Instruction: create a high quality summary of the given input

Input:
Migration from London to satellite towns up 80% since 2012
 In the Kent seaside town of Whitstable, long-term residents call them DFLs – people who have moved “down from London”, sometimes for the lifestyle but more often for cheaper housing.

The number of people fleeing the capital to live elsewhere has hit a five-year high. In the year to June 2016, net outward migration from London reached 93,300 people – more than 80% higher than five years earlier, according to analysis of official statistics.

A common theme among the leavers’ destinations is significantly cheaper housing, according to the estate agent Savills, which analysed figures from the Office for National Statistics and the Land Registry.

Cambridge, Canterbury, Dartford and Bristol are reportedly among the most popular escape routes for people who have grown tired of London and its swollen property prices.

The most likely destination for people aged over 25 moving from Islington is St Albans in Hertfordshire, where the average home is £173,000 cheaper. People moving from Ealing to Slough – the most popular move from the west London borough – stand to save on average £241,000. Among all homeowners leaving London, the average house price was £580,000 while the average in the areas they moved to was £333,000.

The exodus is not just of homeowners, but of renters too. Rents in London have soared by a third in the last decade, compared to 18% in the south-west, 13% in the West Midlands and 11% in the north-west of England.

The only age group that has a positive net migration figure in the capital is those in their twenties, the research found. Everyone else, from teens to pensioners, is tending to get out. Since 2009, the trend has been steadily increasing among people in their thirties with 15,000 more people in that age bracket leaving every year than at the end of the last decade – a 27% rise.

The phenomenon is being driven by a widespread desire to “trade up the housing ladder”, something that is all too often impossible in London according to Lucian Cook, Savill’s head of residential research.

“Five years ago people would have been reluctant [to move out] because the economy wasn’t as strong and some owners didn’t want to miss out on house price growth [in London],” he said.

But with increased employment opportunities outside London and property prices levelling off in the capital, people are becoming more confident to make the leap, he said.

The trend is particularly affecting public servants, according to teaching and healthcare unions.

“Teachers who can find a job elsewhere move out,” said Martin Powell-Davies, London regional secretary of the National Union of Teachers. “There’s a high turnover of staff in London caused in part because they can’t afford to live. That creates instability for the pupils. They build up a relationship with their teacher and the next year that teacher will be a different person.”

Andy Winter, 38, a secondary school teacher in London has managed to stay in the capital because he started living on a canal boat four and a half years ago.

“My experience is that [housing] is completely unaffordable,” he said.

Winter balked at paying a £1,000-a-month mortgage and started living on the water. From September, he will pay £80 a month to moor his £10,500 boat near his new school in Hayes, west London.

Nurses, care workers and social workers are among those “being forced to live outside London and commute for longer” according to Christina Mcanea, assistant general secretary of the public service union Unison.

“The danger is London will start to lose these staff” she said. “Turnover rates in some parts of London are already 30%. People do it for a while and then the commute gets too difficult an experience.”

Unison has urged the government to allow the public sector to start repurposing redundant buildings as staff accommodation rather than selling them off for private housing.

However, despite the figures, London is not shrinking. The Savills research does not account for flows of migration abroad (a net increase of almost 100,000 between 2011 and 2015) and new births in the capital far outweighing deaths, that continue to push the population up towards 10m. It is expected to expand from its current 8.7m population to 9.8m by mid-2025.

‘It is nice to have somewhere we enjoy being in’

Ellie Russell, a former science teacher, and her husband, Colin Stratton, who works in IT, moved from Leytonstone to Southend-on-Sea last year with their eight-year-old daughter and two-year-old son. The couple had been looking to buy in east London, but could only find two-bedroom flats for around £300,000.

Ellie Russell, Colin Stratton and their two children moved to Southend. Photograph: The Guardian

“We were renting and I was pregnant with my son and we wanted a bigger place,” Russell said. “The rent was crazy to get a three-bedroom place and while we had a deposit and were looking to buy, prices went up about £60,000 as we were doing so. We could have got a two-bedroom flat if we had pushed the boat out.“

So they took the plunge to move to Southend in Essex and bought a c1900 three-bed house with a garden for £230,000.

“It was difficult as we didn’t know anyone in Southend,” she said. “But it is nice to have somewhere we enjoy being in. Now there seems to be quite a few people moving from London to Southend.”

Output: The number of people leaving London in search of cheaper housing in nearby areas has reached a five-year high, according to analysis of official statistics. In the year to June 2016, 93,300 people moved from London to live elsewhere, an increase of over 80% from five years before. Buyers and renters alike sought less expensive homes outside the capital, with many typically heading towards St Albans in Hertfordshire and Slough, with Bristol, Cambridge, Canterbury and Dartford also popular choices. The only age group showing net migration to London was those in their 20s.


Instruction: create a high quality summary of the given input

Input:
Redrow Three of Redrow Homes South Wales employees win NHBC awards
 Redrow Homes South Wales is celebrating after three employees were awarded a Pride in The Job Quality Award 2017 by the National House Building Council (NHBC).

Redrow Site Managers, John Harris, Rob Summers and Ross Robertson were all selected for the prestigious accolade from the NHBC which acknowledged the high level of quality and management on their respective sites at Mon Bank in Newport, Awel Y Garth in Pentyrch, and Manor View in Treharris.

Now in its 37th year ‘Pride in the Job’ celebrates the exceptional contribution site managers make in creating homes of outstanding quality, and this year more than 16,000 site managers across the UK competed for the awards.

Redrow South Wales’ winners were hand-picked for their excellent standards on the local sites following 12 months of rigorous judging.

Beverley Wookey, Sales Director at Redrow Homes South Wales, said:

“We are all incredibly proud that John, Rob and Ross have all been recognised for their hard work on the developments, which is a result of the pride and dedication shown by each of them and their teams, and everybody else in the business who puts in such hard work to ensure we build the best houses and create the best homes for our customers in the area.”

Redrow has achieved a record 27 awards at the Pride in the Job Quality Awards this year and not only has Redrow outdone its 2016 tally of 24, the company’s 2017 awards count is more than double the average in the competition, which stands at 12. The number of successful site managers at Redrow has increased every year since 2013.

Redrow’s long history of success at these awards makes it one of the UK’s leading house builders with 10 developments across the region.

Output: Redrow Homes South Wales has been celebrating three of its employees receiving a Pride in The Job Quality Award 2017 by the National House Building Council. Redrow Site Managers Rob Summers, John Harris and Ross Robertson all achieved recognition for their vital contributions to creating high-quality homes.


Instruction: create a high quality summary of the given input

Input:
Apple blog explores refining synthetic images to help AI to learn
 Most successful examples of neural nets today are trained with supervision. However, to achieve high accuracy, the training sets need to be large, diverse, and accurately annotated, which is costly. An alternative to labelling huge amounts of data is to use synthetic images from a simulator. This is cheap as there is no labeling cost, but the synthetic images may not be realistic enough, resulting in poor generalization on real test images. To help close this performance gap, we've developed a method for refining synthetic images to make them look more realistic. We show that training models on these refined images leads to significant improvements in accuracy on various machine learning tasks.

Overview

Training machine learning models on standard synthetic images is problematic as the images may not be realistic enough, leading the model to learn details present only in synthetic images and failing to generalize well on real images. One approach to bridge this gap between synthetic and real images would be to improve the simulator which is often expensive and difficult, and even the best rendering algorithm may still fail to model all the details present in the real images. This lack of realism may cause models to overfit to ‘unrealistic’ details in the synthetic images. Instead of modeling all the details in the simulator, could we learn them from data? To this end, we developed a method for refining synthetic images to make them look more realistic (Figure 1).

Figure 1. The task is to learn a model that improves the realism of synthetic images from a simulator using unlabeled real data, while preserving the annotation information.

The goal of 'improving realism' is to make the images look as realistic as possible to improve the test accuracy. This means we want to preserve annotation information for training of machine learning models. For example, the gaze direction in Figure 1 should be preserved, as well as not generate any artifacts, as machine learning models could learn to overfit to them. We learn a deep neural network, which we call 'refiner network', that processes synthetic images to improve their realism.

To learn such a refiner network, we need some real images. One option would be to require pairs of real and synthetic images with pixel-wise correspondence, or real images with annotations—e.g. the gaze information in the case of eyes. This is arguably an easier problem, but such data is very difficult to collect. To create pixel-wise correspondence, we need to either render a synthetic image that corresponds to a given real image, or capture a real image that matches a rendered synthetic image. Could we instead learn this mapping without pixel-wise correspondence, or any labels for the real images? If so, we could just generate a bunch of synthetic images, capture real images of eyes, and without labeling any real images at all, learn this mapping—making the method cheap and easy to apply in practice.

To learn our refiner network in an unsupervised manner, we utilize an auxiliary discriminator network that classifies the real and the refined (or fake) images into two classes. The refiner network tries to fool this discriminator network into thinking the refined images are the real ones. Both the networks train alternately, and training stops when the discriminator cannot distinguish the real images from the fake ones. The idea of using an adversarial discriminator network is similar to the GANs (Generative Adversarial Networks [1]) approach that maps a random vector to an image such that the generated image is indistinguishable from the real ones. Our goal is to train a refiner network—a generator—that maps a synthetic image to a realistic image. Figure 2 shows an overview of the method.

