Tag Archives: data silos

12 Issues Inhibiting the Internet of Things

While the Internet of Things (IoT) accounts for approximately 1.9 billion devices today, it is expected to be more than 9 billion devices by 2018—roughly equal to the number of smartphones, smart TVs, tablets, wearable computers and PCs combined. But, for the IoT to scale beyond early adopters, it must overcome specific challenges within three main categories: technology, privacy/security and measurement.

Following are 12 hurdles that are hampering the growth of the IoT:

1. Basic Infrastructure Immaturity

IoT technology is still being explored, and the required infrastructure must be developed before it can gain widespread adoption. This is a broad topic, but advancement is needed across the board in sensors themselves, sensor interfaces, sensor-specific micro controllers, data management, communication protocols and targeted application tools, platforms and interfaces. The cost of sensors, especially more sophisticated multi-media sensors, also needs to shrink for usage to expand into mid-market companies.

2. Few Standards

Connections between platforms are now only starting to emerge. (E.g., I want to turn my lights on when I walk in the house and turn down the temperature, turn on some music and lock all my doors – that’s four different ecosystems, from four different manufacturers.) Competing protocols will create demand for bridge devices. Some progress is emerging in the connected home with Apple and Google announcements, but the same must happen in the enterprise space.

3. Security Immaturity

Many products are built by smaller companies or leverage open source environments that do not have the resources or time to implement the proper security models. A recent study shows that 70% of consumer-oriented IoT devices are vulnerable to hacking. No IoT-specific security framework exists yet; however, the PCI Data Security Standard may find applicability with IoT, or the National Institute of Standards and Technology (NIST) Risk Management Guide for ITS may.

4. Physical Security Tampering

IoT endpoints are often physically accessible by the very people who would want to meddle with their results: customers interfering with their smart meter, for example, to reduce their energy bill or re-enable a terminated supply.

5. Privacy Pitfalls

Privacy risks will arise as data is collected and aggregated. The collation of multiple points of data can swiftly become personal information as events are reviewed in the context of location, time, recurrence, etc.

6. Data Islands

If you thought big data was big, you haven’t see anything yet. The real value of the IoT is when you overlay data from different things — but right now you can’t because devices are operating on different platforms (see #2). Consider that the connected house generates more than 200 megabytes of data a day, and that it’s all contained within data silos.

7. Information, but Not Insights

All the data processed will create information, eventually intelligence – but we aren’t there yet. Big data tools will be used to collect, store, analyze and distribute these large data sets to generate valuable insights, create new products and services, optimize scenarios and so on. Sensing data accurately and in timely ways is only half of the battle. Data needs to be funneled into existing back-end systems, fused with other data sources, analytics and mobile devices and made available to partners, customers and employees.

8. Power Consumption and Batteries

50 billion things are expected to be connected to the Internet by 2020 – how will all of it be powered? Battery life and consumption of energy to power sensors and actuators needs to be managed more effectively. Wireless protocols and technologies optimized for low data rates and low power consumption are important. Three categories of wireless networking technologies are either available or under development that are better suited for IoT, including personal area networks, longer-range sensors and mesh networks and application-specific networks.

9. New Platforms with New Languages and Technologies

Many companies lack the skills to capitalize on the IoT. IoT requires a loosely coupled, modular software environment based on application programming interfaces (APIs) to enable endpoint data collection and interaction. Emerging Web platforms using RESTful APIs can simplify programming, deliver event-driven processes in real time, provide a common set of patterns and abstractions and enable scale. New tools, search engines and APIs are emerging to facilitate rapid prototyping and development of IoT applications.

10. Enterprise Network Incompatibility

Many IoT devices aren’t manageable as part of the enterprise network infrastructure. Enterprise-class network management will need to extend into the IoT-connected endpoints to understand basic availability of the devices as well as manage software and security updates. While we don’t need the same level of management access as we do to more sophisticated servers, we do need basic, reliable ways to observe, manage and troubleshoot. Right now, we have to deal with manual and runaway software updates. Either there’s limited or no automated software updates or there are automatic updates with no way to stop them.

11. Device Overload

Another issue is scale. Enterprises are used to managing networks of hundreds or thousands of devices. The IoT has the potential to increase these numbers exponentially. So the ways we currently procure, monitor, manage and maintain will need to be revisited.

