 2) On the other side, I know I am not the only one, you already being bombarded with emails from companies that use bots and where it is clearly a one size fits all to poach for business, to invite you to webinars, to pass on offerings on tools and applications and it gets annoying as we seem to go back to cold calling and chasing you based on AI generated data from LinkedIn or others that you signed up for and where clearly your data has been sold to an AI tool to contact you. 3) for candidates the process can be automated and digitalized and a bot or AI can manage the entire process for you but you still want to sense the culture, values and ethics by meeting future colleagues and peers. The bot as well as AI can do the sell and drive the process, they can't sell you the WHY you should join the company. So, yes as an enabler to and to create efficiency but not to replace interaction and genuine interest. It is probably going in that direction but social and communication skills are equally important than analytics and data .

I am most concerned that we will not learn the proper management of good AI. We were late to recognise the potential of digital natives and to learn to manage them in a workforce of boomers and xers. In a similar manner, we are underestimating the potential and focusing on the negative aspects and risks without learning the skills needed to mitigate them.

My biggest concern related to artificial intelligence is data privacy and ethics. The question is to what extent these two aspects will be respected and taken into consideration for the benefit of everyone.

Increased complexity in architectural ecosystem design and management to efficiently adapt to new sources, data management requirements, and data engineering for model monitoring and continuous optimisation.

Lack of functional implementation of responsible AI framework in a way that support key organisational stakeholders alike. A comprehensive governance model is required to bridge the gap between the principles and practice by AI practitioners within organisations. I propose a layered approach in this regard, with useful recommendations that organisations should consider to achieve integration between the layers of the principles and practical AI processes execution. This
also naturally spotlight the responsibility of the organisational stakeholders and collaboration required, by providing a practical view of the different requirements of the actions and
leverage over AI systems, and decision-making horizons. The ultimate outcome of this design is a concrete governed entity of a socio-technical system that consists of people, organisational process, work systems, data, and related ecosystem institutions. I propose four layers- societal, industry, organisational, Internal AI system layers respectively.

This must all be measured via a Responsible AI scorecard covering the following elements:

1. AI system and design
2. Algorithms 
3. Data Management &amp; Operations 
4. Risk &amp; Impact
5. Transparency, Explainability &amp; Contestability 
6. Accountability &amp; Ownership 
7. Development &amp; Technology Operations 
8. Compliance

This then dovetails into an elevated report for various board subcommittees based on elements- these committee are often the IT &amp; Data committee for the technical elements, Social &amp; ethics committee and Audit &amp; Risk Committee for the risk &amp; impact, accountability, transparency and compliance issues.

Best U.S. Healthcare Payers’ Provider Data Management Applications Reviews 2024 | Gartner Peer Insights

HealthShare's brought together information the board has been a help to our group. Nonetheless, the absence of easy to understand detailing devices ruins its maximum capacity. I invest more energy grappling with reports than I'd like.

InterSystems HealthShare is a healthcare informatics platform for hospitals, integrated delivery networks and regional and national health information exchanges. it makes us to know about the healthcare sharing programs as an alternative to traditional health insurance. in every state there are different health share schemes so the person will check through the HealthShare and can take the product from it so it will help himself as well as his family for future.

HealthShare

InterSystems

Kyruus provides a vital need of provider management for healthcare organizations. The product is rigid with few customizations options but definitely fills a need.

U.S. Healthcare Payers’ Provider Data Management Applications Reviews and Ratings

What are U.S. Healthcare Payers’ Provider Data Management Applications?

Products In U.S. Healthcare Payers’ Provider Data Management Applications Market

HealthShare

HealthShare

Kyruus Connect

Kyruus Connect

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