Figure 2. Our refiner neural network, R, minimizes a combination of local adversarial loss and a ‘self-regularization’ term. The adversarial loss ‘fools’ the discriminator network, D, that classifies an image as real or refined. The self-regularization term minimizes the image difference between the synthetic and the refined images. The refiner network and the discriminator network are updated alternately.

How do we preserve the annotations?

In addition to generating realistic images, the refiner network should preserve the annotation information of the simulator. For example, for gaze estimation the learned transformation should not change the gaze direction. This restriction is an essential ingredient to enable training a machine learning model that uses the refined images with the simulator’s annotations. To preserve the annotations of synthetic images, we complement the adversarial loss with a self-regularization L1 loss that penalizes large changes between the synthetic and refined images.

How do we prevent artifacts?

Making local changes

Another key requirement for the refiner network is that it should learn to model the real image characteristics without introducing any artifacts. When we train a single strong discriminator network, the refiner network tends to over-emphasize certain image features to fool the current discriminator network, leading to drifting and producing artifacts. A key observation is that any local patch sampled from the refined image should have similar statistics to a real image patch. Therefore, rather than defining a global discriminator network, we can define a discriminator network that classifies all local image patches separately (Figure 3). This division not only limits the receptive field, and hence the capacity of the discriminator network, but also provides many samples per image for learning the discriminator network. The refiner network is also improved by having multiple ‘realism loss’ values per image.

Figure 3. Illustration of local adversarial loss. The discriminator network outputs a width by height probability map. The adversarial loss function is the sum of the cross-entropy losses over the local patches.

Using Generator History to Improve the Discriminator

The generator can fool the discriminator either with samples from a novel distribution or the target (real data) distribution. Generating from a novel distribution fools the discriminator only until the discriminator learns that novel distribution. The more useful way the generator can fool the discriminator is by generating from the target distribution.

Given these two ways of evolving, the easiest is usually to generate a novel output, which is what we observed when training the current generator and discriminator against each other. A simplified illustration of this unproductive sequence is shown in the left-hand side of Figure 4. The generator and discriminator distributions are shown in yellow and blue respectively.

By introducing a history which stores generator samples from previous generations (right-hand of Figure 4), the discriminator is less likely to forget the part of the space about which it has already learned. The more powerful discriminator helps the generator to move toward the target distribution faster. The illustration is a simplification and neglects to show that the distributions are complex and often disconnected regions. In practice however, a simple random replacement buffer captures enough diversity from previous generator distributions to prevent repetition by strengthening the discriminator. Our notion is that any refined image generated by the refiner network at any time during the entire training procedure is really a ‘fake’ image for the discriminator. We found that by constructing a mini-batch for D with half the samples from the history buffer and the other half from the current generator's output (as shown in Figure 5) we can improve training.

Figure 4. An illustration of intuition behind using a history of images to improve the discriminator.

Figure 5. Illustration of mini-batch with history for D. Each mini-batch consists of images from the current iteration of the generator, as well as from a buffer of previous fake images.

How does the training progress?

We first train the refiner network with only self-regularization loss, and introduce the adversarial loss after the refiner network starts producing blurry versions of the input synthetic images. Figure 6 shows the output of refiner network at various steps of training. In the beginning, it outputs a blurry image that becomes more and more realistic as training progresses. Figure 7 visualizes the discriminator and generator losses at different training iterations. Note that the discriminator loss is low in the beginning—meaning it can easily tell the difference between real and refined. Slowly, the discriminator loss increases and the generator loss decreases as training progress, generating more real images.

Figure 6. Output of the refiner network as training progresses. Starting with a blurry image, the network learns to model the details present in real images.

Figure 7. Generator and discriminator losses as training progresses.

Is the self-regularization L1 loss limiting?

When the synthetic and real images have significant shift in the distribution, a pixel-wise L1 difference may be restrictive. In such cases, we can replace the identity map with an alternative feature transform, putting a self-regularizer on a feature space. These could be hand-tuned features, or learnt features such as a mid layer from VGGnet. For example, for color image refinement, the mean of RGB channels can generate realistic color images such as in Figure 8.

Figure 8. Example of self-regularization loss in feature space.

Are labels changed by the generator?

To verify that the labels do not change significantly, we manually drew ellipses on the synthetic and refined images, and computed the difference between their centers. In Figure 9, we show a scatter plot of 50 such center differences. The absolute difference between the estimated pupil center of synthetic and corresponding refined image is quite small: 1.1 +/- 0.8px (eye width = 55px).

Figure 9. Scatter plot of distances between pupil centers of synthetic and real images.

How to set the hyper-parameters? Tips & tricks.

Initialization of G

First we initialized G only with the self-regularization loss so that it can start producing blurry version of the synthetic input. Typically it took 500-2,000 steps of G (without training D).

Different steps of G and D for each training iteration

We used different number of steps for the generator and discriminator in each training iteration. For hand-pose estimation using depth, we used 2 steps of G for each D step, and for eye gaze estimation experiment we ended up using 50 steps of G for each D step. We found the discriminator to converge more quickly compared to the generator, partly because of the batch-norm in the discriminator. So, we fixed the #D steps to 1, and started varying #G steps starting from a small number, slowly increasing it depending on the discriminator loss values.

Learning rate and stopping criteria

We found it helpful to keep learning rate very small (~0.0001) and train for a long time. This approach worked probably because it keeps both the generator or the discriminator from making sudden shifts which would leave the other behind. We found it difficult to stop the training by visualizing the training loss. Instead, we saved training images as training progresses, and stopped training when the refined images looked visually similar to real images.

Qualitative Results

To evaluate the visual quality of the refined images, we designed a simple user study where subjects were asked to classify images as real or refined synthetic. The subjects found it very difficult to tell the difference between the real and refined images. In our aggregate analysis, 10 subjects chose the correct label 517 times out of 1000 trials, meaning they were not able to reliably distinguish real images from refined synthetic ones. In contrast, when testing on original synthetic images vs real images, we showed 10 real and 10 synthetic images per subject, and the subjects chose correctly 162 times out of 200 trials. In Figure 10 we show some example synthetic and corresponding refined images.

Figure 10. Example refined eye images using the proposed method.

Quantitative Results

Figure 11 shows the improvement using refined data, compared to training with original synthetic data. Two things to note from this figure: (1) Training with refined images is better than training with original synthetic images, and (2) using more synthetic data further improves performance. In Figure 12, we compare the gaze estimation error with other state-of-the-art methods, and show that improving the realism significantly helps the model generalize on real test data.

Figure 11. Comparison of training using synthetic and refined images for gaze estimation. Evaluated on real test images.

Figure 12. Comparison of different methods for gaze estimation on the MPIIGaze dataset. The first two methods are described in references [2] and [3].

Related Work

Recently there has been a lot of interest in domain adaptation using adversarial training. The image-to-image translation [4] paper by Isola et al. describe a method that learns to change an image from one domain to another, but it needs pixel-wise correspondences. The unpaired image-to-image translation paper [5] discussing relaxing the requirement of pixel-wise correspondence, and follows our strategy of using the generator history to improve the discriminator. The unsupervised image-to-image translation network [6] uses a combination of a GAN and variational auto-encoder to learn the mapping between source and target domains. Costa et al. [7] use ideas from our work to learn to generate images of the eye fundus. Sela et al. [8] use a similar self-regularization approach for facial geometry reconstruction. Lee et al. [9] learn to synthesize an image from key local patches using a discriminator on patches. For more details on the work we describe in this article, see our CVPR paper “Learning from Simulated and Unsupervised Images through Adversarial Training” [10].

References

[1] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative Adversarial Nets. Proceedings Neural Information Processing Systems Conference, 2014.

[2] X. Zhang, Y. Sugano, M. Fritz, and A. Bulling, Appearance-based Gaze Estimation in the Wild. Proceedings Computer Vision Pattern Recognition Conference, 2015.

[3] E. Wood, T. Baltrušaitis, L.-P. Morency, P. Robinson, and A. Bulling, Learning an Appearance-based Gaze Estimator from One Million Synthesised Images. Proceedings ACM Symposium on Eye Tracking Research & Applications, 2016.

[4] P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, Image-to-Image Translation with Conditional Adversarial Networks. ArXiv, 2016.

[5] J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. ArXiv, 2017.

[6] M.-Y. Liu, T. Breuel, and J. Kautz, Unsupervised Image-to-Image Translation Networks. ArXiv, 2017.

[7] P. Costa, A. Galdran, M. I. Meyer, M. D. Abràmoff, M. Niemeijer, A. M.Mendonça, and A. Campilho, Towards Adversarial Retinal Image Synthesis. ArXiv, 2017.

[8] M. Sela, E. Richardson, and R. Kimmel, Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation. ArXiv, 2017

[9] D. Lee, S.Yun, S. Choi, H. Yoo, M.-H. Yang, and S. Oh, Unsupervised Holistic Image Generation from Key Local Patches. ArXiv, 2017.

[10] A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, R. Webb, Learning from Simulated and Unsupervised Images through Adversarial Training. CVPR, 2017.

Output: Apple has published an official blog dedicated to machine learning, an unusual move for the company. The first post refers to work being done to enable the use of synthetic – as opposed to real – images to train image recognition neural nets. While synthetic images are cheaper to produce, they can lack realism, which hampers the image recognition software's ability to learn. Apple has therefore developed a way of refining synthetic images to make them more lifelike.


