26 hours ago Duplicate and fragmented medical records of patients can be a potential risk to the patient’s safety, continuity of care, billing, and leads to financial losses for the hospitals, as well as reduction in reimbursement rates and patient satisfaction scores for providers. In addition, the privacy of the patient is at stake, if the wrong patient ... >> Go To The Portal
One hospital found that duplicates accounted for 22 percent of all patient records, resulting in $96 in additional costs per duplicate. Duplicate and unmatched patient records in hospitals and health systems are likely costing those organizations more than they realize.
Incomplete information resulting from duplicate or partial records can contribute to clinical errors. For example, providers without access to single, complete patient records may be in the dark about the medications their patients are taking.
As the patient, maybe you thought your healthcare insurance company was at fault by the second duplicate bill. Erroring on the side of caution, you give them a call. After calling, your insurance will begin losing trust in your physician. The more often double billing occurs, the more scrutiny your doctor faces.
Duplication of patient records is not as uncommon as you may think. Whether a staff member mistakenly adds a new patient record that has already been created or duplication occurs through an HL7 interface with your medical software, from time to time you will have to deal with duplicate patient records.
Algorithms are automatic rules to identify problems. Algorithms can solve and prevent duplicate records by providing patient registration clerks and HIIM professionals with an accuracy rate between two records that may or may not be the same person. Algorithms are used in hospital information systems, and several different algorithms are used, including deterministic, probabilistic, and rules-based. Deterministic algorithms yield a 50 to 60 percent accuracy rate as they require exact or phonetic matches on certain data elements. 4 Deterministic means the results are predictable. For example, if a duplicate record pair has the exact name, address, and date of birth, it is more likely to be the same person. Probabilistic algorithms provide an accuracy rate up to 95 percent or higher as they determine precise record linkages by using complex mathematical principles to help analyze organization-specific data. Rules-based algorithms have a more advanced matching method that utilizes pre-set confidence levels for certain data elements and offers an accuracy rate between 70 to 80 percent. 5
Duplicate records are a popular issue amongst healthcare organizations since they wreak havoc on organizational performance and data quality. Health informatics plays a pivotal role in minimizing the rate of duplicate records. HIIM professionals must take the lead in recognizing and understanding this problem and ultimately providing viable solutions through not only technology but collaboration amongst other major stakeholders that share the common goal of tackling duplicate records as well.
Biometrics is another informatics approach gaining popularity. With biometrics, physiological characteristics of the human body can be used by healthcare facilities to seamlessly identify a patient by scanning their biometric identity. Biometrics include iris, palm vein, and fingerprint scanning. Iris scanning supports hospital infection control initiatives and is very effective in preventing duplicates as there is a low occurrence of false positives and extremely low (almost zero percent) false negative rate. 7 Palm vein biometric identification relies on matching technologies that can’t completely prevent duplicate medical records at the point of service. As such, fingerprints are the most well-known biometric modality but require physical contact with a hardware device, which is not conducive to infection control in a hospital setting.
When duplicate records are present in the EHR, data can become conflicted amongst providers, causing poor patient care and incorrect treatment. For example, a physician may locate two records for a patient and select only one of the records as a reference for how he/she would administer treatment for the patient. This physician could then prescribe a medication for the patient that produces an adverse reaction, causing the patient to be referred for emergency treatment.
Patients must understand the importance of providing consistent identification information across facilities to prevent duplicate entries, especially as mobile health becomes more prevalent and patients start taking a more active role in managing their information through personal health records.
The prevalence of duplicate records in most hospitals has been generally estimated between five percent to 10 percent of all stored records. 1,2 However, health systems that have multiple facilities or have merged with other systems are seeing duplicate rates around 20 percent. Duplicate rates also differ based on healthcare setting. For example, physicians’ office settings are known to have a duplicate rate at five percent, which is the “acceptable” rate for all healthcare settings. In contrast, according to a Gallagher Healthcare Practice report, one medical record consulting firm was employed to remove approximately 250,000 duplicate records, which represented 22 percent of the Master Patient Index (MPI) of one metro urban hospital system. 3
There are several solutions or tools that may be adopted to solve and prevent duplicate records from an informatics perspective, such as algorithms, smart cards, biometrics, advanced enterprise master patient index, and radio frequency identification technology.
Duplicate or incomplete patient records can cause hospitals to inadvertently bill payers or patients for more than or less than the correct amount, leading to direct revenue loss. Incorrect billing results in wasted time and resources, lengthening of days in accounts receivable (A/R), and an increase in bad debt write-offs.
There are numerous ways in which duplicate or incomplete patient records can undermine hospitals’ quality efforts and metrics that directly drive the payment formulas for Medicare, Medicaid, and other private payers under various value-based payment methods.
Duplicate and incomplete records can hurt revenues under both fee-for-service and value-based payment, and these issues are intensifying as providers with disparate systems merge. To improve performance on traditional and emerging revenue cycle metrics, revenue cycle leaders should consider technology that improves data quality.
As we have mentioned before, duplicates and overlays are the consequence of impatience. Medical workers do not take their time to actually look through the profiles or insert more information. Creating a new record seems to be much quicker but all the tests and denial claims are telling otherwise. According to a survey, 66% of leaders at provider and HIE organizations believe that data entry errors cause significant amounts of data duplication in healthcare facilities.
