Debt Collection – Unfair Practice Guidelines

When a debt turns bad, the creditor will engage numerous tactics to recover the debt. The tactics employed by the creditor varies from one company to another company. The most common ones are sending payment request letters by hand, normal mail, registered mails, couriers, emails or visit the debtor at his work place or home.

When faced with stubborn debtors, the creditors may engage third party collection agencies or engage a lawyer to file legal suit to recover the debts.

The debt collection tactics can turn rough and in certain cases have caused undue pressure to the debtors which can severe the health of the debtors and their closed ones.

Therefore, most countries have setup an Office of Fair Trading to monitor and control the money lending industry to ensure fair practices between both the creditors and the debtors.

All creditors must abide by a set of guidelines outlined by the local Office of Fair Trading. The main objective of the guideline is to protect the interest of the creditors without jeopardizing the health of the debtors. Included in this set of guidelines are the unfair debt collection practices which prohibit the use of improper actions by the creditor against the debtor during the debt collection process.

Many debtors and staff of the financial institutions or collection agencies are not aware of the existence of this unfair debt collection practices. Both parties are anxious to know what these guidelines are and has anyone been subjected to unfair practices.

Here I would like to share with you on some details of these guidelines which should be done or are prohibited during the collection process:

1. The creditors or collection agencies must identify themselves and let the debtor know about their identities, their roles and their purpose.

2. Unhelpful technical languages should be avoided by both parties.

3. If the debtor suggest a place or a time to meet later, such request should be regarded or considered.

4. The creditors or collection agencies should not do things that could jeopardize the debtors’ health such as ignoring requests of not contacting them at work which could jeopardize their jobs.

5. Putting the debtor under undue pressure.

6. Mislead debtor to believe that being in debt is a crime.

7. Suggest to debtors that they should their properties or assets to settle the debts or loans.

8. Recommend or suggest to debtors to apply for new loans to pay off their debts. Such act could amount to instigating the debtors to commit fraud.

9. Engage more than one debt collecting agency at any one time.

10. When the debt is in dispute with the debtors, the recovery proceedings or efforts should be temporary stop until they are settled.

11. It is the creditor’s obligation to prove the debts to the debtors.

12. The right to pursue legal suit against the debtors will lapse in the event that the creditors did not contact the debtors for more than 6 years with regard to the debts.

13. Creditors are fully responsible for the actions of the collection agencies that they appoint.

The above only provides a brief guideline of unfair practices and you should check with the local Office of Fair Trade for full details. All situations are investigated on a case-by-case basis and may differ from one another.

Creditors that found to be practicing unfair tactics can be penalized severely not limited to suspension of Consumer Credit License.

Phlebotomists: Important Health Care Team Members

Just saying the word phlebotomist is quite a mouthful. So, what exactly is a phlebotomist and what do they do? Phlebotomists are trained to collect blood samples. It is very common for a routine doctor’s visit or a trip to the Emergency Department to include a blood sample. This can be frightening for a lot of patients. It is the job of the phlebotomist to put their patient at ease and collect the proper sample for the blood tests ordered. The phlebotomy experience starts with the paperwork. The first most important step of a phlebotomy procedure is correctly identifying the patient. It may seem simple, but it is vital. Most errors occur not in the actual testing process, but in the paperwork. Incorrectly labeled samples are a huge problem. There is more work involved in tracking down the correct patient and results than just taking the time to properly identify and label the samples from the start. With the technology of today and a lot of patient privacy laws, more and more companies are using bar-coding for patient identification. A bar code is not readable by a human and cannot tell you if an error has occurred. This is why it is so important to take the simple step at the very beginning to properly identify each and every patient.

The next thing a trained phlebotomist will do is asses the patient. If there are any questions, doubts or fears, now is the time to address them. A phlebotomist knows their job and can easily answer most questions up front. Putting the patient at ease allows for a better experience all around. Patients who are nervous or tense can have a more painful than necessary experience or provide a sample that is sub-par. Extreme tension in a patient will not provide a good sample. Some patients can even tense themselves up to the point of stopping their own blood flow. Phlebotomists can ease the tension of patients by talking to them and walking them step by step through the process. Even chatting about a topic that interests a patient can put them at ease enough to allow for proper collection of a blood sample.

The phlebotomist will then check the orders and obtain the proper sample containers for the blood collection. There are a variety of blood tests available and each one has specific requirements. Your trained phlebotomist knows exactly what tube or container to collect from you based on the order from the doctor. The trained phlebotomist has all the supplies needed on hand. They will then collect the sample from the patient. Each patient and situation is unique and your phlebotomist will be able to collect your blood sample based on your needs.

After the sample is collected, the phlebotomist will then label each sample with the proper labels or actually hand write on the blood tubes. This is another vital step in proper patient identification. After all of this is complete, your blood samples are sent to the laboratory for testing. The result reports are sent to the ordering physician for interpretation. Patients can then discuss their results with their doctor.

The trained phlebotomist is an important member of a health care team. Helping doctors and nurses to understand what is going on with patients through blood testing can reveal a lot about a patients current condition. With a trained phlebotomist on your side, you are in good hands.

Population Level Health Management and Predictive Analytics

There has been much discussion of population health management coupled with predictive analytics recently in the health care field. Why? Most who are discussing these topics see it as a means of improving the health of patients while reducing the costs of doing so. Providing better care at lower costs is becoming necessary as payers are beginning to pay for quality outcomes as they move away from fee-for-service.