Instruction: create a high quality summary of the given input

Input:
The Price May Not Be Right: The Value of Comparison Shopping for Prescription Drugs
 ABSTRACT Objectives: To measure variations in drug prices across and within zip codes that may reveal simple strategies to improve patients’ access to prescribed medications.

Study Design: We compared drug prices at different types of pharmacies across and within local markets. In-store prices were compared with a Web-based service providing discount coupons for prescription medications. Prices were collected for 2 generic antibiotics because most patients have limited experience with them and are less likely to know the price ranges for them.

Methods: Drug prices were obtained via telephone from 528 pharmacies in Los Angeles (LA) County, California, from July to August 2014. Online prices were collected from GoodRx, a popular Web-based service that aggregates available discounts and directly negotiates with retail outlets.

Results: Drug prices found at independent pharmacies and by using discount coupons available online were lower on average than at grocery, big-box, or chain drug stores for 2 widely prescribed antibiotics. The lowest-price prescription was offered at a grocery, big-box, or chain drug store in 6% of zip codes within the LA County area. Drug prices varied dramatically within a zip code, however, and were less expensive in lower-income areas. The average price difference within a zip code was $52 for levofloxacin and $17 for azithromycin.

Conclusions: Price shopping for medications within a small geographic area can yield considerable cost savings for the uninsured and consumers in high-deductible health plans with high negotiated prices. Clinicians and patient advocates have an incentive to convey this information to patients to improve adherence to prescribed medicines and lower the financial burden of purchasing prescription drugs.Takeaway Points

The high costs of prescription drugs and lack of price transparency suggest that many patients might be overpaying for medications. We found that:

Drug prices were lowest at independent pharmacies and when using online coupons compared with grocery, big-box, or chain drug stores.

The average price difference within a zip code was $52 for levofloxacin and $17 for azithromycin.

Medications were less expensive when purchased in lower-income areas.

Clinicians and patient advocates have an incentive to convey this information to their patients or members to improve adherence to prescribed medicines and lower the financial burden of purchasing prescription drugs.

Consumers typically know the price of a product and have some information about its quality before purchasing. This information makes comparison shopping possible and is a key tenet of well-functioning markets. However, healthcare markets are different. Patients rarely know the price of a medical product or service before using it and sometimes even after the service is provided.

A lack of price transparency and difficulty in assessing its quality are the likely key reasons the price of medical care varies so widely. A primary care doctor visit in San Francisco ($251) is twice as expensive as in Miami ($95), and a lipid panel in Pittsburgh ($19) is one-fourth of the price in Indianapolis ($89), which is just a fraction of the cost in Dallas ($343). Even within the same market, New Yorkers seeking magnetic resonance imaging (MRI) can pay anywhere between $416 and $4527 for the same service.1

Although millions of Americans gained coverage through the Affordable Care Act (ACA), more than 32 million consumers remain uninsured and exposed to the full cost of services.2 Many of the uninsured are low-income consumers, who lack coverage as a result of states’ decisions not to expand Medicaid under the ACA. In addition, an increasing number of Americans are enrolled in health plans with high annual deductibles and face potentially high out-of-pocket costs. Four of 5 workers who now receive insurance through an employer pay a deductible, and 1 in 5 faces a deductible of $2000 or more.3 Plan members can purchase covered medications at a negotiated rate that might not vary much across in-network pharmacies; however, the plan’s negotiated price can be significantly greater than what consumers would pay using a store’s proprietary discount card or with online coupons. As such, both the uninsured and an increasing number of insured consumers have incentives to use price information and discount programs to comparison shop for their prescription medications.

Several states have passed laws and private-sector initiatives are underway to encourage or require greater price transparency for medical services.4 Recent work suggests that providing employees with price information is associated with lower total claims payments for common medical services.5 In this paper, we examined the potential benefit of comparison shopping for prescription drug prices and pharmacy characteristics that patients should consider when shopping for medications. Prescription drugs provide a good test case because, unlike most medical services, the quality of the product is constant across providers, making it easy for patients to comparison shop. Further, the market for prescription drugs is generally local: the majority of Americans live within 5 miles of a pharmacy and about 80% of the population uses only 1 pharmacy for outpatient prescriptions.6 We compared drug prices from different types of pharmacies (ie, chain drug stores, independent pharmacies, grocery stores, big-box stores) within local markets and described how they compare with the prices of Web-based services offering discount coupons for prescription medications. We measured the extent to which prices vary within a zip code and whether drug prices vary in high- versus low-income areas. The extent of price variation within a market is an implicit measure of the benefit of price shopping and may reveal simple strategies that health plans and providers can use to help patients access prescribed medications.

METHODS

We conducted a cross-sectional analysis of drug prices from July to August 2014 from all outpatient prescription drug outlets in a selected set of zip codes in Los Angeles (LA) County. We focused on the prices of 2 commonly prescribed generic antibiotics for community-acquired pneumonia: levofloxacin and azithromycin. They are relatively equally prescribed and can be used as substitutes for each other in most areas. We purposely selected medications used for an acute medical condition because patients typically have limited experience with them and are less likely to know the range of prices for these products. Further, the consequences of not filling medications for acute infections, such as pneumonia, may lead to sepsis, resulting in otherwise preventable hospitalizations, increased healthcare costs, and greater risk of mortality. The acute nature of their use and limited time to price shop after diagnosis may result in greater price variation than for chronic medications, although there is limited evidence on this point.7

We used the American Community Survey to obtain zip code—level measures of population and median household income for LA County. As low-density areas have few pharmacies, we restricted the study sample to zip codes with at least 10,000 residents. We ranked the 164 remaining zip codes by median household income and selected the top (high-income) and bottom (low-income) quartiles for inclusion.

We obtained a complete list of pharmacies operating in these 82 zip codes from the California Board of Pharmacy. We excluded hospital-based pharmacies and membership clubs (eg, Sam’s Club, Costco) and categorized outpatient pharmacies in the following way: chain drug store (eg, CVS), independent pharmacy, grocery store (eg, Safeway), or big-box store (eg, Target). Prices at these pharmacies were obtained via telephone. Three trained research assistants (RAs) called all pharmacies on the list over a 1-month period from July to August 2014. They followed a standardized script informing pharmacy staff that they were calling from LA County Hospital on behalf of a hypothetical uninsured patient with pneumonia.

They then requested quotes for the cash prices for the generic forms of 7 tablets of 500-mg levofloxacin and 6 tablets of 250-mg azithromycin. Once a price was given, they asked about any available discounts that could lower the price of the drug (ie, “discounted price.”) If there was no answer, the pharmacy staff was too busy to run a price check, or the RA was put on hold for more than 10 minutes, the RA would call back every hour with the same request until 5 pm that day and then resume hourly calls the next day at 8 am. Phone prices for both medications were recorded on a standardized data collection sheet.

Given the growth in internet use and online purchases of prescription drugs, we simultaneously collected prices for the 2 medications obtained at the same set of pharmacies from GoodRx, a popular Web-based service that aggregates available discounts and directly negotiates with retail outlets to provide consumers with coupons for discounted drug prices. Patients can enter a medication name and zip code and the website will list prices at most pharmacies operating in or near that zip code. To test the acceptance of the GoodRx coupons, we physically presented them at 5% of the pharmacies to ensure their prices would be honored. The study protocol was reviewed by the University of Southern California’s institutional review board prior to initiation. We examined the distribution of discounted drug prices by pharmacy type, as well as the extent of price variation in high- versus low-income areas, and then explored how prices varied across pharmacies in the same zip code. Given that most individuals purchase medications near home, examining prices within a zip code is an implicit measure of the potential cost savings from price shopping.

RESULTS

We obtained drug prices from 528 of 535 eligible pharmacies (98.7% response rate). The sample consisted of 170 chain drug stores, 49 grocery stores, 39 big-box stores, and 270 independent pharmacies. Independent pharmacies accounted for a larger proportion of the total number of pharmacies in low-income zip codes (65%) than in high-income areas (37%).

Table 1 shows the variation in drug prices by pharmacy type, categorized as chain, independent, grocery store, big-box, and online (GoodRx). This captures variation in prices both across and within zip codes and reflects the discounted price. The average price of generic levofloxacin purchased with a GoodRx coupon or at an independent pharmacy was less than half the price versus a grocery or big-box store and less than one-fourth of the discounted price obtained over the phone at chain drug stores. Although prices were highest at chain drug stores, there was far less variation in price at this type of location (interquartile ratio [IQR], 1.05) compared with the other retail outlets (IQRs, ~2.0).

Prices varied less for azithromycin than levofloxacin, but relative prices followed a similar pattern. The lowest average prices were found via GoodRx ($20) and at independent pharmacies ($23); chain drug stores charged the most ($37). There was little variation in price for chain drug stores (IQR, 1.05), particularly in comparison with independent pharmacies (IQR, 2.47) and grocery stores (IQR, 4.30). Asking for a discount had the largest effect at chain drug stores in the case of levofloxacin (lowering the average price by $11, or 10%) and at grocery stores in the case of azithromycin (by $8, or about 25%).