However, the best way to prevent duplicate records and improve patient data integrity is to use the power of modern technology. Combining biometrics with a record matching technology can help hospitals overcome such challenges as the absence of data standardization, lack of information, and human mistake.
The impact of duplicate medical records in healthcare is huge and affects everyone and everything: patients, personnel, facilities budget, and workflow. While numerous healthcare units think that it is easier to ignore the duplicates than fix them, they do not know how they actually slowly kill the system themselves.
It is simply annoying when you have to choose between two identical records in order to find out later that they are the same person. Personnel spends too much time choosing the right record. They may help another patient in the time that they spend on matching the records in the system or save precious time that not every patient has. In the end, they may just create the new one to save time. This vicious cycle will never stop if overlays and duplicates exist.
Record overlays emerge when the information of one patient is overlapping with the record of the other person with the same name. So the final record contains fragments of information about each person. The main cause of this problem is that there might be several people with the same names in one city and hospital personnel can confuse them.
According to statistics, between 2009 and 2020 there was a spike in healthcare data breaches. During these years, 3,705 breaches …
There are at least 4 causes of duplicate records and records overlays, so let’s take a closer look at each of them.
With the Merge Patients feature, you can select the primary patient record (the record you will keep) and merge data from the secondary patient record (the record you will delete), and then merge these two patient records into one , saving you the time-consuming process of re-entering and backdating data.
Duplication of patient records is not as uncommon as you may think. Whether a staff member mistakenly adds a new patient record that has already been created or duplication occurs through an HL7 interface with your medical software, from time to time you will have to deal with duplicate patient records.
NOTE: Some data, such as images from a 3rd party software, will need to be added manually to the merged patient record.
During registration, hospital staff might see a name and a partial address, but do not have a complete enough view of the patient who just walked in to determine whether it’s the same patient who had X-rays last week or just someone with a similar name (e.g., Kelley Smith vs. Kelly Smith, or Mary Jones on Oak Ave. vs. Mary Jones on Oak Rd.). The information is incomplete and siloed from department to department within a hospital or health system; each department may have entered the information differently, inaccurately, or incompletely. Without an EMPI or similar data management system in place, there is no central repository that contains all the disparate information and no way for one department to “talk” to another to verify information.
Unnecessary care. Without an EMPI to help eliminate duplicate records, patients can be sent for tests they’ve already had. Consider a patient who first visits the ED and then later sees an orthopedic specialist, both for knee pain. Without an EMPI in place, the specialist might not know that the patient had diagnostic scans in the ED just two weeks earlier and may order costly duplicate testing. Or, a patient may have multiple chronic conditions, and each specialist he visits orders similar sets of lab and radiology tests that are unnecessary and inconvenient for the patient. In the past, fee-for-service payments would cover these costs, but with fixed payments from episodic bundled payments and other value-based payment models that transfer risk to the provider, hospitals will increasingly bear these costs themselves.
Some hospitals and health systems are tackling this challenge by implementing enterprise master patient indexes (EMPIs)—databases that maintain information on registered patients across various facilities—to address duplicate and incomplete patient records. EMPI’s allow a single view of patients that ensure patient safety and provide a solid justification for the necessary investments in staff time, services, and information systems.
Duplicate records infect many aspects of hospital operations with costly inefficiencies. These types of problems are only escalating with trends such as mergers between providers, payers, and even retailers such as CVS and Amazon. More disparate systems mean the care coordination mandated by value-based payment models will be more complicated than ever. There is a cost to not planning for these known inefficiencies sooner rather than later.
Clinical errors. Incomplete information resulting from duplicate or partial records can contribute to clinical errors. For example, providers without access to single, complete patient records may be in the dark about the medications their patients are taking. This can lead to duplicate medications being prescribed as well as potential adverse events resulting from dangerous drug-to-drug interactions or drug allergies that may have been documented in one system but are missing from others.
Accurate patient data is more important than ever as value-based payment models take hold. An enterprise-wide database is one solution to avoid duplicate patient records that negatively affect productivity and costs.
The cost of clinical errors can reach far beyond immediate operational expenses as a result of low patient satisfaction, decreased employee and physician satisfaction (i.e., turnover cost), and heightened litigation risk.
If duplicate billing or “double billing” lands within their top five, there is a serious problem. If they continue to ignore that type of denial, government agencies may accuse them of fraud. These fraud cases result in massive fines (more on that later).
CRC Health was also required to pay $2.2 million to the state of West Virginia. First, CRC Health collected blood and urine samples from drug testing facilities. Once collected, they then sent the samples to San Diego Reference Laboratory. The double billing fraud occurred when CRC would bill Acadia.
The general definition of an exact duplicate claim is pretty straightforward. This type of claim contains the same information as a previously submitted claim. Although that’s the generally accepted definition, it may differ based on the insurance payer.
Every insurance company defines how they accept claims within their provider manual. Once your doctor submits the claim, your insurance checks if it meets their criteria.
Re-submitting duplicates without proper remediation negatively impacts your revenue and trust. If this form of negligence continues, the healthcare organization will face an investigation and a massive fraud penalty.
The discovery came from more than 13 audits conducted on MassHealth.
Pentec Health Pharmacy. In February 2019 Beckers Hospital Review reported that Pentec Pharmacy agreed to pay $17 million in fraud fines. The Department of Justice accused Pentec of over-billing Medicaid for wasted amounts of product while compounding its drug, Proplete.