What is population health and how does predictive analytics fit in? Let me begin by defining population health and illustrate predictive analytics. In statistics, population refers to the complete set of objects of interest to the investigation. For instance, it could be the temperature range of adolescents with measles. It could be the individuals in a rural town who are prediabetic. These two are of interest in healthcare. Population also applies to any other field of research. It could be the income level of adults in a county or the ethnic groups living in a village.

Typically, population health management refers to managing the health outcomes of individuals by looking at the collective group. For instance, at the clinical practice level, population health management would refer to effectively caring for all the patients of the practice. Most practices segregate the patients by diagnosis when using population health management tools, such as patients with hypertension. Practices typically focus on patients with high costs for care so that more effective case management can be provided to them. Better case management of a population typically leads to more satisfied patients and lower costs.

Population health from the perspective of a county health department (as illustrated in last month’s newsletter) refers to all the residents of a county. Most services of a health department are not provided to individuals. Rather, the health of residents of a county is improved by managing the environment in which they live. For instance, health departments track the incidence of flu in a county in order to alert providers and hospitals so that they are ready to provide the levels of care needed.

You should be able to see that the population whose health is being managed depends upon who is providing the service. Physician practices’ population is all the patients of the practice. For county health departments it is all residents of a county. For the CDC it is all residents of the United States.

Once the population is identified, the data to be collected is identified. In a clinical setting, a quality or data team is most likely the body that determines what data should be collected. Once data is collected, trends in care can be identified. For instance, a practice may find that the majority of the patients who are identified as being hypertensive are managing their condition well. The quality team decides that more can be done to improve the outcomes for those who do not have their blood pressure under control. Using the factors from the data that it has collected the team applies a statistical approach called predictive analytics to see if can find any factors that may be in common among those whose blood pressure is not well managed. For instance, they may find that these patients lack the money to buy their medication consistently and that they have trouble getting transportation to the clinic that provides their care service. Once these factors are identified, a case manager at the clinic can work to overcome these barriers.

I will finish this overview of population health management and predictive analytics with two examples of providers using the approach correctly. In August 2013 the Medical Group Management Association presented a webinar featuring the speakers Benjamin Cox, the director of Finance and Planning for Integrated Primary Care Organization at Oregon Health Sciences University, an organization with 10 primary care clinics and 61 physicians, and Dr. Scott Fields, the Vice Chair of Family Medicine at the same organization. The title of the webinar was “Improving Your Practice with Meaningful Clinical Data”. Two of the objectives of the webinar were to define the skill set of their Quality Data Team, including who the members were, and describing the process of building a set of quality indicators.

The clinics were already collecting a large variety of data to report to various groups. For instance, they were reporting data for “meaningful use” and to commercial payers as well as employee groups. They decided to take this data and more and organize it into scorecards that would be useful to individual physicians and to practice managers at each clinic. Some of the data collected was patient satisfaction data, hospital readmission data, and obesity data. Scorecards for physicians were designed to meet the needs and requests of the individual physicians as well as for the practice as a whole. For instance, a physician could ask to have a scorecard developed for him that identified individual patients whose diabetes indicators showed that the patient was outside of the control limits for his diabetes. Knowing this, a physician could devote more time to improving the quality of life of the patient.

Scorecards for the clinic indicated how well the physicians at the site were managing patients with chronic conditions as a whole. With predictive analytics the staff of the clinic could identify which processes and actions helped improve the health of the patients. Providing more active case management may have been demonstrated to be effective for those with multiple chronic conditions.

Mr. Cox and Dr. Fields also stated that the quality data team members were skilled at understanding access, structuring data in meaningful ways, at presenting data to clinicians effectively and in extracting data from a variety of sources. The core objectives of the data team were to balance the competing agendas of providing quality care, making sure that operations were efficient and that patient satisfaction was high.

A second example of population health management focuses on preventing cardiovascular disease in a rural county in Maine-Franklin County. Over a 40-year period, starting in the late 1960’s, a volunteer nonprofit group and a clinical group worked together to improve the cardiovascular health of the residents of the county. As the project advanced, a hospital joined in the efforts.

At the beginning of the prevention efforts, the cardiovascular health of this poor county was below the state average. As volunteers and clinical groups became more active in improving the health of its residents, various cardiovascular measures improved significantly and actually were better in some respects than more affluent counties in the state that had better access to quality health services. The improvements were driven by volunteers who went out into the community to get those identified as being at risk of developing cardiovascular problems involved in smoking cessation classes, in increasing their physical activity and in improving their diets. This led to lowering blood pressure, lowering cholesterol rates and improving endurance.

The results and details of this 40-year effort in Franklin County has been published in the Journal of the American Medical Association in January 2015. The article is “Community-wide CVD prevention programs linked with improved health outcomes”.

As you can see, a population level approach to healthcare provides effective results. A clinic can improve the outcomes of its patients with chronic diseases while balancing costs through improved efficiency by focusing on data at the population level. A community can improve the lives of its residents by taking a population level approach to preventive care. Population level approaches to healthcare are varied and can be very successful if population level theory is correctly implemented. Better results can be obtained pairing it with predictive analytics.