Table 2 shows the distribution of drug prices in high- and low-socioeconomic status (SES) areas, as defined by the median household income in the zip code. Levofloxacin and azithromycin were less expensive, on average, when purchased in pharmacies located in lower-income zip codes compared with higher-income zip codes. Because high- and low-income areas are likely to have a different mix of pharmacies (eg, big-box stores may be less likely to locate in low-income areas), we also estimated the average prices controlling for pharmacy type and the results were substantively unchanged. Although the median price of levofloxacin was $26 in low-income areas, prices ranged from a low of $4 to a high of $149, depending on the pharmacy. In high-income areas, the median price of levofloxacin was $80 and ranged from $5 to $229.

Table 3 shows the extent of price variation within the same zip code, which is the implicit value of price shopping within a localized area. The average price difference between the highest and lowest-cost pharmacies in a zip code was greater than $100 for levofloxacin and $30 for azithromycin. Perhaps a more salient comparison is the average price difference between a randomly selected pharmacy and the lowest-priced pharmacy in the same zip code. In this case, consumers would save an average $52 per prescription for levofloxacin and $17 per prescription for azithromycin simply by comparison shopping within the same zip code (results not shown). We observed modestly greater price variation in high-income areas despite there being more pharmacies per zip code in low-income areas (7.7 pharmacies per zip code vs 6.5 in high-income zips).

Table 4 highlights general approaches for obtaining the lowest priced medication in an area. In more than half of the 71 unique zip codes in the study sample, independent pharmacies had the lowest price for levofloxacin (53%), followed by GoodRx (44%). In only 2 of 71 zip codes did a chain or big-box store have the lowest price for levofloxacin. We observed the same pattern when we restricted the analysis to the 39 zip codes with a big-box store. For both levofloxacin and azithromycin, the lowest price prescription was offered at a grocery, big-box, or chain drug store in just 6% of zip codes.

DISCUSSION

In our study sample of 528 pharmacies, prices found at independent pharmacies and by using online discount coupons were markedly lower, on average, than at grocery, big-box, or chain drug stores for 2 widely prescribed antibiotics. Drug prices varied dramatically within a zip code and typically were less expensive when purchased in lower-income areas. The average price difference within a zip code was $52 for levofloxacin and $17 for azithromycin, which suggests that price shopping within a small geographic area can yield considerable cost savings, particularly for uninsured and insured consumers in high-deductible health plans with high negotiated prices. A possible explanation for the greater price variation with levofloxacin is that it recently became available as a generic, so there has been less time to establish a fair market value.

There is a common perception that chain drug stores have lower prices than independent pharmacies due to economies of scale and the fact that the chains derive a smaller fraction of their revenue from the sale of prescription drugs. However, chain drug stores typically compete less on price and more on convenience, brand name, and nondrug items.8 By contrast, independent pharmacies compete largely on price and service to induce consumers to bypass chain drug stores.9 Our results suggest that cash-paying consumers often face a premium for going to chain drug stores and could save substantially by using online coupons or purchasing their medications at independent pharmacies in the same or neighboring zip codes.

Although poor adherence is endemic, it is particularly problematic for individuals of lower SES.10 An estimated 20% to 35% of patients are primary nonadherent by failing to fill an initial prescription, and an additional 20% discontinue therapy after filling the first prescription.11-13 In 2012, 22% of uninsured adults aged 18 to 64 years reported not getting needed prescription drugs due to cost compared with 5% of adults who were insured for the whole year.14 Noncompliance with antibiotics for respiratory infections can result in treatment failure, worsening severity of disease, sepsis, antibiotic resistance, and increased risk of hospitalization.

A critical question is the extent to which consumers would use price information in purchasing medical services. The highly touted movement toward “consumer-directed healthcare” relies on patients having easy access to information concerning drug prices and quality. A recent survey indicates that a majority of Americans have tried to find out how much they would have to pay out of pocket—not including a co-pay—before getting care. However, the survey also reveals that most Americans are not aware that prices can vary across healthcare providers.15

Our results differ from those of a Florida study by Gellad et al that obtained drug prices for 3 chronic medications (esomeprazole, fluticasone, and clopidogrel) and a generic antibiotic (azithromycin).7 They found that mean drug prices were 9% higher in the poorest zip codes and that independent pharmacies in the poorest areas charged the highest mean prices. We, however, found the opposite: lower prices at independent and online pharmacies and pharmacies located in low-income areas. A possible explanation for the differences across studies is that the Florida study obtained drug prices from a website whereas we collected prices by calling individual pharmacies. We also asked for any available discounts and verified concordance with in-store prices in a pilot study. The Florida study also restricted the sample to pharmacies that filled 1 of the 4 drugs to a Medicaid beneficiary in a single month (November 2006). This may have resulted in a nonrepresentative sample of pharmacies across income areas. By contrast, we collected price data over the phone from all available pharmacies. We focused on price variation for antibiotics under the assumption that consumers would have limited experience purchasing them (and thus would be less aware of price) and the consequences of not filling the prescription due to cost would have a more immediate impact on health.

Limitations

There are several limitations to our study, the most prominent of which is that we only measured prices of 2 medications for an acute condition in a single county in California (LA County). We do not know if the findings will hold in other regions or states. However, LA is an economically and culturally diverse county with a broad array of income levels and population densities. The magnitude of price variation across outlets and the savings associated with online coupons at nationwide chains suggest that we could expect similar results in other areas of the country. The extent of price variation may be lower for chronic medications, but these by their nature (length of time taken) may impose a larger financial burden on patients. Future research should focus on comparing prices across a broader spectrum of pharmaceuticals, including medications for chronic diseases.

Another limitation of our study is that we obtained drug prices via telephone rather than in person, and pharmacies may offer discounts in the store that they are unable or unwilling to provide over the phone. Also, the calls to the pharmacy were made from a doctor’s office on behalf of a hypothetical uninsured patient, and the callers asked each pharmacy for any potential discounts after an original price was provided. Over 98% of pharmacies in our sample provided prices over the phone. Patients calling on their own behalf may not receive the same discounts we received. Additionally, we only called pharmacies in the highest and lowest quartiles of median income. It is possible that we might have a better understanding of price variation if we had contacted all pharmacies regardless of income level.

Finally, we used a single website to represent discounts available online. Nonetheless, GoodRx is the largest price aggregator and coupon tool used by thousands of doctors and millions of patients every month. Further, 100% of GoodRx coupons were honored when physically presented at the pharmacy during this study.

CONCLUSIONS

Slowing the growth of healthcare costs underscores nearly every health policy initiative in the United States and is the motivation for public and private efforts to increase price transparency in healthcare markets. Price transparency initiatives face considerable obstacles, however; most prominently, how to reliably measure and convey information about quality and price for thousands of complex medical services produced by a wide array of providers and organizations. The task is less daunting for prescription drugs because quality is fixed.

The extent of price variation found in this study suggests that consumers could readily benefit from greater price transparency. If this information were widely available to consumers, large variations in drug prices across pharmacies would likely be reduced.

Acknowledgments

The authors would like to thank Kaleigh Barnes, Brian Raffetto, Melissa Luttio, Janice Rivelle, and Erin Higginbotham for helping to collect and analyze the data.Author Affiliations: Keck School of Medicine, (SA, ST); Sol Price School of Public Policy (NS); and School of Pharmacy (GJ), Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, CA.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (SA, GFJ, NS, ST); acquisition of data (NS, ST); analysis and interpretation of data (SA, NS, ST); drafting of the manuscript (SA, GFJ, SN, ST); critical revision of the manuscript for important intellectual content (SA, GFJ, NS, ST); statistical analysis (SN); administrative, technical, or logistic support (ST); and supervision (SA, ST).

Address Correspondence to: Geoffrey F. Joyce, PhD, Leonard D. Schaeffer Center for Health Policy and Economics and School of Pharmacy, University of Southern California, 635 Downey Way, Los Angeles, CA 90089-3333. E-mail: gjoyce@usc.edu. REFERENCES

1. Costliest cities 2015: pricing for the same medical services is all over themap (literally). Castlight health website. www.castlighthealth.com/costliest-cities/. Accessed September 27, 2015.

2. Garfield R, Majerol M, Damico A, Foutz J. The uninsured: a primer—key facts about the health insurance and the uninsured in the era of health reform. Kaiser Family Foundation website. http://files.kff.org/attachment/primer-the-uninsured-a-primer-key-facts-about-health-insurance-and-the-uninsured-in-the-era-of-health-reform. Published November 2015. Accessed January 7, 2016.

3. 2015 Employer Health Benefits Survey. Kaiser Family Foundation website. www.kff.org/report-section/ehbs-2015-section-one-cost-of-health-insurance/. Published September 22, 2015. Accessed September 27, 2015.

4. Kullgren, JT, Duey, KA, Werner, RM. A census of state health care price transparency websites. JAMA. 2013;309(23):2437-2438. doi: 10.1001/jama.2013.6557.

5. Whaley C, Schneider Chafen J, Pinkard S, et al. Availability of health service prices and payments for these services. JAMA. 2014;312(16):1670-1676. doi: 10.1001/jama.2014.13373.

6. Look KA. Patient characteristics associated with multiple pharmacy use in the U.S. population: findings from the Medical Expenditure Panel Survey. Res Social Adm Pharm. 2015;11(4):507-516. doi: 10.1016/j.sapharm.2014.10.004.