12. New Communications and Data Architectures

To preserve power consumption and drive down overall cost, IoT endpoints are often limited in storage, processing and communications capabilities. Endpoints that push raw data to the cloud allow for additional processing as well as richer analytics by aggregating data across several endpoints. In the cloud, a “context computer” can combine endpoint data with data from other services via APIs to smartly update, reconfigure and expand the capabilities of IoT devices.

The IoT will be a multi-trillion industry by 2020. But entrepreneurs need to clear the hurdles that threaten to keep the IoT from reaching its full potential.

This article was co-written with Daniel Eckert. The article draws on PwC’s 6th Annual Data IQ Survey. The article first appeared on LinkedIn.

Even More Tips For Building A Workers Compensation Medical Provider "A" Team

Fact
Significant dollars can be saved by getting injured workers to the best doctor. Evidence supporting this fact is the mounting Workers' Comp industry research clearly stating treatment by well-informed and well-intentioned medical doctors results in lower costs and better outcomes.

Belaboring A Point
As repeatedly stated in this series, many doctors in networks are not well-informed or well-intentioned regarding management of Workers' Comp claimants. As a consequence of their involvement, claim results are lacking, costs are high, and outcomes are precarious. This series of articles, “Tips for Building a WC Medical Provider A Team,” is intended to describe how to identify doctors who know the ropes in Workers' Comp using indicators in the data.1

Beyond the indicators discussed in the previous articles in this series, additional salient data elements are available in the data to broaden the scope of medical management evaluation. What makes this approach so feasible is that solid knowledge of who demonstrates best practices is revealed in the data. However, to find that knowledge, some operational processes and the data itself need refinement. Access to the data and its quality must be addressed.

Getting To The Knowledge In The Data
Regrettably, access to the data by the right persons is often a problem. Those who know best what to look for, the business and clinical professionals, cannot use current data in a practical, work-in-progress manner. The reasons are many.

First, relevant data resides in separate databases that must be integrated to understand all activity in a claim. Moreover, in most organizations, provider records are simply inaccurate and incomplete. Until now, the need for them was for reimbursement purposes only, not performance evaluation. Yet another problem is that provider records are frequently duplicated in the data, making it difficult to accurately evaluate individual medical providers' treatment process and results.

Data Silos
Critical data for analyzing medical provider performance is still fragmented in most payer organizations. While people have long complained about data silos in Workers' Comp, little has been done to correct the problem. If anything, data sources have increased. Pharmacy databases have been added, for instance. Yet the databases are not integrated on the claim level, thereby portraying the claim as a whole. Data silos too often lead those who are attempting to evaluate provider performance to rely on a single data source.

Single Source Analysis
Relying on one source of provider performance data is foolhardy. Nevertheless, bill review data is often used, but by itself is inadequate to tell the whole story. Claims level data is also critical to weigh return to work data, indemnity payments, and legal involvement associated with claims and ultimately, to individual doctors. None of these data items are found in bill review data, yet these are essential to complete analysis of provider performance. Because in Workers' Comp, doctors drive the non-medical claim costs as well as the direct medical costs, these data items are essential to evaluating the quality of their performance.

Data Quality
The problem of data quality can be even stickier. Traditionally, medical provider records are kept in the claims database, along with records of other vendors for payment purposes. All that is needed for bill payment is a name, address, and tax ID. Unfortunately, the same provider is frequently added to the database when a new bill is received. This outdated database management practice leads to slightly different records added for the same provider.

Data Optimization
To evaluate medical provider performance, more information about individual providers is needed such as accurate physical addresses. PO Boxes will suffice for mailing checks, but injured workers cannot be sent there for treatment.

Merge Duplicate Records
Tax ID's are still important for reimbursement and 1099 purposes, but often multiple doctors are represented by one Tax ID. To evaluate provider performance, individuals must be differentiated in the data. State medical license numbers and NPI (National Provider Identification) numbers are needed. Frankly, some doctors deliberately obfuscate the data by operating under multiple Tax ID's and multiple NPI numbers. Consequently, provider records must be merged, scrubbed, and optimized before any analysis can begin.

What To Do
For most organizations, choosing best practice providers by analyzing the data is challenged by the shortage of accurate and complete data. Therefore, those wanting to control costs by choosing the best providers should obtain provider performance analysis and scoring from a specialty third party, one that is expert in data integration from multiple sources, as well as provider data scrubbing and optimization.

When behaviors of doctors are analyzed using clean, integrated data, the well-informed and well-intentioned in Workers' Comp will rise to the surface.

1 Tips for Building a Medical Provider “A” Team and More Tips for Building a WC Medical Provider “A Team”