7. Gellad WF, Choudhry NK, Friedberg MW, Brookhart MA, Haas JS, Shrank WH. Variation in drug prices at pharmacies: are prices higher in poorer areas? Health Serv Res. 2009;44(2, pt 1):606-617. doi: 10.1111/j.1475-6773.2008.00917.x.

8. Four big trends shifting the business of retail pharmacy. McKesson website. http://www.mckesson.com/blog/retail-pharmacy-trends-to-watch/. Published March 14, 2016. Accessed June 2017.

9. Leger M. Three ways small drug stores can beat big-box competitors. The Globe and Mail website. https://www.theglobeandmail.com/report-on-business/small-business/sb-marketing/three-ways-boutique-drug-stores-stay-competitive/article12468461/. Published June 14, 2013. Accessed June 2017.

10. Goldman DP, Smith JP. Can patient self-management help explain the SES health gradient? Proc Natl Acad Sci USA. 2002;99(16):10929-10934.

11. Liberman JN, Hutchins DS, Popiel RG, Patel MH, Jan SA, Berger JE. Determinants of primary nonadherence in asthma-controller and dyslipidemia pharmacotherapy. Am J Pharm Benefits. 2010;2(2):111-118.

12. Shin J, McCombs JS, Sanchez RJ, Udall M, Deminski MC, Cheetham TC. Primary non-adherence to medications in an integrated healthcare setting. Am J Manag Care. 2012;18(8):426-434.

13. Evans CD, Eurich DT, Remillard AJ, Shevchuk YM, Blackburn D. First fill medication discontinuations and nonadherence to antihypertensive therapy: an observational study. Am J Hypertens. 2012;25(2):195-203. doi: 10.1038/ajh.2011.198.

14. Health, United States, 2013: with special feature on prescription drugs. National Center for Health Statistics website. https://www.cdc.gov/nchs/data/hus/hus13.pdf. Published May 2014. Accessed June 2017.

15. Schleifer D, Hagelskamp, C, Rinehart, C. How much will it cost? how Americans use prices in health care. Public Agenda website. https://www.publicagenda.org/files/HowMuchWillItCost_PublicAgenda_2015.pdf. Published March 9, 2015. Accessed June 2017.

Output: Price shopping for medications within a small geographic area can yield considerable cost savings for uninsured and insured consumers in high-deductible health plans.


Instruction: create a high quality summary of the given input

Input:
Shining a light on prescription drug pricing
 After years of failed efforts, the California Legislature may finally pass a bill that responds to the problem of rising prescription drug costs. But temper your enthusiasm: Though this measure (SB 17) has been fiercely resisted by the pharmaceutical industry, it wouldn’t actually stop manufacturers from raising their prices as high as they think the market will bear. It would just make them reveal more about the cost and value of their drugs as they do so.

Some lawmakers and consumer advocates would like to go further, capping drug prices in some fashion. But efforts to control drug prices through legislation or the ballot have failed, in part because of heavy industry opposition and in part because of concerns that price caps might lead to drug shortages or other unintended consequences. The state also could be on shaky ground in federal court if it took steps that impeded interstate drug sales.

In short, the Legislature’s options are limited. Worse, it’s hard for policymakers to find lesser measures that might be helpful because of the cloud of mystery surrounding how pharmaceutical companies price their products. Although publicly traded companies report broadly on their expenses and revenues, those numbers don’t provide much explanation for specific pricing decisions, such as why an EpiPen that sold for $104 in 2013 was priced at $225 two years later. The companies’ typical explanation is that every successful drug has to cover the cost of developing many other drugs that don’t make it to market.

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Even after their patents expire, drug makers sometimes face so little competition that there’s nothing to stop them from jacking up prices exponentially.

That’s where SB 17 by Sen. Ed Hernandez (D-West Covina) comes in. It would gather data from insurers about which drugs are the most commonly prescribed, which are the most costly and which saw the largest increase in total sales, as well as what percentage of premiums and premium increases were attributable to prescription medications. So much for the non-controversial portion of the bill. More contentiously, it would require drug makers to give notice weeks in advance of a major price hike. It would also compel manufacturers to lay out the rationale for such increases, along with documentation of any improvement in “clinical efficacy” that their drugs offer over alternative treatments. The same disclosures would be required for newly approved specialty drugs.

The drug companies’ biggest fear seems to be that the measure is a stalking horse for price caps, which they say would dry up the investment they rely on to fund R&D. But that’s a wholly separate debate, and one that should be informed by the kind of industry data that SB 17 aims to collect.

Nor can drugmakers argue with a straight face (although some do) that the Legislature should simply trust competition and market forces to protect consumers. Not to put too fine a point on it, but pharmaceutical companies enjoy goverment-awarded monopolies on their products that last for a decade or more, and that they often manage to extend by tweaking their formulations. And even after their patents expire, drug makers sometimes face so little competition that there’s nothing to stop them from jacking up prices exponentially (see, e.g., the exploits of “pharma bro” Martin Shkreli). On top of that, consumers aren’t trained to diagnose their own ailments and prescribe the right pills — they rely on medical professionals to make those choices for them. That’s why pharmaceutical companies spend so much time and money marketing their wares to doctors and clinicians.

Drug makers insist that their products are being demonized in the debate over healthcare costs, and that their prices aren’t as high as they appear because of the rebates and discounts obtained by pharmacy benefit managers such as Express Scripts — powerful middlemen that process claims for prescription-drug benefits and negotiate with drug makers on behalf of insurers and large employers. They complain that Hernandez’s bill doesn’t account adequately for the role of these middlemen.

There are a host of questions surrounding pharmacy benefit managers, which critics say pocket some portion of the discounts they negotiate rather than passing them along to their customers. And a bill is moving through the Legislature (AB 315) to offer a bit of needed clarity by requiring these companies to disclose to their clients the discounts they obtain, while also reporting aggregate information to the state about the prices they pay for drugs.

If the industry is worried about SB 17 painting a misleading and prejudicial picture about drug prices, it should be working with Hernandez to generate better data for state policymakers, not trying to stymie the push for transparency. The fact that pricing is complicated is all the more reason to develop a clearer picture of the factors involved. It’s not as if this issue is going away — as drug prices continue to rise along with healthcare spending in general, so will the pressure on lawmakers to ease the pain.

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Output: After years of failed efforts, the California Legislature may finally pass a bill that responds to the problem of rising prescription drug costs. But temper your enthusiasm: Though this measure (SB 17) has been fiercely resisted by the pharmaceutical industry, it wouldn’t actually stop manufacturers from raising their prices as high as they think the market will bear.


Instruction: create a high quality summary of the given input

Input:
California prepares to fight GOP attempts to undermine Obamacare
 Hell hath no fury like a president scorned. And with the embarrassing collapse of the GOP-controlled Senate’s plan to repeal and replace the Affordable Care Act, many health care experts predict that Donald Trump and his administration will do whatever it takes to “let Obamacare fail,” as the president put it last week.

But the people who run the state’s Obamacare insurance marketplace say they have their own plans in place to make sure hundreds of thousands of Californians don’t lose their health coverage.

“We have pieced together good ideas that have come from health plans and advocates — four or five elements, all about protecting consumers,’’ said Peter Lee, Covered California’s executive director.

Motivating the effort is this stark reality: Even without Congress’ assent, Trump has a variety of options to obstruct or even undermine the Affordable Care Act.

Among the strategies is a fallback plan in case the Trump administration eliminates the subsidies that help low-income Obamacare enrollees pay for out-of-pocket medical expenses. And should Trump use his executive powers to weaken the enforcement of a critical part of the law — the individual mandate requiring people to have health insurance or pay a steep tax penalty — California could impose its own.

Finally, if Trump curtails the advertising surrounding the upcoming 2018 enrollment season, as his administration did last winter to drive down the number of sign-ups, Covered California can rely on its own robust budget for marketing and outreach efforts.

“Most other states don’t have the ability to do what we do,’’ Lee said last week by phone from Washington, D.C., where he was meeting with congressional staff and administration officials as well as leaders of other state-based marketplaces to talk up California’s successes with Obamacare, and discussing ideas about how to stabilize the insurance market in the near term.

Unlike other state-based exchanges in the country, Lee said, California’s has greater leeway to shape the market and set the rules of the road for health plans — a result of a bipartisan effort by the Democratic-controlled Legislature and former Republican Gov. Arnold Schwarzenegger.

Health care experts say Covered California’s ability to maintain an array of health plans leads to lower premiums and lower annual rate hikes compared with states that depend on the federally operated exchange.

The Golden State also has managed to keep rates relatively stable because California’s large population helps attract more insurers, which makes the marketplace more competitive. And the exchange aggressively negotiates directly with insurers to get the best rates, networks and benefits.

Since Obamacare kicked into gear in January 2014, the law has slashed the rate of uninsured Californians by more than half. Those numbers were boosted by the 3.8 million residents who took advantage of a provision that allows adults without dependent children to enroll in Medicaid, the low-income health program that the Golden State calls Medi-Cal.

While he could impose work requirements on Medicaid recipients, as long as Republicans don’t pass a replacement health care bill, there’s little Trump can do to undermine the half-century-old Medi-Cal program. So Lee’s biggest priority will be to save the private plans on the health insurance exchange that now enroll 1.4 million Californians — most of whom are in highly subsidized plans.

Obamacare is primarily funded by the federal government by spending reductions in Medicare, tax hikes on the wealthy and taxes on drug and medical equipment makers.

The law’s generous subsidies are available to any legal California resident earning between 139 percent and 400 percent of the federal poverty level. But anyone who earns between 139 percent and 250 percent of the poverty threshold — between $34,200 and $61,500 for a family of four — also is eligible for additional subsidies called “cost-sharing reductions,” which lower out-of-pocket costs such as co-pays and deductibles.

About 7 million Americans, including roughly 700,000 Californians, receive those extra subsidies. And the federal government reimburses insurers on the exchanges about $7 billion to reduce the cost of the co-pays and deductibles for low-income people.

In 2014, however, House Republicans sued the Obama administration, saying that Congress never appropriated funding to give insurers that money. A federal judge ruled in the Republicans’ favor and ordered that payments be stopped. But the ruling was appealed by the administration, and the payments were allowed to continue pending the appeal.

Not only would a decision by Trump to end to those subsidies send many premiums soaring because health insurers would have to pick up those costs themselves, many companies would also likely flee the markets, said Amy Adams, a senior program officer at the Oakland-based California Health Care Foundation.

In anticipation of such a move, Covered California has devised a work-around.

For 2018, the exchange will allow health insurance companies to artificially inflate their popular “Silver” plan rates to cover the cost of cost-sharing reductions.

But consumers enrolled in moderately priced “Silver” plans whose rates are inflated should not be adversely impacted because their monthly subsidy — paid by the federal government — also would increase, Lee said.

And Covered California officials also said that exchange enrollees with unsubsidized Silver-tier coverage will have an option to purchase comparable Silver-tier coverage off-exchange without facing a steep increase in their price, even if the cost-sharing reductions are killed.

“It’s a creative solution and a good example of where California is trying to get out ahead of the curve,” Adams said.

Still, the uncertainty over whether or not the federal government will continue the cost-sharing payments to low-income people has led Covered California to ask all of its plans to submit two sets of 2018 rates, which will be made public on Aug. 1. One set assumes the subsidies will be removed; the other does not.

“I want to be really clear: This is not what we want to happen. It will confuse the consumer,’’ Lee said. “But we will be doing mountains of direct outreach and education with consumers through emails, voicemails and the (insurance) agent community’’ to make it work, he said.

Meanwhile, if Trump decides not to enforce the tax penalty for not having insurance, experts predict more people would likely go without coverage — and that would shrink the insurance pool, leaving older and sicker people in the pool, causing premiums to skyrocket.

How could the state fight that decision?

One little-known part of the Affordable Care Act allows states to pass an individual mandate, enacting their own tax penalty for not having health insurance, said Robert Laszewski, a Virginia-based health policy expert.

Lee said Massachusetts, which passed a health care law similar to Obamacare in 2006, has a such a mandate and “it can work.” But, he noted, the decision is the prerogative of the Legislature — and the new law would require a two-thirds vote.

Anthony Wright, executive director of the advocacy group Health Access California, said to have any effect on Covered California’s open enrollment later this year, that vote would have to happen before September — when the current legislative session ends.

“Frankly, the big driver of enrollment is the subsidies, not the penalty,’’ Wright said.

“People want coverage — and the tax credits are crucial in helping people afford it,” he said. “The Affordable Care Act uses a carrot and stick approach to encourage coverage. But the carrots are more important than the sticks.’’

Output: Hell hath no fury like a president scorned. And with the embarrassing collapse of the GOP-controlled Senate’s plan to repeal and replace the Affordable Care Act, many health care experts predict that Donald Trump and his administration will do whatever it takes to “let Obamacare fail,” as the president put it last week.


Instruction: create a high quality summary of the given input

Input:
House floats bill to incentivize mental health providers to buy EHRs
 A bill introduced last week by Reps. Lynn Jenkins, R-Kansas, and Doris Matsui, D-California, would promote incentive payments for behavioral health providers who purchase and adopt electronic health records.

The proposed bill would apply to community mental health centers, public or private psychiatric hospitals, accredited residential or outpatient mental health treatment facilities, accredited substance abuse treatment sites, clinical psychologists and clinical social workers.

The bipartisan legislation is part of the Centers for Medicare and Medicaid Services Innovation Center project to promote not only the adoption of certified EHR systems, but to use the technology to improve the coordination and quality of care through health information exchange.

While meaningful use has been successful in increasing EHR adoption, the representatives said that behavioral health providers were excluded from the program and lack the funds to implement full EHRs.

The proposed project would allow congress to avoid expanding meaningful use to cover mental health providers.

“This is another step forward in our work to put mental health on a level playing field with physical health care,” said Matsui in a statement. “By encouraging the use of EHR technology by behavioral health providers, we can improve care coordination and behavioral health integration. That helps ensure patients receive the treatment they need in the right place at the right time.”

The CMS Innovation Center has been considering potential payment and service delivery models focused on behavioral health, as well as telemedicine. CMS will hold a Behavioral Health Payment and Care Delivery Innovation Summit to address workforce challenges in the mental health field and reimbursement for the use of telehealth.

Twitter: @JessieFDavis

Email the writer: jessica.davis@himssmedia.com

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Output: A bill introduced last week by Reps. Lynn Jenkins, R-Kansas, and Doris Matsui, D-California, would promote incentive payments for behavioral health providers who purchase and adopt electronic health records.


Instruction: create a high quality summary of the given input

Input:
Mayor of London unveils vision of night-time economy
 A 24-Hour Vision for London’s Night Time Economy

Licensing

The Mayor of London has today announced the first ever 24-hour vision for London’s Night Time Economy.

London’s night time economy is worth £26bn and generates one in eight jobs in the capital. This new vision recognises the importance of the night time economy and sets out 10 guiding principles to support the building of a night-time culture which serves the needs of all those living in and visiting London.

The Vision will be realised through the Mayor’s Night Time Commission, which is chaired by Cornerstone Barristers’ Head of Chambers Philip Kolvin QC. Philip will also be working together with the Night Czar Amy Lamé.

The 10 principles in the Vision have been developed with insight from businesses, Night Time Economy Borough Champions, the police and other night time industry stakeholders. They are that London’s night time economy will:

Be a global leader; Provide vibrant opportunities for all Londoners, regardless of age, disability, gender, gender identity, race, religion, sexual orientation or means; Promote all forms of cultural, leisure, retail and service activity; Promote the safety and well-being of residents, workers and visitors; Promote welcoming and accessible nightlife; Promote and protect investment, activity and entrepreneurship; Promote domestic and international visits to London; Be strategically located across London to promote opportunity and minimise impact; Become a 24-hour city that supports flexible lifestyles; Take account of future global and domestic trends in leisure, migration, technology, employment and economics.

Philip Kolvin QC comments: “London can only become a truly 24-hour city if we can bring everyone across the industry, the boroughs, police, transport and health to work together. This vision gives us the guiding principles we need to ensure decisions around planning, licensing and building for the future make the night-time economy a priority. With the multi-talented Night Time Commission, we can bring the whole city’s resources together to tackle the challenges we face and to maximise every opportunity for London to become the world-leading night-time economy.”

The Night Time Commission, which includes planners, licensing experts, local authorities, venue owners, artists, the police, and cultural organisations, will develop policy and programme recommendations for the Mayor over the coming year.

Click here to view the 24-Hour Vision in full.

Output: Mayor of London, Sadiq Khan, has revealed plans for London's night-time economy. Worth £26bn and responsible for one in eight jobs in the city, the night-time economy's importance is to be realised through 10 guiding principles that will help build and support it. These principles have been put together by local businesses, the police, industry stakeholders and the Night Time Economy Borough Champions, and include aims to "promote a welcoming and accessible nightlife" and creating a "24-hour city that supports flexible lifestyles", among other goals.


Instruction: create a high quality summary of the given input

Input:
How CMS can help hospitals succeed in providing sustainable pop health initiatives
 I think it’s fair to say that many of us who have been immersed in the EHR revolution of the past decade on a day-to-day basis most likely never expected to see the electronic health records systems functioning as population health platforms — for many reasons.

That said, EMR vendors seem to be suggesting that if you wait, they will build it, and so have apparently convinced some health care providers and systems to actually do just that — hold off until their EHR vendor adds population health to its existing implementations. But the wait for these holdouts may not be worth it in terms of lost opportunities to leverage evolving payer-provider-employer population health collaborations, incentives, shared savings, quality improvements and better resource optimization.

The wait, in fact, could be sadly reminiscent of Vladimir’s and Estragon’s painstaking sojourn waiting for Godot.

Having participated in numerous population health program designs and rollouts, which in some cases are very cleverly designed extensions of investments made in EHRs, over the past four years, it has become increasingly apparent that the sustainability and even the survival of population health as a strategy for managing sick, not so sick, and healthy lives coupled with evolving best practices and the inclusion of accessible social-determinants-of-health [to include mental (MH) and behavioral health (BH) factors] is just as critical for qualitative and quantitative outcomes today as it will be tomorrow.

Indeed, health care providers can and are fine-tuning new business models with the more enlightened among them designing collaborative value-based population health management programs with the payers at the table, including the biggest of them all: CMS.

The Centers for Medicare and Medicaid Services, in fact, currently has pop health initiatives underway that hospitals should understand. Let’s take a look.

New data sets enhance pop health effectiveness

These models are literally a work-in-progress and definitely dependent on the availability and condition of data perceived to be the most useful, actionable and beneficial in delivering on the promise of pop health. Of the sources of data being vetted for inclusion into more intelligent screening, stratification and outreach programs, data collected and maintained by many state public health agencies are now being adapted to fill in numerous gaps in what clinical-only (EHR) data cannot provide about the patient, member or person’s physical environment and identified pockets of known areas of deprivation, poverty, illiteracy, drug/alcohol/opiod addictions, air and water quality issues, elevated mortality/morbidity rates, vaccination compliance, mental health disorders, safety and crime, repeat ED utilization, and seasonal outbreak forecasts and warnings in keeping with their role of providing traditional CDC-like services.

CMS is beginning to support these efforts with the newly released Accountable Health Communities Screening Tool (2017) intended to provide standardized screening for health-related social needs in clinical settings to address this critical gap between clinical care and community services in the current health care delivery system. Designed by a panel of national experts, CMS developed this “10-item screening tool to identify patient needs in 5 different domains that can be addressed through community services (housing instability, food insecurity, transportation difficulties, utility assistance needs, and interpersonal safety), according to the National Academy of Medicine. “Clinicians and their staff can use this short tool across a spectrum of ages, backgrounds, and settings, and it is streamlined enough to be incorporated into busy clinical workflows. Just like with clinical assessment tools, results from this screening tool can be used to inform a patient’s treatment plan as well as make referrals to community services.”

Patient Centered Medical Home 2.0

In 2015, CMS introduced the most important broadly applicable change it has made to primary care to date: Payment for Chronic Care Management (CCM). Designed to accelerate the shift from office-based fee-for-services to value-based-outcomes, this program focused on supporting a “high-preforming primary care system that improves the value through increased emphasis on access, prevention, and care coordination by supporting care managers, patient communication, medication refills, and care provided electronically or by telephone,” said an article in the New England Journal of Medicine.

Over the last couple of years, the IT construct of the Patient Centered Medical Home has matured to a point where it can act as the central hub in a sustainable hub-and-spoke model of care coordination and delivery and, in some instances, has the ability to integrate new data into a more comprehensive care management profile of the patient at hand thereby allowing for more exactness and personalization in care plan design, intervention, and outcomes monitoring. Judith Baumhauer, MD, wrote in a different NEJM article that the end-game is to better understand and leverage the causal relationships between purely clinical (EHR) data and the other 80 percent of healthcare-related data such as mental and behavioral health, lifestyle choices, genetic, socio-economic determinants, device and patient-reported data (PROs).

CMS Looking Ahead: Medicare’s MACRA—QPP—2018

Last month, CMS released another proposed modification to the MACRA Quality Payment Program (QPP), which is slated to go into effect in 2018. Overall, the goal of these tweaks is to make the whole program less complicated and burdensome, primarily among the independent and small group practices. For example, the CMS “Year 2 proposed rule offers Virtual Group participation, which is another way clinicians can elect to participate in MIPS. Virtual Groups would be composed of solo practitioners and groups of 10 or fewer eligible clinicians, eligible to participate in MIPS, who come together virtually with at least 1 other such solo practitioner or group to participate in MIPS for a performance period of a year,” CMS explained. “Our goal is to make it as easy as possible for Virtual Groups to form no matter where the group members are located or what their medical specialties are. Generally, clinicians in a Virtual Group will report as a Virtual Group across all 4 performance categories and will need to meet the same measure and performance category requirements as non-virtual MIPS groups.”

For those eligible physicians who elect to participate in the modified program, two payment options are on the table: MIPS (Merit-based Incentive Payment System) and the Advanced Payment Model (APM).

Population Health Management 2020

In looking at the probable trajectory of some of the most thoughtfully designed population health programs supported by rigorously integrated data from multiple sources, the ability to measure, monitor, and manage discrete populations with one or more co-morbidities is beginning to move off the drawing board and into beta implementations is some PCMH 2.0/IDN provider systems that leverage the new CoCM model.

This marks a major milestone in providing a measures-based method for looking at the interactions between (in the example below) a patient’s mental health metrics (M3), blood pressure, BMI, and HgbA1c for managing a diabetic with symptoms of anxiety through interval testing to fine-tune treatment and outcomes.

Leading hospitals already paving the path ahead

Continuing to refine these care management models by working shoulder-to-shoulder with CMS also gives hospitals and outreach facilities the opportunity to provide input to CMS’s decision-making processes, to participate in pilots and studies, and to teach them how to support best-population-health practices and get their buy in along the way.

Contact Charles A. Coleman, Ph.D., Population Health Portfolio Solutions, Behavioral & Mental Health Program Integration Specialist at dr.charles.coleman@gmail.com or 919-271-4359.

Output: Don’t wait for your EHR vendors to bolt on pop health features when CMS already offers programs, tools, financial incentives and a roadmap for integrated, coordinated care.


Instruction: create a high quality summary of the given input

Input:
A Focus on Risk, Quality & Network Optimziation
 As provider organizations continue toward value-based outcomes and reimbursement, the risk is too high to rely on assumptions. Providers must take insight-driven actions around key objectives such as managing risk, improving quality and provider network management.

As provider organizations continue the journey toward value-based outcomes and reimbursement, such as MACRA, they face many critical decisions that will impact their business and patient outcomes. The risk is too high to rely on assumptions and interpretations. Instead, providers must be able to take insight-driven actions around key objectives such as managing risk and improving quality and provider network operations - areas that can significantly impact cost and quality outcomes.

Understanding best practices to using data and analytics to face these challenges head-on can be a critical tool to drive value-based contract performance, as well as deliver key insights needed to successfully manage at-risk contracts and populations.

As an industry, we understand a tipping point is on the horizon. The question to ask is: Will your healthcare organization be ready for it?

By the year 2020, value-based care is expected to be the dominant payment model, according to a survey conducted by Lazard. The upshot? Healthcare provider organizations will have to deal with new realities – by implementing new strategies.

Below is a quick rundown of what might come into play as the transformation takes hold.

4 New Realities in a Value-Based World

1. Healthcare networks will need to focus on efficient care delivery. Successful networks are likely to include tightly aligned providers. And care will be rationalized among lower cost alternate care sites such as ambulatory facilities, pharmacies and urgent care centers.

2. Healthcare delivery systems will increasingly compete on quality and cost. And, to succeed, advanced analytics will be needed to understand variation and improvement opportunities. Greater efficiency will be achieved by standardizing care across settings and addressing variation while managing a population’s health.

3. Care delivery is best developed and delivered by teams with a focus on care coordination.

4. Value based care is here to stay. With MACRA, MIPS and other value-based payment programs, providers have started their adoption journey and agree that reducing healthcare costs while ensuring quality is the only viable option.

6 Strategies for Success

1. Understand the data that your organization collects. Having a data strategy that centers on using impactful data vs. big data (all data). Trend identification is the key.

2. Prepare to rely more heavily on layering data (clinical, financial, behavioral, etc.). Providers should pair the right data together in order to understand where and how care is being delivered – and the nature of any care gaps that exist across care networks. As such, providers will be able to target and improve overall care delivery and quality.

3. Use predictive and prescriptive analytics to transform data into impactful intelligence. Organizations will need to not only understand what happened, but also predict what will happen in the future and what the best strategy is to reach desired outcomes.

4. Drive quality improvements. No matter where providers are on their journey toward value-based care delivery, all will need to focus heavily on delivering higher quality care using the most efficient and effective methods. Providers also must meet quality measures in order to maximize performance in value-based contracts.

5. Manage population and financial risk. Understand where risk exists across your network and identify high ROI opportunities to reduce that risk.

6. Optimize provider networks. Insights into issues such as referral patterns and leakage can help networks improve efficiency, reduce costs and manage provider quality performance.

The adoption of value-based payments is happening now and accelerating rapidly. It is our observation that payers, providers and consumers are transitioning to these models, but each at varying speeds. Regardless of their location on this path, healthcare organizations need to focus on using data and analytics as a tool that will guide them in meeting their quality-based goals.

In summary, the industry if finding that all healthcare organizations have will be focusing on using data and analytics as a tool that will guide them in meeting their quality-based goals.

Output: As provider organizations continue toward value-based outcomes and reimbursement, the risk is too high to rely on assumptions. Providers must take insight-driven actions around key objectives such as managing risk, improving quality and provider network management.


Instruction: create a high quality summary of the given input

Input:
How would the revised Senate health care bill affect individual market premiums in 2026 and over the long run?
 This analysis is part of USC-Brookings Schaeffer Initiative on Health Policy , which is a partnership between the Center for Health Policy at Brookings and the University of Southern California Schaeffer Center for Health Policy & Economics. The Initiative aims to inform the national health care debate with rigorous, evidence-based analysis leading to practical recommendations using the collaborative strengths of USC and Brookings.

On Thursday, the Congressional Budget Office (CBO) published an analysis of an updated version of the Better Care Reconciliation Act (BCRA), Senate Republicans’ health care legislation, including how the BCRA would affect individual market premiums. In its report, CBO estimated that the average premium for the “benchmark” individual market plan—the plan used to determine the amount of premium tax credits—would be 25 percent lower under the BCRA than under current law by 2026.

But as we noted after CBO analyzed an earlier draft of the BCRA, this estimate must be interpreted with caution. In particular, while this estimate may be of interest if one’s objective is to estimate how the federal government’s cost of providing premium tax credits would change under the BCRA, it does not answer the question of greatest interest to enrollees, which is how the BCRA will affect average premiums for a given generosity of coverage and a fixed population of individual market enrollees.

This is the case for two reasons. First, the BCRA substantially reduces the generosity of the benchmark plan. It directly reduces its actuarial value (AV)—the share of the total cost of covered services that are paid for by the insurance plan, rather than by the individual through cost sharing—from 70 percent to 58 percent, which CBO estimated would increase the deductible in such plans from around $5,000 under current law to around $13,000 under the BCRA in 2026. It would also make it easier for states to narrow the definition of “essential health benefits,” which would narrow the set of services covered by the benchmark plan in many states. Second, by allowing premiums to vary more widely by age and changing the structure of the premium tax credit, the BCRA would drive a significant shift in individual market enrollment toward younger individuals. Since younger individuals pay lower premiums, this shift would mechanically lower the average premium even if every individual’s premium remained the same.

Our earlier analysis showed how supplemental information included in the CBO report could be used to construct more informative premium comparisons. This blog post provides a brief update of that earlier analysis to reflect the new CBO estimates and also examines how premiums would change under the BCRA in the long run. Readers seeking greater methodological detail should refer to our earlier piece.

We find that the current population of individual market enrollees would pay more for the same level of coverage under the BCRA than under current law, before accounting for any premium tax credits for which individuals may be eligible.

We find that the current population of individual market enrollees would pay more for the same level of coverage under the BCRA than under current law, before accounting for any premium tax credits for which individuals may be eligible, as illustrated in Figure 1. Average premiums for a plan with a 58 percent actuarial value would be 1 percent higher under the BCRA than under current law in 2026, holding the individual market age distribution fixed at what it would be under current law. Similarly, average premiums for a plan with a 70 percent actuarial value would be 5 percent higher under the BCRA than under current law, holding the individual market age distribution fixed at what it would be under current law. The main reason these estimates differ from the 25 percent decline in the benchmark premium featured by CBO is that they hold actuarial value constant.

These estimates focus on 2026 because that is the year for which CBO provides detailed premium estimates, and they incorporate the effects of funding the BCRA would provide for state programs to reduce individual market premiums. Because the BCRA does not provide such funding for those programs for years after 2026, these estimates understate the increase in individual market premiums under the BCRA over the long run. The difference between the BCRA’s effects in 2026 and in the long run is larger than in prior drafts of the BCRA because the current draft would provide considerably more funding of this type in 2026 than prior drafts would have provided.

For this reason, to illustrate how the BCRA would affect the long run, we also provide estimates that adjust our 2026 estimates to remove the effects of the state funding. As shown in Figure 2, we find that the long-run increase in average premiums under the BCRA would be 7 or 8 percentage points larger than the increase in 2026. The details of our approach are provided in the methodological appendix.

It is important to note that there are two important respects in which these estimates understate the premium increases that individual market enrollees would face for a plan with fixed generosity.

First, as noted above, the BCRA would make it easier for states to narrow the definition of essential health benefits, causing individual market plans in many states to cover a smaller set of services. Ideally, therefore, we would make an upward adjustment to CBO’s premium estimates under the BCRA, but CBO does not provide the information required to make such an adjustment.

Second, our estimates reflect premiums before accounting for subsidies available to people purchasing individual market coverage. The subsidies available under the BCRA would, on average, be significantly smaller than the subsidies available under current law. As a result, average post-subsidy premiums would increase by considerably more than average pre-subsidy premiums.

The increase in average premiums under the BCRA should not be surprising in light of the fact that the BCRA would repeal the individual mandate and curtail subsidies for people purchasing individual market coverage. Both of these policies would likely reduce individual market enrollment, particularly among the healthy, necessitating higher premiums to cover the claims costs of the worsened risk pool. Our earlier analysis discusses why the BCRA would have these effects on premiums in somewhat greater detail.

Methodological Appendix: Method for Estimating Long-Run Premium Effects of BCRA

As described in the main text, to illustrate how the BCRA would affect individual market premiums over the long run, we present estimates of the BCRA’s effects on premiums in 2026 that remove the effects of the BCRA’s funding for state programs intended to reduce premiums. To do so, we first replace CBO’s estimates of premiums under the current version of the BCRA with its estimates under the prior version of the BCRA, as CBO indicates that the only reason its premium estimates differ between these two versions of the legislation is the additional state funding included in the current version.

We then adjust CBO’s estimate of the prior version of the BCRA to remove the effect of state funding included in that version of the BCRA. We estimate the required percentage increase in three steps:

Step 1 – Estimate aggregate individual market premiums in 2026: CBO’s estimate of this version of the BCRA, together with its March 2016 baseline projections, indicate that 18 million people would have been enrolled in the individual market in 2026 under the BCRA. We assume, likely conservatively for these purposes, that the average premium would equal the average premium for a plan with a 70 percent actuarial value, roughly the average actuarial value under current law. To derive that premium, we take the estimate of the average benchmark premium under the BCRA from our prior analysis ($6,058) and scale it up by CBO’s estimate of the ratio of premiums for a plan with a 70 percent actuarial value to premiums for a plan with a 58 percent actuarial value (1.28). Multiplying the estimated average premium by estimated enrollment yields projected aggregate individual market premium revenue of $140 billion in 2026 under the BCRA.

CBO’s estimate of this version of the BCRA, together with its March 2016 baseline projections, indicate that 18 million people would have been enrolled in the individual market in 2026 under the BCRA. We assume, likely conservatively for these purposes, that the average premium would equal the average premium for a plan with a 70 percent actuarial value, roughly the average actuarial value under current law. To derive that premium, we take the estimate of the average benchmark premium under the BCRA from our prior analysis ($6,058) and scale it up by CBO’s estimate of the ratio of premiums for a plan with a 70 percent actuarial value to premiums for a plan with a 58 percent actuarial value (1.28). Multiplying the estimated average premium by estimated enrollment yields projected aggregate individual market premium revenue of $140 billion in 2026 under the BCRA. Step 2 – Estimate total outlays for premium-reducing programs in 2026: The prior version of the BCRA would have appropriated $4 billion for its State Stability and Innovation Fund in 2026, of which CBO estimated that three-quarters would be devoted to reducing individual market premiums. Accounting for the requirement for states to provide matching funds equal to 35 percent of total program costs, this implies that the prior version of the BCRA would have generated total outlays on these programs of $4.6 billion for the 2026 plan year.

The prior version of the BCRA would have appropriated $4 billion for its State Stability and Innovation Fund in 2026, of which CBO estimated that three-quarters would be devoted to reducing individual market premiums. Accounting for the requirement for states to provide matching funds equal to 35 percent of total program costs, this implies that the prior version of the BCRA would have generated total outlays on these programs of $4.6 billion for the 2026 plan year. Step 3 – Estimate the percentage increase in individual market premiums: Assuming that $4.6 billion in funding estimated in step 2 would be passed through to consumers under the BCRA, this implies that premiums would have been 3.3 percent higher in the absence of this funding. Note that this estimate may modestly understate the effect that removing this funding would have on premiums since the mechanical increase in premiums caused by removing this funding would likely worsen the individual market risk pool, necessitating a modest additional premium increase.

Supplemental Tables and Figures

Our prior analysis included a figure illustrating how premiums would change by age under the BCRA, as well as a table reporting the estimates of average premiums that underlie our estimates of premium changes under the BCRA. Updated versions of those figures are provided here for completeness. For additional details on the calculations that underlie these figures, please refer to our prior analysis.

Output: On Thursday, the Congressional Budget Office (CBO) published an analysis of an updated version of the Better Care Reconciliation Act (BCRA), Senate Republicans’ health care legislation, including how the BCRA would affect individual market premiums.


Instruction: create a high quality summary of the given input

Input:
Senate Braces for Health Showdown With McCain on Hand but a Plan Unclear
 WASHINGTON — Senate Republican leaders, keeping alive their push to dismantle the Affordable Care Act, are barreling toward a showdown vote on Tuesday to begin debating a repeal of the health law. And Senator John McCain announced Monday night that he will be on hand to cast his vote, despite a diagnosis of brain cancer.

Before Mr. McCain, an Arizona Republican, announced that he was jetting in to cast what is expected to be a vote in favor of starting debate, President Trump spent Monday ratcheting up pressure on Republican senators to get onboard. Mr. Trump criticized their inaction and warned that they risked betraying seven years’ worth of promises to raze and revamp the health law if they did not.

“Remember ‘repeal and replace,’ ‘repeal and replace’ — they kept saying it over and over again,” Mr. Trump said at the White House, flanked by people who he said suffered as “victims” of the “horrible disaster known as Obamacare.”

“Every Republican running for office promised immediate relief from this disastrous law,” the president said. “But so far, Senate Republicans have not done their job in ending the Obamacare nightmare.”

Output: Senate Republican leaders, keeping alive their push to dismantle the Affordable Care Act, are barreling toward a showdown vote on Tuesday to begin debating a repeal of the health law.